revdep/problems.md
In igraph/rigraph: Network Analysis and Visualization
BCT
- Version: 1.2
- GitHub: NA
- Source code: https://github.com/cran/BCT
- Date/Publication: 2022-05-12 14:00:05 UTC
- Number of recursive dependencies: 13
Run revdepcheck::cloud_details(, "BCT") for more info
Newly broken
-
checking examples ... ERROR
```
Running examples in ‘BCT-Ex.R’ failed
The error most likely occurred in:
Name: draw_models
Title: Plot the results of the BCT and kBCT functions
Aliases: draw_models
** Examples
Use the pewee dataset as an example:
q <- BCT(pewee, 5) # maximum depth of 5
draw_models(q)
Error in getval(w, name) : argument "newval" is missing, with no default
Calls: draw_models ... .igraph.graph.union.or.intersection -> rename.attr.if.needed -> getval
Execution halted
```
In both
-
checking C++ specification ... NOTE
Specified C++11: please drop specification unless essential
-
checking Rd files ... NOTE
checkRd: (-1) gene_s.Rd:17: Lost braces; missing escapes or markup?
17 | giving the virus access to the cell and thus facilitating the COVID-19 disease. The gene sequence is mapped to the alphabet {0,1,2,3} via the obvious map A->0, C->1, G->2, T->3.
| ^
checkRd: (-1) sars_cov_2.Rd:16: Lost braces; missing escapes or markup?
16 | It consists of n = 29903 base pairs. The gene sequence is mapped to the alphabet {0,1,2,3} via the obvious map A->0, C->1, G->2, T->3.
| ^
netropy
- Version: 0.1.0
- GitHub: NA
- Source code: https://github.com/cran/netropy
- Date/Publication: 2022-02-02 08:20:02 UTC
- Number of recursive dependencies: 85
Run revdepcheck::cloud_details(, "netropy") for more info
Newly broken
-
checking examples ... ERROR
```
Running examples in ‘netropy-Ex.R’ failed
The error most likely occurred in:
Name: assoc_graph
Title: Association Graphs
Aliases: assoc_graph
** Examples
library(ggraph)
Loading required package: ggplot2
use internal data set
data(lawdata)
df.att <- lawdata[[4]]
three steps of data editing:
1. categorize variables 'years' and 'age' based on
approximately three equally size groups (values based on cdf)
2. make sure all outcomes start from the value 0 (optional)
3. remove variable 'senior' as it consists of only unique values (thus redundant)
df.att.ed <- data.frame(
+ status = df.att$status,
+ gender = df.att$gender,
+ office = df.att$office-1,
+ years = ifelse(df.att$years<=3,0,
+ ifelse(df.att$years<=13,1,2)),
+ age = ifelse(df.att$age<=35,0,
+ ifelse(df.att$age<=45,1,2)),
+ practice = df.att$practice,
+ lawschool= df.att$lawschool-1)
association graph based on cutoff 0.15
assoc_graph(df.att.ed, 0.15)
Error in igraph::distances(g) :
At vendor/cigraph/src/paths/dijkstra.c:128 : Weights must not contain NaN values. Invalid value
Calls: assoc_graph ... layout_with_stress -> .layout_with_stress_dim ->
Execution halted
```
-
checking re-building of vignette outputs ... ERROR
```
Error(s) in re-building vignettes:
...
--- re-building ‘joint_entropies.Rmd’ using rmarkdown
Quitting from lines 96-98 [assoc_g] (joint_entropies.Rmd)
Error: processing vignette 'joint_entropies.Rmd' failed with diagnostics:
At vendor/cigraph/src/paths/dijkstra.c:128 : Weights must not contain NaN values. Invalid value
--- failed re-building ‘joint_entropies.Rmd’
--- re-building ‘prediction_power.Rmd’ using rmarkdown
--- finished re-building ‘prediction_power.Rmd’
--- re-building ‘univariate_bivariate_trivariate.Rmd’ using rmarkdown
--- finished re-building ‘univariate_bivariate_trivariate.Rmd’
--- re-building ‘variable_domains.Rmd’ using rmarkdown
--- finished re-building ‘variable_domains.Rmd’
SUMMARY: processing the following file failed:
‘joint_entropies.Rmd’
Error: Vignette re-building failed.
Execution halted
```
In both
- checking Rd files ... NOTE
checkRd: (-1) lawdata.Rd:15: Lost braces in \itemize; meant \describe ?
checkRd: (-1) lawdata.Rd:16: Lost braces in \itemize; meant \describe ?
checkRd: (-1) lawdata.Rd:17: Lost braces in \itemize; meant \describe ?
checkRd: (-1) lawdata.Rd:18: Lost braces in \itemize; meant \describe ?
checkRd: (-1) lawdata.Rd:19: Lost braces in \itemize; meant \describe ?
checkRd: (-1) lawdata.Rd:20: Lost braces in \itemize; meant \describe ?
checkRd: (-1) lawdata.Rd:21: Lost braces in \itemize; meant \describe ?
checkRd: (-1) lawdata.Rd:22: Lost braces in \itemize; meant \describe ?
rmcfs
- Version: 1.3.5
- GitHub: NA
- Source code: https://github.com/cran/rmcfs
- Date/Publication: 2021-09-18 13:30:02 UTC
- Number of recursive dependencies: 65
Run revdepcheck::cloud_details(, "rmcfs") for more info
Newly broken
-
checking tests ... ERROR
```
Running ‘test-all.R’
Running the tests in ‘tests/test-all.R’ failed.
Complete output:
> library(testthat)
> library(rmcfs)
Loading required package: rJava
#######################
# rmcfs version 1.3.5 #
#######################
If used please cite the following paper:
M. Draminski, J. Koronacki (2018),
rmcfs: An R Package for Monte Carlo Feature Selection and Interdependency Discovery,
Journal of Statistical Software, vol 85(12), 1-28, doi:10.18637/jss.v085.i12.
test_check("rmcfs")
X1 X2 X3 X4 X5 X6 X7
1 0.3390729 0.6827881 0.9614099 0.02778712 0.48614910 0.43471764 0.02274122
2 0.8394404 0.6015412 0.1001408 0.52731078 0.06380247 0.51473265 0.93913671
3 0.3466835 0.2388687 0.7632227 0.88031907 0.78454623 0.66301097 0.29294872
4 0.3337749 0.2581659 0.9479664 0.37306337 0.41832164 0.14316659 0.16432657
5 0.4763512 0.7293096 0.8186347 0.04795913 0.98101808 0.34448739 0.39910256
6 0.8921983 0.4525708 0.3082923 0.13862825 0.28288396 0.40576358 0.45957541
7 0.8643395 0.1751268 0.6495795 0.32149212 0.84788215 0.08531101 0.43403085
8 0.3899895 0.7466983 0.9533555 0.15483161 0.08223923 0.93257193 0.51700983
9 0.7773207 0.1049876 0.9537327 0.13222817 0.88645875 0.83838407 0.84624575
10 0.9606180 0.8645449 0.3399792 0.22130593 0.47193073 0.87943330 0.05516429
X8 X9 X10
1 0.6547329 0.85750154 0.92974321
2 0.1328278 0.37088354 0.90093927
3 0.3418099 0.31420183 0.75088219
4 0.7313716 0.82853436 0.67656877
5 0.9072914 0.45184151 0.64801345
6 0.6961970 0.31587841 0.07324687
7 0.2415792 0.09780854 0.42355842
8 0.6441072 0.06490054 0.53082436
9 0.2807502 0.68945737 0.94270476
10 0.9576365 0.66805060 0.71222456
X27 X28 X29 X30 A1 A2 B1 B2 C1 C2 class
60 0.22730045 0.32639993 0.53409931 0.3797047 0 0 B B 0 0 B
61 0.16826293 0.29429765 0.81091625 0.7799683 0 0 0 0 C C C
62 0.14976251 0.82289798 0.65090651 0.9615772 0 0 0 0 0 0 C
63 0.64809716 0.41434742 0.77098111 0.4439984 0 0 0 0 C C C
64 0.48557255 0.01017122 0.03455832 0.2765765 0 0 0 0 0 0 C
65 0.69324432 0.05240839 0.55866028 0.6528749 0 0 0 0 C C C
66 0.28966946 0.73037038 0.12008737 0.7547240 0 0 0 0 0 0 C
67 0.97867166 0.07339495 0.68629963 0.5887064 0 0 0 0 C C C
68 0.28812633 0.07035663 0.91412566 0.3668981 0 0 0 0 C C C
69 0.52576452 0.63717197 0.56034976 0.5093910 0 0 0 0 0 0 C
70 0.07782227 0.41330587 0.57557604 0.1442291 0 0 0 0 C C C
class: 'data.frame' size: 70 x 37 X1 X2 X3 X4 X5 X6 X7
1 0.53509718 0.7440799 0.70465551 0.58037379 0.62995162 0.45447665 0.33625348
2 0.24689369 0.8384462 0.91905136 0.09076585 0.45587724 0.49380877 0.14465036
3 0.92234682 0.7993568 0.61013655 0.65609916 0.25400483 0.52620712 0.86807104
4 0.75348962 0.8936403 0.05585486 0.35750658 0.05748599 0.38301289 0.05432263
5 0.90350731 0.8311370 0.73615214 0.23402548 0.43530320 0.92889790 0.61388308
6 0.65154816 0.3707501 0.90396792 0.91643034 0.77113498 0.43341785 0.78563030
7 0.02077916 0.8719798 0.70181488 0.03055500 0.12915545 0.24476248 0.57944665
8 0.09587903 0.8007616 0.57769951 0.43907404 0.98391626 0.87625727 0.52716948
9 0.94958696 0.1772845 0.56583925 0.97369032 0.81417693 0.18265992 0.41051341
10 0.88817659 0.1632777 0.36717551 0.39914081 0.98168090 0.09032375 0.73052316
X8 X9 X10
1 0.74771232 0.9503260 0.6835156
2 0.71771862 0.9001683 0.5493564
3 0.09516487 0.6939464 0.6202255
4 0.47922473 0.8362239 0.2962362
5 0.12714205 0.4631887 0.2844997
6 0.26423701 0.6290997 0.6019828
7 0.22704179 0.4391008 0.1676721
8 0.18450321 0.9699211 0.9173871
9 0.01725596 0.2795489 0.9909104
10 0.93482027 0.9794688 0.7476684
X7 X8 X9 X10 A1 A2 B1 B2 C1 C2 class
60 0.82457067 0.748439821 0.5923369 0.53276926 0 0 B B 0 0 B
61 0.52929045 0.952518457 0.9101575 0.11142191 0 0 0 0 C C C
62 0.43723560 0.362614568 0.3939510 0.77589348 0 0 0 0 0 0 C
63 0.05642579 0.346293530 0.9642003 0.06293980 0 0 0 0 C C C
64 0.24777458 0.454230331 0.2550940 0.70528527 0 0 0 0 C C C
65 0.39808635 0.668704697 0.1407718 0.04925588 0 0 0 0 C C C
66 0.11121328 0.529091748 0.5252544 0.78131666 0 0 0 0 0 0 C
67 0.42162257 0.002821188 0.4540667 0.78413926 0 0 0 0 0 0 C
68 0.23918923 0.861873356 0.5217224 0.55257574 0 0 0 0 0 0 C
69 0.85053229 0.556885670 0.0856974 0.85510841 0 0 0 0 C C C
70 0.22702517 0.901919867 0.5975495 0.20987204 0 0 0 0 C C C
class: 'data.frame' size: 70 x 17Checking input data...
Exporting params...
Exporting input data...
Running MCFS-ID...
##################################################
##### dmLab 2.3.4 [2021.07.08] #####
##################################################
Created by Michal Draminski [michal.draminski@ipipan.waw.pl]
http://www.ipipan.eu/staff/m.draminski/
Polish Academy of Sciences - Institute of Computer Science
##################################################
Warning! Value of cutoffPermutations = 0 and cutoffMethod = 'permutations'. Using cutoffMethod = 'mean'.
MCFS-ID Experiment
****
Loading header: 'input.adh'...
Loading data: 'input.csv'...
70 objects and 17 attributes to load... Done
Nominal target detected - using J48 model
MCFS-ID param: ID-Graph is ON
MCFS-ID param: balance classes is AUTO
Classes = ["A", "B", "C"], Sizes = [40, 20, 10], classSizeRatio = 0.25, balanceValue = 1.0
Calculation of DecisionValuesTable...
Starting MCFS-ID Procedure: projectionSize(m) = 4, projections(s) = 200, splits(t) = 5
Start time: Thu Jul 18 09:10:57 UTC 2024
Running: 2 threads...
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1000 trees built within 1.0 s.
Prediction Summary on a Random Subsample (st):
Accuracy = 80.46%
WeightedAccuracy = 70.92%
Cutoff RI (based on linear regression angle) = 0.0451185
Cutoff RI (based on k-means clustering) = 0.3971068
Cutoff RI (based on mean cutoff value) = 0.0451185
Important attributes (based on mean cutoff value) = 6
MCFS-ID Processing is done. Time: 1.1 s.
Reading results...
Done.
Selected 6 nodes and 12 edges.
X1 X2 X3 X4 X5 X6
1 0.212941627 0.32259978 0.068299279 0.49303546 0.08453311 0.197323455
2 0.946402830 0.61844805 0.130542241 0.10290711 0.33361065 0.792430558
3 0.345473791 0.59392818 0.146773063 0.40479039 0.07040867 0.947512934
4 0.405422623 0.04812483 0.860316695 0.05246878 0.70507985 0.071240221
5 0.536541352 0.39381200 0.949276015 0.03656734 0.07419950 0.371038995
6 0.275828688 0.31159812 0.896443296 0.23109615 0.32501378 0.669765127
7 0.405671409 0.43268042 0.113651731 0.97980273 0.35894192 0.009998357
8 0.435509188 0.35949101 0.793385952 0.04629398 0.38833262 0.749893881
9 0.322054159 0.74693284 0.004317772 0.13195064 0.78544337 0.985846242
10 0.003435689 0.95245622 0.698139910 0.92844045 0.60434824 0.273909658
X7 X8 X9 X10
1 0.755911492 0.621500159 0.3899180 0.2825466
2 0.899729525 0.715610913 0.1276254 0.0551484
3 0.308047227 0.006459725 0.9787065 0.9982455
4 0.556854023 0.150810371 0.1611711 0.9606097
5 0.999194679 0.390337652 0.1158990 0.1403129
6 0.202094245 0.714161876 0.7860697 0.9566023
7 0.005399961 0.290325600 0.9052935 0.8806996
8 0.304086372 0.837331551 0.5998701 0.2326705
9 0.496753415 0.809496423 0.3001367 0.8077412
10 0.028377163 0.181867694 0.2260461 0.9273090
X7 X8 X9 X10 A1 A2 B1 B2 C1 C2 class
60 0.99415022 0.52001874 0.7344113 0.14549320 0 0 0 0 0 0 B
61 0.83573589 0.22437866 0.2762943 0.42770355 0 0 0 0 C C C
62 0.72685534 0.42713241 0.3515303 0.28976193 0 0 0 0 C C C
63 0.40742995 0.76241285 0.8684239 0.33320559 0 0 0 0 0 0 C
64 0.95852635 0.59382830 0.7705493 0.38924415 0 0 0 0 C C C
65 0.80196490 0.71950937 0.9914391 0.33556065 0 0 0 0 C C C
66 0.70485195 0.28409134 0.9776807 0.10902903 0 0 0 0 C C C
67 0.74343472 0.44167497 0.3433687 0.08789696 0 0 0 0 C C C
68 0.04116168 0.75138835 0.8932992 0.90448572 0 0 0 0 0 0 C
69 0.05585319 0.93409234 0.6443492 0.42207226 0 0 0 0 0 0 C
70 0.89679041 0.07141334 0.2940574 0.03089679 0 0 0 0 0 0 C
class: 'data.frame' size: 70 x 17Checking input data...
Exporting params...
Exporting input data...
Running MCFS-ID...
##################################################
##### dmLab 2.3.4 [2021.07.08] #####
##################################################
Created by Michal Draminski [michal.draminski@ipipan.waw.pl]
http://www.ipipan.eu/staff/m.draminski/
Polish Academy of Sciences - Institute of Computer Science
##################################################
MCFS-ID Cutoff Permutation Experiment #1/3
*********
Loading header: 'input.adh'...
Loading data: 'input.csv'...
70 objects and 17 attributes to load... Done
Pearson's correlation of shuffled decision: -0.0199
Nominal target detected - using J48 model
MCFS-ID param: ID-Graph is ON
MCFS-ID param: balance classes is AUTO
Classes = ["A", "B", "C"], Sizes = [40, 20, 10], classSizeRatio = 0.25, balanceValue = 1.0
Calculation of DecisionValuesTable...
Starting MCFS-ID Procedure: projectionSize(m) = 4, projections(s) = 200, splits(t) = 5
Start time: Thu Jul 18 09:10:58 UTC 2024
Running: 2 threads...
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1000 trees built within 0.481 s.
Prediction Summary on a Random Subsample (st):
Accuracy = 49.63%
WeightedAccuracy = 32.55%
Cutoff RI (based on linear regression angle) = 0.0210824
Cutoff RI (based on k-means clustering) = 0.0174957
Cutoff RI (based on mean cutoff value) = 0.0174957
Important attributes (based on mean cutoff value) = 6
MCFS-ID Cutoff Permutation Experiment #2/3
*********
Loading header: 'input.adh'...
Loading data: 'input.csv'...
70 objects and 17 attributes to load... Done
Pearson's correlation of shuffled decision: -0.1199
Nominal target detected - using J48 model
MCFS-ID param: ID-Graph is ON
MCFS-ID param: balance classes is AUTO
Classes = ["A", "B", "C"], Sizes = [40, 20, 10], classSizeRatio = 0.25, balanceValue = 1.0
Calculation of DecisionValuesTable...
Starting MCFS-ID Procedure: projectionSize(m) = 4, projections(s) = 200, splits(t) = 5
Start time: Thu Jul 18 09:10:59 UTC 2024
Running: 2 threads...
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1000 trees built within 0.352 s.
Prediction Summary on a Random Subsample (st):
Accuracy = 50.93%
WeightedAccuracy = 32.25%
Cutoff RI (based on linear regression angle) = 0.0085784
Cutoff RI (based on k-means clustering) = 0.0165298
Cutoff RI (based on mean cutoff value) = 0.0085784
Important attributes (based on mean cutoff value) = 6
MCFS-ID Cutoff Permutation Experiment #3/3
*********
Loading header: 'input.adh'...
Loading data: 'input.csv'...
70 objects and 17 attributes to load... Done
Pearson's correlation of shuffled decision: 0.2000
Nominal target detected - using J48 model
MCFS-ID param: ID-Graph is ON
MCFS-ID param: balance classes is AUTO
Classes = ["A", "B", "C"], Sizes = [40, 20, 10], classSizeRatio = 0.25, balanceValue = 1.0
Calculation of DecisionValuesTable...
Starting MCFS-ID Procedure: projectionSize(m) = 4, projections(s) = 200, splits(t) = 5
Start time: Thu Jul 18 09:10:59 UTC 2024
Running: 2 threads...
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1000 trees built within 0.425 s.
Prediction Summary on a Random Subsample (st):
Accuracy = 50.74%
WeightedAccuracy = 33.26%
Cutoff RI (based on linear regression angle) = 0.0138890
Cutoff RI (based on k-means clustering) = 0.0206007
Cutoff RI (based on mean cutoff value) = 0.0138890
Important attributes (based on mean cutoff value) = 6
MCFS-ID Experiment
****
Loading header: 'input.adh'...
Loading data: 'input.csv'...
70 objects and 17 attributes to load... Done
Nominal target detected - using J48 model
MCFS-ID param: ID-Graph is ON
MCFS-ID param: balance classes is AUTO
Classes = ["A", "B", "C"], Sizes = [40, 20, 10], classSizeRatio = 0.25, balanceValue = 1.0
Calculation of DecisionValuesTable...
Starting MCFS-ID Procedure: projectionSize(m) = 4, projections(s) = 200, splits(t) = 5
Start time: Thu Jul 18 09:11:00 UTC 2024
Running: 2 threads...
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1000 trees built within 0.317 s.
Prediction Summary on a Random Subsample (st):
Accuracy = 82.05%
WeightedAccuracy = 71.88%
Cutoff RI (based on linear regression angle) = 0.0249342
Cutoff RI (based on k-means clustering) = 0.4032865
Cutoff RI (based on mean cutoff value) = 0.0249342
Important attributes (based on mean cutoff value) = 6
Calculation of cutoff RI (based on permutations)
Max RI (raw data) = 0.75569206
Max RI (after permutations) = [0.026800117, 0.025955085, 0.03071283]
Anderson-Darling normality test p-value = 0.2157031
Confidence Interval: 0.0215170 ; 0.0341283
Cutoff RI (based on permutations) = 0.0341283
Important attributes (based on permutations) = 6
Calculation of cutoff ID
Anderson-Darling normality test p-value = 0.2467775
Confidence Interval: 5.9950792 ; 7.6353525
Cutoff ID (based on permutations) = 7.6353525
Final Important attributes (based on permutations) = 6
MCFS-ID Processing is done. Time: 1.6 s. ***
Reading results...
Done.
##### MCFS-ID result (s = auto, t = 5, m = auto) #####
Target feature: 'class'
Top 6 features:
position attribute RI
1 A2 0.7556921
2 A1 0.6967780
3 B2 0.4196845
4 B1 0.4032865
5 C1 0.2791012
6 C2 0.2322030
#################################
Cutoff values:
method minRI size minID
criticalAngle 0.02493421 7 NA
kmeans 0.40328652 4 NA
permutations 0.03412831 6 7.635353
mean 0.02493421 6 NA
#################################
Confusion matrix obtained on randomly selected (st) datasets:
Confusion Matrix:
A B C
A 13578 362 200
B 1364 5389 317
C 1403 884 1753
TPR (sensitivity/recall):
TPR
1 96 %
2 76.2 %
3 43.4 %
Accuracy: 82.1 %
wAccuracy: 71.9 %
#################################
MCFS-ID execution time: 2 secs
method minRI size minID
1 criticalAngle 0.02493421 7 NA
2 kmeans 0.40328652 4 NA
3 permutations 0.03412831 6 7.635353
4 mean 0.02493421 6 NA
[1] 6
projection distance commonPart mAvg beta1
1 30 0.625 1 0 0
2 40 0.750 1 0 0
3 50 0.500 1 0 0
4 60 0.750 1 0 0
5 70 0.750 1 0 0
6 80 0.375 1 0 0
7 90 0.125 1 0 0
8 100 0.375 1 0 0
9 110 0.125 1 0 0
10 120 0.000 1 0 0
11 130 0.250 1 0 0
12 140 0.250 1 0 0
13 150 0.125 1 0 0
14 160 0.250 1 0 0
15 170 0.000 1 0 0
16 180 0.000 1 0 0
17 190 0.125 1 0 0
18 200 0.250 1 0 0
position attribute projections classifiers nodes RI
12 1 A2 62 0.91290325 0.9129033 0.755692060
11 2 A1 48 0.84166664 0.8416666 0.696778000
14 3 B2 59 0.86101690 0.8610169 0.419684530
13 4 B1 57 0.88771930 0.8877193 0.403286520
15 5 C1 53 0.86037740 0.8603774 0.279101220
16 6 C2 40 0.75500000 0.7550000 0.232203050
2 7 X2 48 0.38333333 0.6000000 0.024934206
8 8 X8 46 0.35652173 0.5478261 0.024855249
3 9 X3 40 0.21000000 0.4150000 0.012116533
7 10 X7 47 0.21702127 0.2851064 0.010706335
10 11 X10 58 0.21034482 0.3793103 0.010647866
5 12 X5 57 0.21052632 0.2842105 0.009043122
9 13 X9 35 0.15428571 0.2571429 0.007862807
1 14 X1 57 0.18596491 0.2701754 0.007755438
6 15 X6 54 0.18888889 0.2666667 0.006814953
4 16 X4 47 0.09361702 0.1276596 0.003441651
position edge_a edge_b weight
1 1 A2 B1 25.3522010
2 2 B1 C2 14.5552830
3 3 B1 C1 13.6161410
4 4 A1 B2 11.5978510
5 5 A1 C2 10.8978290
6 6 A2 C1 9.9805440
7 7 A2 B2 9.0048310
8 8 B2 C1 8.5823240
9 9 A1 B1 7.3013086
10 10 A1 C1 7.1974344
11 11 B2 C2 6.4786950
12 12 C1 X8 5.7917280
13 13 A2 C2 5.4840190
14 14 B1 X2 3.3558383
15 15 B2 X2 3.1769930
16 16 B2 A2 2.8444445
17 17 X3 X5 2.2166020
18 18 C2 X8 2.0978600
19 19 A2 X2 1.9983566
20 20 B2 X10 1.7701454
21 21 X2 X7 1.7524443
22 22 B2 X1 1.6774335
23 23 X8 X1 1.6554034
24 24 B1 X7 1.6185875
25 25 X10 X7 1.6069815
26 26 X10 X6 1.5277632
27 27 X1 X10 1.5185722
28 28 X8 X10 1.4838791
29 29 X2 X5 1.4619606
30 30 X10 X5 1.4578505
31 31 X5 X3 1.4528583
32 32 B1 X1 1.4239199
33 33 X5 X7 1.3917996
34 34 X2 X9 1.3689568
35 35 C1 X2 1.3621721
36 36 X2 X3 1.3203249
37 37 X6 X3 1.3187371
38 38 X7 X10 1.2841835
39 39 X10 X9 1.2303705
40 40 X10 X3 1.2095273
41 41 X1 X7 1.1508476
42 42 X3 X6 1.0868700
43 43 B1 X3 1.0831480
44 44 B2 X5 1.0708183
45 45 X2 X4 1.0487859
46 46 C1 X3 1.0222663
47 47 X1 X9 0.9942785
48 48 C1 X7 0.9647899
49 49 X7 X1 0.9580313
50 50 X9 X5 0.9557424
Selected 6 nodes and 17 edges.
X1 X2 X3 X4 X5 X6 X7
1 0.98902563 0.23116906 0.78483548 0.821803022 0.71088721 0.58821497 0.7953894
2 0.61944825 0.35058203 0.15437349 0.901695312 0.94776216 0.16505500 0.2390703
3 0.23737288 0.45833595 0.01229192 0.982507341 0.98118909 0.04182482 0.1220222
4 0.36166464 0.07219098 0.08223544 0.265951080 0.05589952 0.46236715 0.3614041
5 0.74262232 0.24336907 0.51909300 0.787098370 0.24540161 0.16827440 0.9249142
6 0.19731734 0.51825450 0.06829250 0.190747030 0.27093381 0.28979740 0.9916906
7 0.02621423 0.50125444 0.81059233 0.003749102 0.90914956 0.33389940 0.5886450
8 0.07198397 0.67452979 0.07985544 0.556995859 0.37703354 0.40293417 0.7091866
9 0.58183258 0.19954013 0.15471898 0.409749276 0.27505538 0.58770520 0.9662458
10 0.60420706 0.52295278 0.06729615 0.922948800 0.17823044 0.25630289 0.8941283
X8 X9 X10
1 0.26332041 0.65081538 0.67427900
2 0.38240303 0.90531400 0.33779017
3 0.54840561 0.74012889 0.93212196
4 0.48009363 0.84171712 0.48498619
5 0.17678015 0.04423991 0.48679400
6 0.19423089 0.42327280 0.88244506
7 0.19061488 0.87393955 0.48127444
8 0.30924269 0.17536434 0.45792697
9 0.39395822 0.34876159 0.02776961
10 0.08136956 0.62184478 0.58021427
X7 X8 X9 X10 A1 A2 B1 B2 C1 C2 class
60 0.4571018 0.65831274 0.9851356 0.097424099 0 0 B B 0 0 B
61 0.3793650 0.82178514 0.6029888 0.008888587 0 0 0 0 C C C
62 0.3447140 0.77345445 0.9146233 0.335543330 0 0 0 0 0 0 C
63 0.2475398 0.03647023 0.2361436 0.951405642 0 0 0 0 C C C
64 0.9009008 0.04317233 0.3525739 0.344889228 0 0 0 0 0 0 C
65 0.6683702 0.05624382 0.9630998 0.969437987 0 0 0 0 C C C
66 0.4350037 0.57622864 0.2180650 0.523872372 0 0 0 0 C C C
67 0.5990060 0.48291037 0.8727669 0.013034772 0 0 0 0 C C C
68 0.8610901 0.12267192 0.8969003 0.751962328 0 0 0 0 0 0 C
69 0.9639909 0.39747332 0.3908880 0.088308024 0 0 0 0 0 0 C
70 0.7672535 0.26049222 0.6209638 0.941725152 0 0 0 0 C C C
class: 'data.frame' size: 70 x 17Checking input data...
Exporting params...
Exporting input data...
Running MCFS-ID...
##################################################
##### dmLab 2.3.4 [2021.07.08] #####
##################################################
Created by Michal Draminski [michal.draminski@ipipan.waw.pl]
http://www.ipipan.eu/staff/m.draminski/
Polish Academy of Sciences - Institute of Computer Science
##################################################
Warning! Value of cutoffPermutations = 0 and cutoffMethod = 'permutations'. Using cutoffMethod = 'mean'.
MCFS-ID Experiment
****
Loading header: 'input.adh'...
Loading data: 'input.csv'...
70 objects and 17 attributes to load... Done
Nominal target detected - using J48 model
MCFS-ID param: ID-Graph is ON
MCFS-ID param: balance classes is AUTO
Classes = ["A", "B", "C"], Sizes = [40, 20, 10], classSizeRatio = 0.25, balanceValue = 1.0
Calculation of DecisionValuesTable...
Starting MCFS-ID Procedure: projectionSize(m) = 4, projections(s) = 200, splits(t) = 5
Start time: Thu Jul 18 09:11:01 UTC 2024
Running: 2 threads...
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[=====================================================================>] 100% Time: 00:00
1000 trees built within 0.28 s.
Prediction Summary on a Random Subsample (st):
Accuracy = 78.90%
WeightedAccuracy = 68.28%
Cutoff RI (based on linear regression angle) = 0.0200903
Cutoff RI (based on k-means clustering) = 0.4499447
Cutoff RI (based on mean cutoff value) = 0.0200903
Important attributes (based on mean cutoff value) = 6
MCFS-ID Processing is done. Time: 0.287 s.
Reading results...
Done.
Selected 6 nodes and 12 edges.
X1 X2 X3 X4 X5 X6 X7
1 0.3390729 0.6827881 0.9614099 0.02778712 0.48614910 0.43471764 0.02274122
2 0.8394404 0.6015412 0.1001408 0.52731078 0.06380247 0.51473265 0.93913671
3 0.3466835 0.2388687 0.7632227 0.88031907 0.78454623 0.66301097 0.29294872
4 0.3337749 0.2581659 0.9479664 0.37306337 0.41832164 0.14316659 0.16432657
5 0.4763512 0.7293096 0.8186347 0.04795913 0.98101808 0.34448739 0.39910256
6 0.8921983 0.4525708 0.3082923 0.13862825 0.28288396 0.40576358 0.45957541
7 0.8643395 0.1751268 0.6495795 0.32149212 0.84788215 0.08531101 0.43403085
8 0.3899895 0.7466983 0.9533555 0.15483161 0.08223923 0.93257193 0.51700983
9 0.7773207 0.1049876 0.9537327 0.13222817 0.88645875 0.83838407 0.84624575
10 0.9606180 0.8645449 0.3399792 0.22130593 0.47193073 0.87943330 0.05516429
X8 X9 X10
1 0.6547329 0.85750154 0.92974321
2 0.1328278 0.37088354 0.90093927
3 0.3418099 0.31420183 0.75088219
4 0.7313716 0.82853436 0.67656877
5 0.9072914 0.45184151 0.64801345
6 0.6961970 0.31587841 0.07324687
7 0.2415792 0.09780854 0.42355842
8 0.6441072 0.06490054 0.53082436
9 0.2807502 0.68945737 0.94270476
10 0.9576365 0.66805060 0.71222456
X97 X98 X99 X100 A1 A2 B1 B2 C1 C2 class
60 0.22378404 0.51307358 0.3994570 0.02202729 0 0 B B 0 0 B
61 0.69587201 0.77525486 0.9689147 0.55346841 0 0 0 0 C C C
62 0.14077874 0.86028153 0.9072225 0.07312673 0 0 0 0 0 0 C
63 0.03859708 0.15871952 0.8472281 0.86110955 0 0 0 0 C C C
64 0.63851974 0.05270203 0.2219598 0.63500276 0 0 0 0 0 0 C
65 0.25135768 0.17265011 0.4159029 0.90253739 0 0 0 0 C C C
66 0.33987375 0.18344931 0.9690545 0.04770292 0 0 0 0 0 0 C
67 0.60909188 0.48718376 0.5835705 0.90106259 0 0 0 0 C C C
68 0.55731016 0.83232775 0.4722076 0.51572658 0 0 0 0 C C C
69 0.66711758 0.06494922 0.6722287 0.36741652 0 0 0 0 0 0 C
70 0.31903838 0.79192239 0.3533426 0.35998763 0 0 0 0 C C C
class: 'data.frame' size: 70 x 107Checking input data...
Exporting params...
Exporting input data...
Running MCFS-ID...
##################################################
##### dmLab 2.3.4 [2021.07.08] #####
##################################################
Created by Michal Draminski [michal.draminski@ipipan.waw.pl]
http://www.ipipan.eu/staff/m.draminski/
Polish Academy of Sciences - Institute of Computer Science
##################################################
Warning! Value of cutoffPermutations = 0 and cutoffMethod = 'permutations'. Using cutoffMethod = 'mean'.
MCFS-ID Experiment
****
Loading header: 'input.adh'...
Loading data: 'input.csv'...
70 objects and 107 attributes to load... Done
Numeric target detected - using M5 model
MCFS-ID param: ID-Graph is ON
MCFS-ID param: finalCV is ON
Starting MCFS-ID Procedure: projectionSize(m) = 10, projections(s) = 212, splits(t) = 5
Start time: Thu Jul 18 09:11:02 UTC 2024
Running: 1 threads...
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1060 trees built within 3.6 s.
Prediction Summary on a Random Subsample (st):
pearson: mean = 0.19714685679046492 median = 0.0 stdev = 0.3785735601222462
MAE: mean = 0.5822268205674695 median = 0.6394927501678467 stdev = 0.16084495727069972
RMSE: mean = 0.6723319524165355 median = 0.707329511642456 stdev = 0.1559868227285496
SMAPE: mean = 0.18326054566350333 median = 0.20413188636302948 stdev = 0.049676804451907905
Cutoff RI (based on linear regression angle) = 0.0061645
Cutoff RI (based on k-means clustering) = 0.4609433
Cutoff RI (based on mean cutoff value) = 0.0082593
Important attributes (based on mean cutoff value) = 9
Running CV experiment on input data limited to the top [2, 5, 7, 9, 11, 14, 18] attributes
Evaluating model performance using 10 fold CV. Model: m5
Evaluating model performance using 10 fold CV. Model: m5
Evaluating model performance using 10 fold CV. Model: m5
Evaluating model performance using 10 fold CV. Model: m5
Evaluating model performance using 10 fold CV. Model: m5
Evaluating model performance using 10 fold CV. Model: m5
Evaluating model performance using 10 fold CV. Model: m5
MCFS-ID Processing is done. Time: 3.9 s.
Reading results...
Done.
X1 X2 X3 X4 X5 X6 X7
1 0.3390729 0.6827881 0.9614099 0.02778712 0.48614910 0.43471764 0.02274122
2 0.8394404 0.6015412 0.1001408 0.52731078 0.06380247 0.51473265 0.93913671
3 0.3466835 0.2388687 0.7632227 0.88031907 0.78454623 0.66301097 0.29294872
4 0.3337749 0.2581659 0.9479664 0.37306337 0.41832164 0.14316659 0.16432657
5 0.4763512 0.7293096 0.8186347 0.04795913 0.98101808 0.34448739 0.39910256
6 0.8921983 0.4525708 0.3082923 0.13862825 0.28288396 0.40576358 0.45957541
7 0.8643395 0.1751268 0.6495795 0.32149212 0.84788215 0.08531101 0.43403085
8 0.3899895 0.7466983 0.9533555 0.15483161 0.08223923 0.93257193 0.51700983
9 0.7773207 0.1049876 0.9537327 0.13222817 0.88645875 0.83838407 0.84624575
10 0.9606180 0.8645449 0.3399792 0.22130593 0.47193073 0.87943330 0.05516429
X8 X9 X10
1 0.6547329 0.85750154 0.92974321
2 0.1328278 0.37088354 0.90093927
3 0.3418099 0.31420183 0.75088219
4 0.7313716 0.82853436 0.67656877
5 0.9072914 0.45184151 0.64801345
6 0.6961970 0.31587841 0.07324687
7 0.2415792 0.09780854 0.42355842
8 0.6441072 0.06490054 0.53082436
9 0.2807502 0.68945737 0.94270476
10 0.9576365 0.66805060 0.71222456
X7 X8 X9 X10 A1 A2 B1 B2 C1 C2 class
60 0.73271802 0.05737268 0.91933177 0.4484275 0 0 B B 0 0 B
61 0.87080555 0.85166764 0.07943969 0.7771450 0 0 0 0 C C C
62 0.57217026 0.21264535 0.50737425 0.1582198 0 0 0 0 0 0 C
63 0.01103607 0.53946203 0.82017162 0.8668086 0 0 0 0 C C C
64 0.90631526 0.13648759 0.59839542 0.2061456 0 0 0 0 0 0 C
65 0.77065363 0.32486514 0.42415353 0.1779497 0 0 0 0 C C C
66 0.38250462 0.62107629 0.55931027 0.1648911 0 0 0 0 0 0 C
67 0.09404589 0.25598225 0.78909447 0.5652690 0 0 0 0 C C C
68 0.04965358 0.63487580 0.16771526 0.7271810 0 0 0 0 C C C
69 0.82116232 0.48567211 0.97045173 0.8759190 0 0 0 0 0 0 C
70 0.82932430 0.93817692 0.47350310 0.7084244 0 0 0 0 C C C
class: 'data.frame' size: 70 x 17Checking input data...
Exporting params...
Exporting input data...
Running MCFS-ID...
##################################################
##### dmLab 2.3.4 [2021.07.08] #####
##################################################
Created by Michal Draminski [michal.draminski@ipipan.waw.pl]
http://www.ipipan.eu/staff/m.draminski/
Polish Academy of Sciences - Institute of Computer Science
##################################################
MCFS-ID Cutoff Permutation Experiment #1/3
*********
Loading header: 'input.adh'...
Loading data: 'input.csv'...
70 objects and 17 attributes to load... Done
Pearson's correlation of shuffled decision: -0.0399
Nominal target detected - using J48 model
MCFS-ID param: ID-Graph is ON
MCFS-ID param: balance classes is AUTO
Classes = ["A", "B", "C"], Sizes = [40, 20, 10], classSizeRatio = 0.25, balanceValue = 1.0
Calculation of DecisionValuesTable...
Starting MCFS-ID Procedure: projectionSize(m) = 4, projections(s) = 200, splits(t) = 5
Start time: Thu Jul 18 09:11:06 UTC 2024
Running: 1 threads...
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1000 trees built within 0.829 s.
Prediction Summary on a Random Subsample (st):
Accuracy = 50.94%
WeightedAccuracy = 35.38%
Cutoff RI (based on linear regression angle) = 0.0177116
Cutoff RI (based on k-means clustering) = 0.0365365
Cutoff RI (based on mean cutoff value) = 0.0211711
Important attributes (based on mean cutoff value) = 5
MCFS-ID Cutoff Permutation Experiment #2/3
*********
Loading header: 'input.adh'...
Loading data: 'input.csv'...
70 objects and 17 attributes to load... Done
Pearson's correlation of shuffled decision: 0.2599
Nominal target detected - using J48 model
MCFS-ID param: ID-Graph is ON
MCFS-ID param: balance classes is AUTO
Classes = ["A", "B", "C"], Sizes = [40, 20, 10], classSizeRatio = 0.25, balanceValue = 1.0
Calculation of DecisionValuesTable...
Starting MCFS-ID Procedure: projectionSize(m) = 4, projections(s) = 200, splits(t) = 5
Start time: Thu Jul 18 09:11:06 UTC 2024
Running: 1 threads...
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1000 trees built within 0.384 s.
Prediction Summary on a Random Subsample (st):
Accuracy = 51.68%
WeightedAccuracy = 35.42%
Cutoff RI (based on linear regression angle) = 0.0213335
Cutoff RI (based on k-means clustering) = 0.0320616
Cutoff RI (based on mean cutoff value) = 0.0213335
Important attributes (based on mean cutoff value) = 6
MCFS-ID Cutoff Permutation Experiment #3/3
*********
Loading header: 'input.adh'...
Loading data: 'input.csv'...
70 objects and 17 attributes to load... Done
Pearson's correlation of shuffled decision: 0.0
Nominal target detected - using J48 model
MCFS-ID param: ID-Graph is ON
MCFS-ID param: balance classes is AUTO
Classes = ["A", "B", "C"], Sizes = [40, 20, 10], classSizeRatio = 0.25, balanceValue = 1.0
Calculation of DecisionValuesTable...
Starting MCFS-ID Procedure: projectionSize(m) = 4, projections(s) = 200, splits(t) = 5
Start time: Thu Jul 18 09:11:07 UTC 2024
Running: 1 threads...
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1000 trees built within 0.344 s.
Prediction Summary on a Random Subsample (st):
Accuracy = 50.35%
WeightedAccuracy = 34.41%
Cutoff RI (based on linear regression angle) = 0.0273366
Cutoff RI (based on k-means clustering) = 0.0199981
Cutoff RI (based on mean cutoff value) = 0.0165373
Important attributes (based on mean cutoff value) = 6
MCFS-ID Experiment
****
Loading header: 'input.adh'...
Loading data: 'input.csv'...
70 objects and 17 attributes to load... Done
Nominal target detected - using J48 model
MCFS-ID param: ID-Graph is ON
MCFS-ID param: finalCV is ON
MCFS-ID param: balance classes is AUTO
Classes = ["A", "B", "C"], Sizes = [40, 20, 10], classSizeRatio = 0.25, balanceValue = 1.0
Calculation of DecisionValuesTable...
Starting MCFS-ID Procedure: projectionSize(m) = 4, projections(s) = 200, splits(t) = 5
Start time: Thu Jul 18 09:11:07 UTC 2024
Running: 1 threads...
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1000 trees built within 0.247 s.
Prediction Summary on a Random Subsample (st):
Accuracy = 79.01%
WeightedAccuracy = 69.00%
Cutoff RI (based on linear regression angle) = 0.0181059
Cutoff RI (based on k-means clustering) = 0.4120377
Cutoff RI (based on mean cutoff value) = 0.0181059
Important attributes (based on mean cutoff value) = 6
Calculation of cutoff RI (based on permutations)
Max RI (raw data) = 0.72376907
Max RI (after permutations) = [0.055015575, 0.05414359, 0.035604194]
Anderson-Darling normality test p-value = 0.0813281
Confidence Interval: 0.0210180 ; 0.0754908
Cutoff RI (based on permutations) = 0.0754908
Important attributes (based on permutations) = 6
Calculation of cutoff ID
Anderson-Darling normality test p-value = 0.6287300
Confidence Interval: 9.7308185 ; 12.8343097
Cutoff ID (based on permutations) = 12.8343097
*** Final Important attributes (based on permutations) = 6
Running CV experiment on input data limited to the top [2, 3, 5, 6, 8, 9, 12] attributes
Evaluating model performance using 10 fold CV. Model: j48, rf, nb, svm, knn, logistic, ripper
Evaluating model performance using 10 fold CV. Model: j48, rf, nb, svm, knn, logistic, ripper
Evaluating model performance using 10 fold CV. Model: j48, rf, nb, svm, knn, logistic, ripper
Evaluating model performance using 10 fold CV. Model: j48, rf, nb, svm, knn, logistic, ripper
Evaluating model performance using 10 fold CV. Model: j48, rf, nb, svm, knn, logistic, ripper
Evaluating model performance using 10 fold CV. Model: j48, rf, nb, svm, knn, logistic, ripper
Evaluating model performance using 10 fold CV. Model: j48, rf, nb, svm, knn, logistic, ripper
MCFS-ID Processing is done. Time: 6.6 s.
Reading results...
Done.
Checking input data...
Error: The names of the following attributes:
MCFS,contrast_1attr_abds,
MCFS'contrast_2attr_abds,
MCFS#contrast_3attr_abds,
MCFS()contrast_attr_abds,
MCFS[]contrast_attr_abds,
MCFS{}contrast_attr_abds,
MCFS{}'#contrast,attr.abds
contain forbidden characters. Please run fix.data() function before running mcfs().
Fixing names...
Fixing values...
Fixing types...
Checking input data...
Exporting params...
Exporting input data...
Running MCFS-ID...
##################################################
##### dmLab 2.3.4 [2021.07.08] #####
##################################################
Created by Michal Draminski [michal.draminski@ipipan.waw.pl]
http://www.ipipan.eu/staff/m.draminski/
Polish Academy of Sciences - Institute of Computer Science
##################################################
MCFS-ID Cutoff Permutation Experiment #1/3
*********
Loading header: 'input.adh'...
Loading data: 'input.csv'...
70 objects and 1007 attributes to load... Done
Pearson's correlation of shuffled decision: 0.0799
Nominal target detected - using J48 model
MCFS-ID param: ID-Graph is ON
MCFS-ID param: balance classes is AUTO
Classes = ["A", "B", "C"], Sizes = [40, 20, 10], classSizeRatio = 0.25, balanceValue = 1.0
Calculation of DecisionValuesTable...
Starting MCFS-ID Procedure: projectionSize(m) = 32, projections(s) = 314, splits(t) = 5
Start time: Thu Jul 18 09:11:13 UTC 2024
Running: 6 threads...
[ ] 0% Time: 00:00 ETA: --:--
[===> ] 6% Time: 00:00 ETA: --:--
[==========> ] 16% Time: 00:00 ETA: --:--
[===================> ] 29% Time: 00:01 ETA: --:--
[===========================> ] 41% Time: 00:01 ETA: --:--
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[=========================================> ] 61% Time: 00:01 ETA: --:--
[==================================================> ] 73% Time: 00:02 ETA: 00:02
[========================================================> ] 83% Time: 00:02 ETA: 00:02
[=================================================================> ] 96% Time: 00:02 ETA: 00:02
[=====================================================================>] 100% Time: 00:03
1570 trees built within 3.0 s.
Prediction Summary on a Random Subsample (st):
Accuracy = 42.61%
WeightedAccuracy = 33.71%
Cutoff RI (based on linear regression angle) = 0.0327596
Cutoff RI (based on k-means clustering) = 0.0167396
Cutoff RI (based on mean cutoff value) = 0.0214019
Important attributes (based on mean cutoff value) = 50
MCFS-ID Cutoff Permutation Experiment #2/3
*********
Loading header: 'input.adh'...
Loading data: 'input.csv'...
70 objects and 1007 attributes to load... Done
Pearson's correlation of shuffled decision: 0.2199
Nominal target detected - using J48 model
MCFS-ID param: ID-Graph is ON
MCFS-ID param: balance classes is AUTO
Classes = ["A", "B", "C"], Sizes = [40, 20, 10], classSizeRatio = 0.25, balanceValue = 1.0
Calculation of DecisionValuesTable...
Starting MCFS-ID Procedure: projectionSize(m) = 32, projections(s) = 314, splits(t) = 5
Start time: Thu Jul 18 09:11:16 UTC 2024
Running: 6 threads...
[ ] 0% Time: 00:00 ETA: --:--
[===> ] 6% Time: 00:00 ETA: --:--
[==========> ] 16% Time: 00:00 ETA: --:--
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[===========================> ] 41% Time: 00:01 ETA: --:--
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[========================================================> ] 83% Time: 00:01 ETA: --:--
[=================================================================> ] 96% Time: 00:02 ETA: 00:02
[=====================================================================>] 100% Time: 00:02
1570 trees built within 2.3 s.
Prediction Summary on a Random Subsample (st):
Accuracy = 42.88%
WeightedAccuracy = 33.73%
Cutoff RI (based on linear regression angle) = 0.0349667
Cutoff RI (based on k-means clustering) = 0.0140571
Cutoff RI (based on mean cutoff value) = 0.0185226
Important attributes (based on mean cutoff value) = 70
MCFS-ID Cutoff Permutation Experiment #3/3
*********
Loading header: 'input.adh'...
Loading data: 'input.csv'...
70 objects and 1007 attributes to load... Done
Pearson's correlation of shuffled decision: -0.1199
Nominal target detected - using J48 model
MCFS-ID param: ID-Graph is ON
MCFS-ID param: balance classes is AUTO
Classes = ["A", "B", "C"], Sizes = [40, 20, 10], classSizeRatio = 0.25, balanceValue = 1.0
Calculation of DecisionValuesTable...
Starting MCFS-ID Procedure: projectionSize(m) = 32, projections(s) = 314, splits(t) = 5
Start time: Thu Jul 18 09:11:18 UTC 2024
Running: 6 threads...
[ ] 0% Time: 00:00 ETA: --:--
[===> ] 6% Time: 00:00 ETA: --:--
[==========> ] 16% Time: 00:00 ETA: --:--
[===================> ] 29% Time: 00:00 ETA: --:--
[===========================> ] 41% Time: 00:00 ETA: --:--
[==================================> ] 51% Time: 00:01 ETA: --:--
[=========================================> ] 61% Time: 00:01 ETA: --:--
[==================================================> ] 73% Time: 00:01 ETA: --:--
[========================================================> ] 83% Time: 00:01 ETA: --:--
[=================================================================> ] 96% Time: 00:01 ETA: --:--
[=====================================================================>] 100% Time: 00:02
1570 trees built within 2.2 s.
Prediction Summary on a Random Subsample (st):
Accuracy = 41.99%
WeightedAccuracy = 33.01%
Cutoff RI (based on linear regression angle) = 0.0270371
Cutoff RI (based on k-means clustering) = 0.0130469
Cutoff RI (based on mean cutoff value) = 0.0169665
Important attributes (based on mean cutoff value) = 78
MCFS-ID Experiment
****
Loading header: 'input.adh'...
Loading data: 'input.csv'...
70 objects and 1007 attributes to load... Done
Nominal target detected - using J48 model
MCFS-ID param: ID-Graph is ON
MCFS-ID param: balance classes is AUTO
Classes = ["A", "B", "C"], Sizes = [40, 20, 10], classSizeRatio = 0.25, balanceValue = 1.0
Calculation of DecisionValuesTable...
Starting MCFS-ID Procedure: projectionSize(m) = 32, projections(s) = 314, splits(t) = 5
Start time: Thu Jul 18 09:11:21 UTC 2024
Running: 6 threads...
[ ] 0% Time: 00:00 ETA: --:--
[===> ] 6% Time: 00:00 ETA: --:--
[==========> ] 16% Time: 00:00 ETA: --:--
[===================> ] 29% Time: 00:00 ETA: --:--
[===========================> ] 41% Time: 00:00 ETA: --:--
[==================================> ] 51% Time: 00:00 ETA: --:--
[=========================================> ] 61% Time: 00:01 ETA: --:--
[==================================================> ] 73% Time: 00:01 ETA: --:--
[========================================================> ] 83% Time: 00:01 ETA: --:--
[=================================================================> ] 96% Time: 00:01 ETA: --:--
[=====================================================================>] 100% Time: 00:01
1570 trees built within 1.9 s.
Prediction Summary on a Random Subsample (st):
Accuracy = 47.16%
WeightedAccuracy = 38.15%
Cutoff RI (based on linear regression angle) = 0.0288587
Cutoff RI (based on k-means clustering) = 0.3288324
Cutoff RI (based on mean cutoff value) = 0.0389880
Important attributes (based on mean cutoff value) = 14
Calculation of cutoff RI (based on permutations)
Max RI (raw data) = 0.6696969
Max RI (after permutations) = [0.06497993, 0.057736687, 0.059140034]
Anderson-Darling normality test p-value = 0.2396656
Confidence Interval: 0.0510763 ; 0.0701614
Cutoff RI (based on permutations) = 0.0701614
Important attributes (based on permutations) = 6
Calculation of cutoff ID
Anderson-Darling normality test p-value = 0.1113444
Confidence Interval: 0.2776522 ; 2.4266361
Cutoff ID (based on permutations) = 2.4266361
Final Important attributes (based on permutations) = 6
MCFS-ID Processing is done. Time: 10.1 s. ***
Reading results...
Done.
X1 X2 X3 X4 X5 X6 X7
1 0.3390729 0.6827881 0.9614099 0.02778712 0.48614910 0.43471764 0.02274122
2 0.8394404 0.6015412 0.1001408 0.52731078 0.06380247 0.51473265 0.93913671
3 0.3466835 0.2388687 0.7632227 0.88031907 0.78454623 0.66301097 0.29294872
4 0.3337749 0.2581659 0.9479664 0.37306337 0.41832164 0.14316659 0.16432657
5 0.4763512 0.7293096 0.8186347 0.04795913 0.98101808 0.34448739 0.39910256
6 0.8921983 0.4525708 0.3082923 0.13862825 0.28288396 0.40576358 0.45957541
7 0.8643395 0.1751268 0.6495795 0.32149212 0.84788215 0.08531101 0.43403085
8 0.3899895 0.7466983 0.9533555 0.15483161 0.08223923 0.93257193 0.51700983
9 0.7773207 0.1049876 0.9537327 0.13222817 0.88645875 0.83838407 0.84624575
10 0.9606180 0.8645449 0.3399792 0.22130593 0.47193073 0.87943330 0.05516429
X8 X9 X10
1 0.6547329 0.85750154 0.92974321
2 0.1328278 0.37088354 0.90093927
3 0.3418099 0.31420183 0.75088219
4 0.7313716 0.82853436 0.67656877
5 0.9072914 0.45184151 0.64801345
6 0.6961970 0.31587841 0.07324687
7 0.2415792 0.09780854 0.42355842
8 0.6441072 0.06490054 0.53082436
9 0.2807502 0.68945737 0.94270476
10 0.9576365 0.66805060 0.71222456
X97 X98 X99 X100 A1 A2 B1 B2 C1 C2 class
60 0.22378404 0.51307358 0.3994570 0.02202729 0 0 B B 0 0 B
61 0.69587201 0.77525486 0.9689147 0.55346841 0 0 0 0 C C C
62 0.14077874 0.86028153 0.9072225 0.07312673 0 0 0 0 0 0 C
63 0.03859708 0.15871952 0.8472281 0.86110955 0 0 0 0 C C C
64 0.63851974 0.05270203 0.2219598 0.63500276 0 0 0 0 0 0 C
65 0.25135768 0.17265011 0.4159029 0.90253739 0 0 0 0 C C C
66 0.33987375 0.18344931 0.9690545 0.04770292 0 0 0 0 0 0 C
67 0.60909188 0.48718376 0.5835705 0.90106259 0 0 0 0 C C C
68 0.55731016 0.83232775 0.4722076 0.51572658 0 0 0 0 C C C
69 0.66711758 0.06494922 0.6722287 0.36741652 0 0 0 0 0 0 C
70 0.31903838 0.79192239 0.3533426 0.35998763 0 0 0 0 C C C
class: 'data.frame' size: 70 x 107Checking input data...
Exporting params...
Exporting input data...
Running MCFS-ID...
##################################################
##### dmLab 2.3.4 [2021.07.08] #####
##################################################
Created by Michal Draminski [michal.draminski@ipipan.waw.pl]
http://www.ipipan.eu/staff/m.draminski/
Polish Academy of Sciences - Institute of Computer Science
##################################################
Running Phase I - Initial MCFS-ID filtering
****
MCFS-ID Experiment
****
Loading header: 'input.adh'...
Loading data: 'input.csv'...
70 objects and 107 attributes to load... Done
Nominal target detected - using J48 model
MCFS-ID param: balance classes is AUTO
Classes = ["A", "B", "C"], Sizes = [40, 20, 10], classSizeRatio = 0.25, balanceValue = 1.0
Adding Contrast Attributes...
Data size: attributes: 118 objects: 70
Calculation of DecisionValuesTable...
Starting MCFS-ID Procedure: projectionSize(m) = 10, projections(s) = 530, splits(t) = 5
Start time: Thu Jul 18 09:11:23 UTC 2024
Running: 1 threads...
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[=====================================================================>] 100% Time: 00:01 ETA: --:--
[=====================================================================>] 100% Time: 00:01
2650 trees built within 1.1 s.
Prediction Summary on a Random Subsample (st):
Accuracy = 56.02%
WeightedAccuracy = 45.49%
Cutoff RI (based on linear regression angle) = 0.0224505
Cutoff RI (based on k-means clustering) = 0.4918377
Cutoff RI (based on contrast attributes) = 0.0253349
Cutoff RI (based on mean cutoff value) = 0.0253349
Important attributes (based on mean cutoff value) = 13
MCFS-ID Processing is done. Time: 1.1 s.
Running Phase II - Final MCFS-ID filtering
*******
MCFS-ID Cutoff Permutation Experiment #1/3
*******
Loading data: 'input.adx'...
70 objects and 15 attributes to load... Done
Pearson's correlation of shuffled decision: -0.1307
Nominal target detected - using J48 model
MCFS-ID param: ID-Graph is ON
MCFS-ID param: balance classes is AUTO
Classes = ["A", "B", "C"], Sizes = [40, 20, 10], classSizeRatio = 0.25, balanceValue = 1.0
Calculation of DecisionValuesTable...
Starting MCFS-ID Procedure: projectionSize(m) = 4, projections(s) = 175, splits(t) = 5
Start time: Thu Jul 18 09:11:24 UTC 2024
Running: 1 threads...
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[=======================================================> ] 80% Time: 00:00 ETA: --:--
[===============================================================> ] 91% Time: 00:00 ETA: --:--
[=====================================================================>] 100% Time: 00:00
875 trees built within 0.162 s.
Prediction Summary on a Random Subsample (st):
Accuracy = 49.85%
WeightedAccuracy = 32.44%
Cutoff RI (based on linear regression angle) = 0.0155160
Cutoff RI (based on k-means clustering) = 0.0155160
Cutoff RI (based on mean cutoff value) = 0.0116263
Important attributes (based on mean cutoff value) = 5
MCFS-ID Cutoff Permutation Experiment #2/3
*********
Loading data: 'input.adx'...
70 objects and 15 attributes to load... Done
Pearson's correlation of shuffled decision: -0.0230
Nominal target detected - using J48 model
MCFS-ID param: ID-Graph is ON
MCFS-ID param: balance classes is AUTO
Classes = ["A", "B", "C"], Sizes = [40, 20, 10], classSizeRatio = 0.25, balanceValue = 1.0
Calculation of DecisionValuesTable...
Starting MCFS-ID Procedure: projectionSize(m) = 4, projections(s) = 175, splits(t) = 5
Start time: Thu Jul 18 09:11:24 UTC 2024
Running: 1 threads...
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[=======================================================> ] 80% Time: 00:00 ETA: --:--
[===============================================================> ] 91% Time: 00:00 ETA: --:--
[=====================================================================>] 100% Time: 00:00
875 trees built within 0.239 s.
Prediction Summary on a Random Subsample (st):
Accuracy = 50.57%
WeightedAccuracy = 32.59%
Cutoff RI (based on linear regression angle) = 0.0152615
Cutoff RI (based on k-means clustering) = 0.0134873
Cutoff RI (based on mean cutoff value) = 0.0134873
Important attributes (based on mean cutoff value) = 7
MCFS-ID Cutoff Permutation Experiment #3/3
*********
Loading data: 'input.adx'...
70 objects and 15 attributes to load... Done
Pearson's correlation of shuffled decision: 0.1923
Nominal target detected - using J48 model
MCFS-ID param: ID-Graph is ON
MCFS-ID param: balance classes is AUTO
Classes = ["A", "B", "C"], Sizes = [40, 20, 10], classSizeRatio = 0.25, balanceValue = 1.0
Calculation of DecisionValuesTable...
Starting MCFS-ID Procedure: projectionSize(m) = 4, projections(s) = 175, splits(t) = 5
Start time: Thu Jul 18 09:11:24 UTC 2024
Running: 1 threads...
[ ] 0% Time: 00:00 ETA: --:--
[=======> ] 11% Time: 00:00 ETA: --:--
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[=======================> ] 34% Time: 00:00 ETA: --:--
[===============================> ] 46% Time: 00:00 ETA: --:--
[=======================================> ] 57% Time: 00:00 ETA: --:--
[===============================================> ] 69% Time: 00:00 ETA: --:--
[=======================================================> ] 80% Time: 00:00 ETA: --:--
[===============================================================> ] 91% Time: 00:00 ETA: --:--
[=====================================================================>] 100% Time: 00:00
875 trees built within 0.164 s.
Prediction Summary on a Random Subsample (st):
Accuracy = 50.89%
WeightedAccuracy = 32.12%
Cutoff RI (based on linear regression angle) = 0.0125553
Cutoff RI (based on k-means clustering) = 0.0086137
Cutoff RI (based on mean cutoff value) = 0.0098696
Important attributes (based on mean cutoff value) = 6
MCFS-ID Experiment
****
Loading data: 'input.adx'...
70 objects and 15 attributes to load... Done
Nominal target detected - using J48 model
MCFS-ID param: ID-Graph is ON
MCFS-ID param: finalCV is ON
MCFS-ID param: balance classes is AUTO
Classes = ["A", "B", "C"], Sizes = [40, 20, 10], classSizeRatio = 0.25, balanceValue = 1.0
Calculation of DecisionValuesTable...
Starting MCFS-ID Procedure: projectionSize(m) = 4, projections(s) = 175, splits(t) = 5
Start time: Thu Jul 18 09:11:25 UTC 2024
Running: 1 threads...
[ ] 0% Time: 00:00 ETA: --:--
[=======> ] 11% Time: 00:00 ETA: --:--
[===============> ] 23% Time: 00:00 ETA: --:--
[=======================> ] 34% Time: 00:00 ETA: --:--
[===============================> ] 46% Time: 00:00 ETA: --:--
[=======================================> ] 57% Time: 00:00 ETA: --:--
[===============================================> ] 69% Time: 00:00 ETA: --:--
[=======================================================> ] 80% Time: 00:00 ETA: --:--
[===============================================================> ] 91% Time: 00:00 ETA: --:--
[=====================================================================>] 100% Time: 00:00
875 trees built within 0.188 s.
Prediction Summary on a Random Subsample (st):
Accuracy = 83.77%
WeightedAccuracy = 76.18%
Cutoff RI (based on linear regression angle) = 0.2454500
Cutoff RI (based on k-means clustering) = 0.4110613
Cutoff RI (based on mean cutoff value) = 0.2454500
Important attributes (based on mean cutoff value) = 5
Calculation of cutoff RI (based on permutations)
Max RI (raw data) = 0.7629547
Max RI (after permutations) = [0.023562135, 0.022775238, 0.013791701]
Anderson-Darling normality test p-value = 0.1076272
Confidence Interval: 0.0065589 ; 0.0335271
Cutoff RI (based on permutations) = 0.0335271
Important attributes (based on permutations) = 7
Calculation of cutoff ID
Anderson-Darling normality test p-value = 0.5356377
Confidence Interval: 2.6019662 ; 11.1905529
Cutoff ID (based on permutations) = 11.1905529
*** Final Important attributes (based on permutations) = 7
Running CV experiment on input data limited to the top [2, 4, 5, 7, 9, 11, 14] attributes
Evaluating model performance using 10 fold CV. Model: j48, rf, nb, svm, knn, logistic, ripper
Evaluating model performance using 10 fold CV. Model: j48, rf, nb, svm, knn, logistic, ripper
Evaluating model performance using 10 fold CV. Model: j48, rf, nb, svm, knn, logistic, ripper
Evaluating model performance using 10 fold CV. Model: j48, rf, nb, svm, knn, logistic, ripper
Evaluating model performance using 10 fold CV. Model: j48, rf, nb, svm, knn, logistic, ripper
Evaluating model performance using 10 fold CV. Model: j48, rf, nb, svm, knn, logistic, ripper
Evaluating model performance using 10 fold CV. Model: j48, rf, nb, svm, knn, logistic, ripper
MCFS-ID Processing is done. Time: 5.0 s.
Reading results...
Done.
[ FAIL 3 | WARN 6 | SKIP 0 | PASS 35 ]
══ Failed tests ════════════════════════════════════════════════════════════════
── Error ('test-man.build.idgraph.R:21:3'): man build.idgraph ──────────────────
Error: from() was deprecated in igraph 2.0.4 and is now defunct.
ℹ Please use .from() instead.
Backtrace:
▆
1. └─rmcfs::build.idgraph(result, size = 6, size_ID = 12, orphan_nodes = TRUE) at test-man.build.idgraph.R:21:3
2. └─base::sapply(m, function(x) length(igraph::E(g)[from(x)]))
3. └─base::lapply(X = X, FUN = FUN, ...)
4. └─rmcfs (local) FUN(X[[i]], ...)
5. ├─igraph::E(g)[from(x)]
6. └─igraph:::[.igraph.es(igraph::E(g), from(x))
7. └─igraph:::lazy_eval(...)
8. └─base::lapply(x, lazy_eval, data = data)
9. └─igraph (local) FUN(X[[i]], ...)
10. └─base::eval(x$expr, data, x$env)
11. └─base::eval(x$expr, data, x$env)
12. └─igraph (local) from(x)
13. └─lifecycle::deprecate_stop("2.0.4", "from()", ".from()")
14. └─lifecycle:::deprecate_stop0(msg)
15. └─rlang::cnd_signal(...)
── Error ('test-man.mcfs.R:57:3'): man mcfs artificial ─────────────────────────
Error: from() was deprecated in igraph 2.0.4 and is now defunct.
ℹ Please use .from() instead.
Backtrace:
▆
1. └─rmcfs::build.idgraph(result) at test-man.mcfs.R:57:3
2. └─base::sapply(m, function(x) length(igraph::E(g)[from(x)]))
3. └─base::lapply(X = X, FUN = FUN, ...)
4. └─rmcfs (local) FUN(X[[i]], ...)
5. ├─igraph::E(g)[from(x)]
6. └─igraph:::[.igraph.es(igraph::E(g), from(x))
7. └─igraph:::lazy_eval(...)
8. └─base::lapply(x, lazy_eval, data = data)
9. └─igraph (local) FUN(X[[i]], ...)
10. └─base::eval(x$expr, data, x$env)
11. └─base::eval(x$expr, data, x$env)
12. └─igraph (local) from(x)
13. └─lifecycle::deprecate_stop("2.0.4", "from()", ".from()")
14. └─lifecycle:::deprecate_stop0(msg)
15. └─rlang::cnd_signal(...)
── Error ('test-man.plot.idgraph.R:19:3'): man build.idgraph ───────────────────
Error: from() was deprecated in igraph 2.0.4 and is now defunct.
ℹ Please use .from() instead.
Backtrace:
▆
1. └─rmcfs::build.idgraph(result, size = 6, size_ID = 12, orphan_nodes = TRUE) at test-man.plot.idgraph.R:19:3
2. └─base::sapply(m, function(x) length(igraph::E(g)[from(x)]))
3. └─base::lapply(X = X, FUN = FUN, ...)
4. └─rmcfs (local) FUN(X[[i]], ...)
5. ├─igraph::E(g)[from(x)]
6. └─igraph:::[.igraph.es(igraph::E(g), from(x))
7. └─igraph:::lazy_eval(...)
8. └─base::lapply(x, lazy_eval, data = data)
9. └─igraph (local) FUN(X[[i]], ...)
10. └─base::eval(x$expr, data, x$env)
11. └─base::eval(x$expr, data, x$env)
12. └─igraph (local) from(x)
13. └─lifecycle::deprecate_stop("2.0.4", "from()", ".from()")
14. └─lifecycle:::deprecate_stop0(msg)
15. └─rlang::cnd_signal(...)
[ FAIL 3 | WARN 6 | SKIP 0 | PASS 35 ]
Error: Test failures
Execution halted
```
In both
- checking Rd files ... NOTE
checkRd: (-1) plot.mcfs.Rd:24: Lost braces; missing escapes or markup?
24 | \item\code{ri} {plots top features set with their RIs as well as max RI obtained from permutation experiments. Red color denotes important features.}
| ^
simcausal
- Version: 0.5.6
- GitHub: https://github.com/osofr/simcausal
- Source code: https://github.com/cran/simcausal
- Date/Publication: 2022-10-28 11:52:27 UTC
- Number of recursive dependencies: 83
Run revdepcheck::cloud_details(, "simcausal") for more info
Newly broken
-
checking tests ... ERROR
```
Running ‘test-all.R’
Running the tests in ‘tests/test-all.R’ failed.
Complete output:
> ## unit tests will not be done if RUnit is not available
> # setwd("..")
> # getwd()
> # library(RUnit)
> if(require("RUnit", quietly=TRUE)) {
-
--- Setup ---
- pkg <- "simcausal" # <-- Tested package name
- if(Sys.getenv("RCMDCHECK") == "FALSE") {
-
Path to unit tests for standalone running under Makefile (not R CMD check)
-
PKG/tests/../inst/unitTests
-
path <- file.path(getwd(), "..", "inst", "unitTests")
- } else {
-
Path to unit tests for R CMD check
-
PKG.Rcheck/tests/../PKG/unitTests
-
path <- system.file(package=pkg, "RUnit")
-
REPLACED WITH:
- path <- file.path(getwd(), "RUnit")
- }
- cat("\nRunning unit tests\n")
- print(list(pkg=pkg, getwd=getwd(), pathToUnitTests=path))
- library(package=pkg, character.only=TRUE)
-
If desired, load the name space to allow testing of private functions
-
if (is.element(pkg, loadedNamespaces()))
-
attach(loadNamespace(pkg), name=paste("namespace", pkg, sep=":"), pos=3)
-
-
or simply call PKG:::myPrivateFunction() in tests
-
--- Testing ---
-
Define tests
- test.suite <- defineTestSuite(name=paste(pkg, "unit testing"),
-
dirs="./RUnit",
- dirs=path,
- testFileRegexp = "^RUnit_tests_+",
- testFuncRegexp = "^test.+",
- rngKind = "Marsaglia-Multicarry",
- rngNormalKind = "Kinderman-Ramage")
-
Run
- tests <- runTestSuite(test.suite)
-
Default report name
- pathReport <- file.path(path, "report")
-
Report to stdout and text files
- cat("------------------- UNIT TEST SUMMARY ---------------------\n\n")
- printTextProtocol(tests, showDetails=FALSE)
- printTextProtocol(tests, showDetails=FALSE,
- fileName=paste0(pathReport, "Summary.txt"))
- printTextProtocol(tests, showDetails=TRUE,
- fileName=paste0(pathReport, ".txt"))
-
Report to HTML file
- printHTMLProtocol(tests, fileName=paste0(pathReport, ".html"))
-
Return stop() to cause R CMD check stop in case of
-
- failures i.e. FALSE to unit tests or
-
- errors i.e. R errors
- tmp <- getErrors(tests)
- if(tmp$nFail > 0 | tmp$nErr > 0) {
- stop(paste("\n\nunit testing failed (#test failures: ", tmp$nFail,
- ", #R errors: ", tmp$nErr, ")\n\n", sep=""))
- }
- } else {
- warning("cannot run unit tests -- package RUnit is not available")
- }
Running unit tests
$pkg
[1] "simcausal"
$getwd
[1] "/tmp/workdir/simcausal/new/simcausal.Rcheck/tests"
$pathToUnitTests
[1] "/tmp/workdir/simcausal/new/simcausal.Rcheck/tests/RUnit"
Executing test function test.EFUeval ... ...automatically assigning order attribute to some nodes...
node C.time_0, order:1
node Y_0, order:2
node D_0, order:3
node C.time_1, order:4
node Y_1, order:5
node D_1, order:6
node C.time_2, order:7
node Y_2, order:8
node D_2, order:9
node C.time_3, order:10
node Y_3, order:11
node D_3, order:12
node C.time_4, order:13
node Y_4, order:14
node D_4, order:15
node C.time_5, order:16
node Y_5, order:17
node D_5, order:18
simulating observed dataset from the DAG object
...automatically assigning order attribute to some nodes...
node C.time_0, order:1
node Y_0, order:2
node D_0, order:3
node C.time_1, order:4
node Y_1, order:5
node D_1, order:6
node C.time_2, order:7
node Y_2, order:8
node D_2, order:9
node C.time_3, order:10
node Y_3, order:11
node D_3, order:12
node C.time_4, order:13
node Y_4, order:14
node D_4, order:15
node C.time_5, order:16
node Y_5, order:17
node D_5, order:18
using the following vertex attributes:
NAdarkbluenone70.50
using the following edge attributes:
black0.210.30.2
simulating observed dataset from the DAG object
simulating observed dataset from the DAG object
done successfully.
Executing test function test.Nsamp.n.test ... ...automatically assigning order attribute to some nodes...
node A, order:1
node N, order:2
simulating observed dataset from the DAG object
...automatically assigning order attribute to some nodes...
node A, order:1
node N, order:2
simulating observed dataset from the DAG object
...automatically assigning order attribute to some nodes...
node A, order:1
node N, order:2
done successfully.
Executing test function test.bugfixes ... ...automatically assigning order attribute to some nodes...
node const, order:1
node W1, order:2
node W2, order:3
node W3, order:4
node A, order:5
node Y, order:6
simulating observed dataset from the DAG object
...automatically assigning order attribute to some nodes...
node L2_0, order:1
node L1_0, order:2
node A1_0, order:3
node L2_1, order:5
node L2_2, order:6
node L2_3, order:7
node L2_4, order:8
node L2_5, order:9
node L2_6, order:10
node L2_7, order:11
node L2_8, order:12
node L2_9, order:13
node L2_10, order:14
node L2_11, order:15
node L2_12, order:16
node L2_13, order:17
node L2_14, order:18
node L2_15, order:19
node L2_16, order:20
...automatically assigning order attribute to some nodes...
node L2_0, order:1
node L1_0, order:2
node L2_1, order:3
node L1_1, order:4
node L2_2, order:5
node L1_2, order:6
node L2_3, order:7
node L1_3, order:8
node L2_4, order:9
node L1_4, order:10
node L2_5, order:11
node L1_5, order:12
node L2_6, order:13
node L1_6, order:14
node L2_7, order:15
node L1_7, order:16
node L2_8, order:17
node L1_8, order:18
node L2_9, order:19
node L1_9, order:20
node L2_10, order:21
node L1_10, order:22
node L2_11, order:23
node L1_11, order:24
node L2_12, order:25
node L1_12, order:26
node L2_13, order:27
node L1_13, order:28
node L2_14, order:29
node L1_14, order:30
node L2_15, order:31
node L1_15, order:32
node L2_16, order:33
node L1_16, order:34
Error in add.nodes(DAG = obj1, nodes = obj2) :
DAG object is locked: nodes in this DAG cannot be modified or added after set.DAG()
In addition: Warning messages:
1: In RNGkind(kind = testSuite$rngKind, normal.kind = testSuite$rngNormalKind) :
RNGkind: Marsaglia-Multicarry has poor statistical properties
2: In RNGkind(kind = testSuite$rngKind, normal.kind = testSuite$rngNormalKind) :
RNGkind: severe deviations from normality for Kinderman-Ramage + Marsaglia-Multicarry
3: In add.nodes(DAG = obj1, nodes = obj2) :
existing non-time-varying node L2 was overwritten with a time-varying node
4: In add.nodes(DAG = obj1, nodes = obj2) :
existing non-time-varying node L2 was overwritten with a time-varying node
5: In set.DAG(D) :
trying to lock an empty DAG, add nodes before calling set.DAG()
simulating observed dataset from the DAG object
simulating observed dataset from the DAG object
simulating observed dataset from the DAG object
simulating observed dataset from the DAG object
simulating observed dataset from the DAG object
simulating observed dataset from the DAG object
...automatically assigning order attribute to some nodes...
node L0, order:1
node L1, order:2
node L2, order:3
simulating observed dataset from the DAG object
simulating observed dataset from the DAG object
...automatically assigning order attribute to some nodes...
node L0, order:1
node L1, order:2
node L2, order:3
simulating observed dataset from the DAG object
...automatically assigning order attribute to some nodes...
node L0, order:1
node L1, order:2
simulating observed dataset from the DAG object
simulating observed dataset from the DAG object
...automatically assigning order attribute to some nodes...
node L0, order:1
node L1, order:2
simulating observed dataset from the DAG object
simulating observed dataset from the DAG object
done successfully.
Executing test function test.condrcat.factor ... ...automatically assigning order attribute to some nodes...
node W, order:1
node Cat3, order:2
...automatically assigning order attribute to some nodes...
node W, order:1
node Cat3, order:2
...automatically assigning order attribute to some nodes...
node W, order:1
node Cat3, order:2
Error in eval(x[[2]], envir = data.env, enclos = user.env) :
dims [product 3] do not match the length of object [10]
In addition: Warning messages:
1: In rcat.b1(n = n, probs = probs) :
some categorical probabilities add up to more than 1, normalizing to add to 1
2: In rcat.b1(n = n, probs = probs) :
some categorical probabilities add up to more than 1, normalizing to add to 1
3: In rcat.b1(n = n, probs = probs) :
some categorical probabilities add up to more than 1, normalizing to add to 1
4: In rcat.b1(n = n, probs = probs) :
some categorical probabilities add up to more than 1, normalizing to add to 1
5: In rcat.b1(n = n, probs = probs) :
some categorical probabilities add up to more than 1, normalizing to add to 1
6: In (W == 0) * catprob.W0 :
longer object length is not a multiple of shorter object length
Error in set.DAG(D) :
...attempt to simulate data from DAG failed...
...automatically assigning order attribute to some nodes...
node W, order:1
node Cat3, order:2
...automatically assigning order attribute to some nodes...
node W, order:1
node Cat3, order:2
simulating observed dataset from the DAG object
done successfully.
Executing test function test.distr ... All custom distributions defined in SimCausal:
[1] "rbern" "rcat.b0" "rcat.b1" "rcat.factor"
[5] "rcategor" "rcategor.int" "rconst" "rdistr.template"
Error in rdistr.template(n = 100, arg1 = rep(0.5, 100), arg2 = rep(0.3, :
inputs arguments should all have the same length
done successfully.
Executing test function test.experimental_parsingMSMs ... Error in simcausal:::parse.MSMform(msm.form = msm.form_3_error, t_vec = t_vec, :
unable to map some of S() expressions in MSM formula, check that all of the summary measure expressions have been previously defined
data not specified, simulating full data
no actions specified, sampling full data for ALL actions from the DAG
evaluating the target on 100 simulated samples per action
evaluating MSM summary measures and converting full data to long format for MSM target parameter
MSM: fitting glm to full data
simulating action-specific datasets for action(s): A1_th0 A1_th1
simulating action-specific datasets for action(s): A1_th0 A1_th1
evaluating MSM summary measures and converting full data to long format for MSM target parameter
MSM: fitting glm to full data
data not specified, simulating full data
no actions specified, sampling full data for ALL actions from the DAG
evaluating the target on 100 simulated samples per action
evaluating MSM summary measures and converting full data to long format for MSM target parameter
MSM: fitting glm to full data
simulating action-specific datasets for action(s): A1_th0 A1_th1
evaluating MSM summary measures and converting full data to long format for MSM target parameter
MSM: fitting glm to full data
simulating action-specific datasets for action(s): A1_th0 A1_th1
evaluating MSM summary measures and converting full data to long format for MSM target parameter
MSM: fitting glm to full data
for df_full in long format new summary measures cannot be calculated, using whatever summary measures already exist in df_full
for df_full in long format outcome is pooled over the same t vector as defined in the first MSM that generated the long format data, changing pooling t requires re-generating the full data
assuming the data is based on the following map of MSM terms to variable names
S_exprs_vec XMSMterms
1 A1[max(0, t - 2)] XMSMterm.4
MSM: fitting glm to full data
for df_full in long format new summary measures cannot be calculated, using whatever summary measures already exist in df_full
for df_full in long format outcome is pooled over the same t vector as defined in the first MSM that generated the long format data, changing pooling t requires re-generating the full data
assuming the data is based on the following map of MSM terms to variable names
S_exprs_vec XMSMterms
1 A1[max(0, t - 2)] XMSMterm.4
2 A1[max(0, t - 1)] XMSMterm.1
MSM: fitting glm to full data
Error in parse.MSMform(msm.form = form, t_vec = t_vec, old.DAG = DAG, :
unable to map some of S() expressions in MSM formula, check that all of the summary measure expressions have been previously defined
done successfully.
Executing test function test.faster_tolongdata ... simulating observed dataset from the DAG object
simulating observed dataset from the DAG object
simulating observed dataset from the DAG object
done successfully.
Executing test function test.latent ... ...automatically assigning order attribute to some nodes...
node I, order:1
node W1, order:2
node W2, order:3
node W3, order:4
node A, order:5
node U.Y, order:6
node Y, order:7
using the following vertex attributes:
NAdarkbluenone100.50
using the following edge attributes:
black0.210.60.5
Timing stopped at: 0.063 0 0.063
Error : from() was deprecated in igraph 2.0.4 and is now defunct.
ℹ Please use .from() instead.
done successfully.
Executing test function test.longparse ... ...automatically assigning order attribute to some nodes...
node group, order:1
simulating observed dataset from the DAG object
...automatically assigning order attribute to some nodes...
node group, order:1
simulating observed dataset from the DAG object
...automatically assigning order attribute to some nodes...
node A, order:1
node group, order:2
evaluating node group expression(s): rep(A, 3).
One of the distribution parameters evaluated to non-standard vector length (its neither 1 nor n), make sure the distribution function knows how to handle it.
simulating observed dataset from the DAG object
evaluating node group expression(s): rep(A, 3).
One of the distribution parameters evaluated to non-standard vector length (its neither 1 nor n), make sure the distribution function knows how to handle it.
...automatically assigning order attribute to some nodes...
node A, order:1
node group, order:2
simulating observed dataset from the DAG object
done successfully.
Executing test function test.node ... ...automatically assigning order attribute to some nodes...
node L0, order:1
node L1, order:2
simulating observed dataset from the DAG object
...automatically assigning order attribute to some nodes...
node L0, order:1
node L1, order:2
simulating observed dataset from the DAG object
...automatically assigning order attribute to some nodes...
node L0, order:1
simulating observed dataset from the DAG object
...automatically assigning order attribute to some nodes...
node L0, order:1
node L1, order:2
node L2, order:3
simulating observed dataset from the DAG object
...automatically assigning order attribute to some nodes...
node W1, order:1
node W2, order:2
node W3, order:3
simulating observed dataset from the DAG object
existing node W1 was modified
existing node W2 was modified
existing node W3 was modified
...automatically assigning order attribute to some nodes...
node W1, order:1
node W2, order:2
node W3, order:3
simulating observed dataset from the DAG object
...automatically assigning order attribute to some nodes...
node W1, order:1
node W2, order:2
node W3, order:3
simulating observed dataset from the DAG object
...automatically assigning order attribute to some nodes...
node L1_0, order:1
node L2_0, order:2
Error in add.nodes(DAG = obj1, nodes = obj2) :
cannot define nodes with missing t after nodes with t non-missing were already defined
In addition: Warning messages:
1: In (function (n, probs) :
some categorical probabilities add up to more than 1, normalizing to add to 1
2: In (function (n, probs) :
some categorical probabilities add up to more than 1, normalizing to add to 1
3: In rcat.b1(n = n, probs = probs) :
some categorical probabilities add up to more than 1, normalizing to add to 1
4: In rcat.b1(n = n, probs = probs) :
some categorical probabilities add up to more than 1, normalizing to add to 1
existing node L1_3 was modified
existing node L1_4 was modified
existing node L1_5 was modified
existing node L1_4 was modified
existing node L1_5 was modified
...automatically assigning order attribute to some nodes...
node L1_0, order:1
node L2_0, order:2
node L1_1, order:3
node L2_1, order:4
node L1_2, order:5
node L2_2, order:6
node L1_3, order:7
node L2_3, order:8
node L1_4, order:9
node L2_4, order:10
node L1_5, order:11
node L2_5, order:12
simulating observed dataset from the DAG object
existing node L1_2 was modified
existing node L1_3 was modified
existing node L1_4 was modified
existing node L1_5 was modified
...automatically assigning order attribute to some nodes...
node L3_0, order:1
node L3_1, order:2
node L1_2, order:3
node L2_2, order:4
node L3_2, order:5
node L1_3, order:6
node L2_3, order:7
node L3_3, order:8
node L1_4, order:9
node L2_4, order:10
node L3_4, order:11
node L1_5, order:12
node L2_5, order:13
node L3_5, order:14
simulating observed dataset from the DAG object
...automatically assigning order attribute to some nodes...
node L3_0, order:1
node L3_1, order:2
node L1_2, order:3
node L2_2, order:4
node L3_2, order:5
node L1_3, order:6
node L2_3, order:7
node L3_3, order:8
node L1_4, order:9
node L2_4, order:10
node L3_4, order:11
node L1_5, order:12
node L2_5, order:13
node L3_5, order:14
simulating observed dataset from the DAG object
...automatically assigning order attribute to some nodes...
node L2_0, order:1
node L1_0, order:2
node A1_0, order:3
node A2_0, order:4
node Y_0, order:5
node L2_1, order:6
node A1_1, order:7
node A2_1, order:8
node Y_1, order:9
node L2_2, order:10
node A1_2, order:11
node A2_2, order:12
node Y_2, order:13
node L2_3, order:14
node A1_3, order:15
node A2_3, order:16
node Y_3, order:17
node L2_4, order:18
node A1_4, order:19
node A2_4, order:20
node Y_4, order:21
node L2_5, order:22
node A1_5, order:23
node A2_5, order:24
node Y_5, order:25
node L2_6, order:26
node A1_6, order:27
node A2_6, order:28
node Y_6, order:29
node L2_7, order:30
node A1_7, order:31
node A2_7, order:32
node Y_7, order:33
node L2_8, order:34
node A1_8, order:35
node A2_8, order:36
node Y_8, order:37
node L2_9, order:38
node A1_9, order:39
node A2_9, order:40
node Y_9, order:41
node L2_10, order:42
node A1_10, order:43
node A2_10, order:44
node Y_10, order:45
node L2_11, order:46
node A1_11, order:47
node A2_11, order:48
node Y_11, order:49
node L2_12, order:50
node A1_12, order:51
node A2_12, order:52
node Y_12, order:53
node L2_13, order:54
node A1_13, order:55
node A2_13, order:56
node Y_13, order:57
node L2_14, order:58
node A1_14, order:59
node A2_14, order:60
node Y_14, order:61
node L2_15, order:62
node A1_15, order:63
node A2_15, order:64
node Y_15, order:65
node L2_16, order:66
node A1_16, order:67
node A2_16, order:68
node Y_16, order:69
simulating observed dataset from the DAG object
simulating observed dataset from the DAG object
done successfully.
Executing test function test.noexistdistr ... ...automatically assigning order attribute to some nodes...
node W1, order:1
rbinom2: note this distribution could not be located in package namespace, simulating from user-defined distribution found under the same name
...automatically assigning order attribute to some nodes...
node W1, order:1
rbinom2: note this distribution could not be located in package namespace, simulating from user-defined distribution found under the same name
Error in loadNamespace(name) : there is no package called 'rbinom3'
Error in network("net", netfun = "rbinom3", Kmax = 5, size = 4, prob = c(0.4, :
rbinom3: this network generator function could not be located
...automatically assigning order attribute to some nodes...
node W1, order:1
simulating observed dataset from the DAG object
done successfully.
Executing test function test.plotting ... ...automatically assigning order attribute to some nodes...
node L2_0, order:1
node L1_0, order:2
node A1_0, order:3
node A2_0, order:4
node Y_0, order:5
node L2_1, order:6
node A1_1, order:7
node A2_1, order:8
node Y_1, order:9
node L2_2, order:10
node A1_2, order:11
node A2_2, order:12
node Y_2, order:13
node L2_3, order:14
node A1_3, order:15
node A2_3, order:16
node Y_3, order:17
node L2_4, order:18
node A1_4, order:19
node A2_4, order:20
node Y_4, order:21
node L2_5, order:22
node A1_5, order:23
node A2_5, order:24
node Y_5, order:25
node L2_6, order:26
node A1_6, order:27
node A2_6, order:28
node Y_6, order:29
node L2_7, order:30
node A1_7, order:31
node A2_7, order:32
node Y_7, order:33
node L2_8, order:34
node A1_8, order:35
node A2_8, order:36
node Y_8, order:37
node L2_9, order:38
node A1_9, order:39
node A2_9, order:40
node Y_9, order:41
node L2_10, order:42
node A1_10, order:43
node A2_10, order:44
node Y_10, order:45
node L2_11, order:46
node A1_11, order:47
node A2_11, order:48
node Y_11, order:49
node L2_12, order:50
node A1_12, order:51
node A2_12, order:52
node Y_12, order:53
node L2_13, order:54
node A1_13, order:55
node A2_13, order:56
node Y_13, order:57
node L2_14, order:58
node A1_14, order:59
node A2_14, order:60
node Y_14, order:61
node L2_15, order:62
node A1_15, order:63
node A2_15, order:64
node Y_15, order:65
node L2_16, order:66
node A1_16, order:67
node A2_16, order:68
node Y_16, order:69
done successfully.
Executing test function test.set.DAG_DAG1 ... simulating observed dataset from the DAG object
some outcome nodes have EFU=TRUE, applying Last Time Point Carry Forward function: doLTCF()
data not specified, simulating full data
no actions specified, sampling full data for ALL actions from the DAG
evaluating the target on 500 simulated samples per action
some outcome nodes have EFU=TRUE, applying Last Time Point Carry Forward function: doLTCF()
some outcome nodes have EFU=TRUE, applying Last Time Point Carry Forward function: doLTCF()
data not specified, simulating full data
no actions specified, sampling full data for ALL actions from the DAG
evaluating the target on 500 simulated samples per action
some outcome nodes have EFU=TRUE, applying Last Time Point Carry Forward function: doLTCF()
some outcome nodes have EFU=TRUE, applying Last Time Point Carry Forward function: doLTCF()
data not specified, simulating full data
no actions specified, sampling full data for ALL actions from the DAG
evaluating the target on 500 simulated samples per action
some outcome nodes have EFU=TRUE, applying Last Time Point Carry Forward function: doLTCF()
some outcome nodes have EFU=TRUE, applying Last Time Point Carry Forward function: doLTCF()
some outcome nodes have EFU=TRUE, applying Last Time Point Carry Forward function: doLTCF()
some outcome nodes have EFU=TRUE, applying Last Time Point Carry Forward function: doLTCF()
data not specified, simulating full data
no actions specified, sampling full data for ALL actions from the DAG
evaluating the target on 500 simulated samples per action
some outcome nodes have EFU=TRUE, applying Last Time Point Carry Forward function: doLTCF()
some outcome nodes have EFU=TRUE, applying Last Time Point Carry Forward function: doLTCF()
some outcome nodes have EFU=TRUE, applying Last Time Point Carry Forward function: doLTCF()
some outcome nodes have EFU=TRUE, applying Last Time Point Carry Forward function: doLTCF()
done successfully.
Executing test function test.set.DAG_DAG2_errors ... Error in node("A_2", distr = "rbern", prob = 0.5, order = 1) :
node names with underscore character '' are not allowed
Error in L2[0] : undefined time-dependent variable(s): L2_0
Error : error while evaluating node L2_0 formula:
ifelse(L2[0] == 1, 0.5, 0.1).
Check syntax specification.
Error in set.DAG(c(L2_0, L1_0)) :
...attempt to simulate data from DAG failed...
...automatically assigning order attribute to some nodes...
node , order:1
node , order:2
node , order:3
node , order:4
Error in strsplit(cur.node$name, "") : non-character argument
Error in set.DAG(testDAG_2_err1a) :
...attempt to simulate data from DAG failed...
...automatically assigning order attribute to some nodes...
node , order:1
node , order:2
node , order:3
node , order:4
node , order:5
Error in set.DAG(testDAG_2_err1b) :
All DAG nodes must have unique name attributes
done successfully.
Executing test function test.set.DAG_DAG2b_newactions ... ...automatically assigning order attribute to some nodes...
node W1, order:1
node W2, order:2
node W3, order:3
node A, order:4
node Y, order:5
...automatically assigning order attribute to some nodes...
node W1, order:1
node W2, order:2
node W3, order:3
node A, order:4
node Y, order:5
...automatically assigning order attribute to some nodes...
node W1_0, order:1
node W2_0, order:2
node W3_0, order:3
node A_0, order:4
node Y_0, order:5
node W1_1, order:6
node W2_1, order:7
node W3_1, order:8
node A_1, order:9
node Y_1, order:10
...automatically assigning order attribute to some nodes...
node W1_0, order:1
node W2_0, order:2
node W3_0, order:3
node A_0, order:4
node Y_0, order:5
node W1_1, order:6
node W2_1, order:7
node W3_1, order:8
node A_1, order:9
node Y_1, order:10
simulating observed dataset from the DAG object
using the following vertex attributes:
NAdarkbluenone100.50
using the following edge attributes:
black0.210.60.5
simulating observed dataset from the DAG object
simulating observed dataset from the DAG object
simulating action-specific datasets for action(s): A1 A0
simulating action-specific datasets for action(s): A1 A0
simulating action-specific datasets for action(s): A1
some outcome nodes have EFU=TRUE, applying Last Time Point Carry Forward function: doLTCF()
Error : n is not a count (a single positive integer)
data not specified, simulating full data
no actions specified, sampling full data for ALL actions from the DAG
evaluating the target on 500 simulated samples per action
some outcome nodes have EFU=TRUE, applying Last Time Point Carry Forward function: doLTCF()
some outcome nodes have EFU=TRUE, applying Last Time Point Carry Forward function: doLTCF()
data not specified, simulating full data
no actions specified, sampling full data for ALL actions from the DAG
evaluating the target on 500 simulated samples per action
some outcome nodes have EFU=TRUE, applying Last Time Point Carry Forward function: doLTCF()
some outcome nodes have EFU=TRUE, applying Last Time Point Carry Forward function: doLTCF()
data not specified, simulating full data
no actions specified, sampling full data for ALL actions from the DAG
evaluating the target on 500 simulated samples per action
some outcome nodes have EFU=TRUE, applying Last Time Point Carry Forward function: doLTCF()
simulating observed dataset from the DAG object
simulating observed dataset from the DAG object
simulating action-specific datasets for action(s): A1 A0
simulating action-specific datasets for action(s): A1 A0
simulating action-specific datasets for action(s): A1 A0
Error in getactions(DAG, actions) :
Couldn't locate action: A4 , first define action by adding it to the DAG object with DAG+action
In addition: There were 13 warnings (use warnings() to see them)
simulating observed dataset from the DAG object
simulating observed dataset from the DAG object
simulating action-specific datasets for action(s): A1 A0
simulating action-specific datasets for action(s): A1 A0
simulating observed dataset from the DAG object
simulating observed dataset from the DAG object
user system elapsed
0.17 0.00 0.17
simulating observed dataset from the DAG object
user system elapsed
0.185 0.000 0.186
simulating observed dataset from the DAG object
user system elapsed
0.204 0.000 0.205
Error in X_dat_th0[[2]] : subscript out of bounds
In addition: There were 11 warnings (use warnings() to see them)
some outcome nodes have EFU=TRUE, applying Last Time Point Carry Forward function: doLTCF()
data not specified, simulating full data
no actions specified, sampling full data for ALL actions from the DAG
evaluating the target on 500 simulated samples per action
some outcome nodes have EFU=TRUE, applying Last Time Point Carry Forward function: doLTCF()
data not specified, simulating full data
evaluating the target on 500 simulated samples per action
some outcome nodes have EFU=TRUE, applying Last Time Point Carry Forward function: doLTCF()
data not specified, simulating full data
evaluating the target on 500 simulated samples per action
Error in eval.target(D, n = 500, actions = "A1_th1", rndseed = 123) :
some of the actions in param argument could not be found in the simulated full data
some outcome nodes have EFU=TRUE, applying Last Time Point Carry Forward function: doLTCF()
some outcome nodes have EFU=TRUE, applying Last Time Point Carry Forward function: doLTCF()
some outcome nodes have EFU=TRUE, applying Last Time Point Carry Forward function: doLTCF()
some outcome nodes have EFU=TRUE, applying Last Time Point Carry Forward function: doLTCF()
some outcome nodes have EFU=TRUE, applying Last Time Point Carry Forward function: doLTCF()
some outcome nodes have EFU=TRUE, applying Last Time Point Carry Forward function: doLTCF()
MSM: fitting glm to full data
data not specified, simulating full data
no actions specified, sampling full data for ALL actions from the DAG
evaluating the target on 500 simulated samples per action
MSM: fitting glm to full data
data not specified, simulating full data
evaluating the target on 500 simulated samples per action
MSM: fitting glm to full data
MSM: fitting glm to full data
data not specified, simulating full data
no actions specified, sampling full data for ALL actions from the DAG
evaluating the target on 500 simulated samples per action
MSM: fitting glm to full data
simulating observed dataset from the DAG object
done successfully.
Executing test function test.set.DAG_DAG3_wlong ... simulating observed dataset from the DAG object
simulating observed dataset from the DAG object
done successfully.
Executing test function test.set.DAG_DAG_informcens ... simulating observed dataset from the DAG object
simulating observed dataset from the DAG object
simulating observed dataset from the DAG object
data not specified, simulating full data
no actions specified, sampling full data for ALL actions from the DAG
evaluating the target on 100 simulated samples per action
some outcome nodes have EFU=TRUE, applying Last Time Point Carry Forward function: doLTCF()
data not specified, simulating full data
no actions specified, sampling full data for ALL actions from the DAG
evaluating the target on 100 simulated samples per action
some outcome nodes have EFU=TRUE, applying Last Time Point Carry Forward function: doLTCF()
some outcome nodes have EFU=TRUE, applying Last Time Point Carry Forward function: doLTCF()
data not specified, simulating full data
no actions specified, sampling full data for ALL actions from the DAG
evaluating the target on 500 simulated samples per action
MSM: fitting glm to full data
MSM: fitting glm to full data
data not specified, simulating full data
no actions specified, sampling full data for ALL actions from the DAG
evaluating the target on 500 simulated samples per action
MSM: fitting glm to full data
MSM: fitting glm to full data
data not specified, simulating full data
no actions specified, sampling full data for ALL actions from the DAG
evaluating the target on 500 simulated samples per action
evaluating MSM summary measures and converting full data to long format for MSM target parameter
MSM: fitting glm to full data
simulating observed dataset from the DAG object
simulating observed dataset from the DAG object
done successfully.
Executing test function test.set.DAG_general ... Error in set.DAG(testDAG_listobj1) : DAG must be a list
In addition: Warning message:
In predict.lm(object, newdata, se.fit, scale = 1, type = if (type == :
prediction from rank-deficient fit; attr(*, "non-estim") has doubtful cases
Error in set.DAG(testDAG_listobj2) :
each of DAG items must be a list specifying DAG node(s)
...automatically assigning order attribute to some nodes...
node nm, order:1
node nm, order:2
node nm, order:3
Error : !is.null(user.env) is not TRUE
Error in set.DAG(testDAG_names1) :
...attempt to simulate data from DAG failed...
...automatically assigning order attribute to some nodes...
node nm, order:2
node nm, order:3
Error : !is.null(user.env) is not TRUE
Error in set.DAG(testDAG_names2) :
...attempt to simulate data from DAG failed...
Error in set.DAG(testDAG_dist) :
All DAG nodes must have unique name attributes
done successfully.
Executing test function test.substitute ... ...automatically assigning order attribute to some nodes...
node I, order:1
node W1, order:2
node W2, order:3
node W3, order:4
node A, order:5
simulating observed dataset from the DAG object
done successfully.
Executing test function test.t.error ... Error in add.nodes(DAG = obj1, nodes = obj2) :
cannot define nodes with missing t after nodes with t non-missing were already defined
done successfully.
Executing test function test.tswitch_2MSMs ... [1] 0
[1] "abar"
[1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[1] 3
[1] "abar"
[1] 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[1] 6
[1] "abar"
[1] 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1
[1] 10
[1] "abar"
[1] 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1
[1] 13
[1] "abar"
[1] 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1
[1] 17
[1] "abar"
[1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[1] "tswitch_i"
[1] 0
[1] "meanExposureVec"
[1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[1] "tswitch_i"
[1] 3
[1] "meanExposureVec"
[1] 0.000 0.000 0.000 0.250 0.400 0.500 0.571 0.625 0.667 0.700 0.727 0.750
[13] 0.769 0.786 0.800 0.812 0.824
[1] "tswitch_i"
[1] 6
[1] "meanExposureVec"
[1] 0.000 0.000 0.000 0.000 0.000 0.000 0.143 0.250 0.333 0.400 0.455 0.500
[13] 0.538 0.571 0.600 0.625 0.647
[1] "tswitch_i"
[1] 10
[1] "meanExposureVec"
[1] 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.091 0.167
[13] 0.231 0.286 0.333 0.375 0.412
[1] "tswitch_i"
[1] 13
[1] "meanExposureVec"
[1] 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
[13] 0.000 0.071 0.133 0.188 0.235
[1] "tswitch_i"
[1] 17
[1] "meanExposureVec"
[1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
data not specified, simulating full data
no actions specified, sampling full data for ALL actions from the DAG
evaluating the target on 500 simulated samples per action
evaluating MSM summary measures and converting full data to long format for MSM target parameter
MSM: fitting glm to full data
data not specified, simulating full data
no actions specified, sampling full data for ALL actions from the DAG
evaluating the target on 500 simulated samples per action
evaluating MSM summary measures and converting full data to long format for MSM target parameter
MSM: fitting glm to full data
data not specified, simulating full data
no actions specified, sampling full data for ALL actions from the DAG
evaluating the target on 500 simulated samples per action
evaluating MSM summary measures and converting full data to long format for MSM target parameter
MSM: fitting glm to full data
data not specified, simulating full data
no actions specified, sampling full data for ALL actions from the DAG
evaluating the target on 500 simulated samples per action
evaluating MSM summary measures and converting full data to long format for MSM target parameter
MSM: fitting glm to full data
data not specified, simulating full data
no actions specified, sampling full data for ALL actions from the DAG
evaluating the target on 500 simulated samples per action
MSM: fitting glm to full data
[1] "MSMtermName used"
[1] "XMSMterm.1"
[1] "MSMterm_vals last"
[1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[1] "MSMterm_vals last"
[1] 0.0000000 0.0000000 0.0000000 0.2500000 0.4000000 0.5000000 0.5714286
[8] 0.6250000 0.6666667 0.7000000 0.7272727 0.7500000 0.7692308 0.7857143
[15] 0.8000000 0.8125000 0.8235294
[1] "MSMterm_vals last"
[1] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.1428571
[8] 0.2500000 0.3333333 0.4000000 0.4545455 0.5000000 0.5384615 0.5714286
[15] 0.6000000 0.6250000 0.6470588
[1] "MSMterm_vals last"
[1] 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
[7] 0.00000000 0.00000000 0.00000000 0.00000000 0.09090909 0.16666667
[13] 0.23076923 0.28571429 0.33333333 0.37500000 0.41176471
[1] "MSMterm_vals last"
[1] 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
[7] 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
[13] 0.00000000 0.07142857 0.13333333 0.18750000 0.23529412
[1] "MSMterm_vals last"
[1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[1] "MSMtermName used"
[1] "meanExposure"
[1] "MSMterm_vals last"
[1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[1] "MSMterm_vals last"
[1] 0.0000000 0.0000000 0.0000000 0.2500000 0.4000000 0.5000000 0.5714286
[8] 0.6250000 0.6666667 0.7000000 0.7272727 0.7500000 0.7692308 0.7857143
[15] 0.8000000 0.8125000 0.8235294
[1] "MSMterm_vals last"
[1] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.1428571
[8] 0.2500000 0.3333333 0.4000000 0.4545455 0.5000000 0.5384615 0.5714286
[15] 0.6000000 0.6250000 0.6470588
[1] "MSMterm_vals last"
[1] 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
[7] 0.00000000 0.00000000 0.00000000 0.00000000 0.09090909 0.16666667
[13] 0.23076923 0.28571429 0.33333333 0.37500000 0.41176471
[1] "MSMterm_vals last"
[1] 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
[7] 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
[13] 0.00000000 0.07142857 0.13333333 0.18750000 0.23529412
[1] "MSMterm_vals last"
[1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
data not specified, simulating full data
no actions specified, sampling full data for ALL actions from the DAG
evaluating the target on 500 simulated samples per action
evaluating MSM summary measures and converting full data to long format for MSM target parameter
MSM: fitting glm to full data
data not specified, simulating full data
no actions specified, sampling full data for ALL actions from the DAG
evaluating the target on 500 simulated samples per action
evaluating MSM summary measures and converting full data to long format for MSM target parameter
MSM: fitting glm to full data
data not specified, simulating full data
no actions specified, sampling full data for ALL actions from the DAG
evaluating the target on 500 simulated samples per action
MSM: fitting glm to full data
[1] "MSMtermName used"
[1] "XMSMterm.1"
[1] "MSMterm_vals last"
[1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[1] "MSMterm_vals last"
[1] 0.0000000 0.0000000 0.0000000 0.2500000 0.4000000 0.5000000 0.5714286
[8] 0.6250000 0.6666667 0.7000000 0.7272727 0.7500000 0.7692308 0.7857143
[15] 0.8000000 0.8125000 0.8235294
[1] "MSMterm_vals last"
[1] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.1428571
[8] 0.2500000 0.3333333 0.4000000 0.4545455 0.5000000 0.5384615 0.5714286
[15] 0.6000000 0.6250000 0.6470588
[1] "MSMterm_vals last"
[1] 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
[7] 0.00000000 0.00000000 0.00000000 0.00000000 0.09090909 0.16666667
[13] 0.23076923 0.28571429 0.33333333 0.37500000 0.41176471
[1] "MSMterm_vals last"
[1] 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
[7] 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
[13] 0.00000000 0.07142857 0.13333333 0.18750000 0.23529412
[1] "MSMterm_vals last"
[1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[1] "MSMtermName used"
[1] "XMSMterm.1"
[1] "MSMterm_vals last"
[1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[1] "MSMterm_vals last"
[1] 0.0000000 0.0000000 0.0000000 0.2500000 0.4000000 0.5000000 0.5714286
[8] 0.6250000 0.6666667 0.7000000 0.7272727 0.7500000 0.7692308 0.7857143
[15] 0.8000000 0.8125000 0.8235294
[1] "MSMterm_vals last"
[1] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.1428571
[8] 0.2500000 0.3333333 0.4000000 0.4545455 0.5000000 0.5384615 0.5714286
[15] 0.6000000 0.6250000 0.6470588
[1] "MSMterm_vals last"
[1] 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
[7] 0.00000000 0.00000000 0.00000000 0.00000000 0.09090909 0.16666667
[13] 0.23076923 0.28571429 0.33333333 0.37500000 0.41176471
[1] "MSMterm_vals last"
[1] 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
[7] 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
[13] 0.00000000 0.07142857 0.13333333 0.18750000 0.23529412
[1] "MSMterm_vals last"
[1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[1] "MSMtermName used"
[1] "meanExposure"
[1] "MSMterm_vals last"
[1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[1] "MSMterm_vals last"
[1] 0.0000000 0.0000000 0.0000000 0.2500000 0.4000000 0.5000000 0.5714286
[8] 0.6250000 0.6666667 0.7000000 0.7272727 0.7500000 0.7692308 0.7857143
[15] 0.8000000 0.8125000 0.8235294
[1] "MSMterm_vals last"
[1] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.1428571
[8] 0.2500000 0.3333333 0.4000000 0.4545455 0.5000000 0.5384615 0.5714286
[15] 0.6000000 0.6250000 0.6470588
[1] "MSMterm_vals last"
[1] 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
[7] 0.00000000 0.00000000 0.00000000 0.00000000 0.09090909 0.16666667
[13] 0.23076923 0.28571429 0.33333333 0.37500000 0.41176471
[1] "MSMterm_vals last"
[1] 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
[7] 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
[13] 0.00000000 0.07142857 0.13333333 0.18750000 0.23529412
[1] "MSMterm_vals last"
[1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[1] 0
[1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[1] 5
[1] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.1666667 0.2857143
[8] 0.3750000 0.4444444 0.5000000 0.5454545 0.5833333 0.6153846 0.6428571
[15] 0.6666667 0.6875000 0.7058824
[1] 10
[1] 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
[7] 0.00000000 0.00000000 0.00000000 0.00000000 0.09090909 0.16666667
[13] 0.23076923 0.28571429 0.33333333 0.37500000 0.41176471
[1] 17
[1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
data not specified, simulating full data
no actions specified, sampling full data for ALL actions from the DAG
evaluating the target on 200 simulated samples per action
MSM: fitting glm to full data
[1] "MSMtermName used"
[1] "meanExposure"
[1] "MSMterm_vals last"
[1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[1] "MSMterm_vals last"
[1] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.1666667 0.2857143
[8] 0.3750000 0.4444444 0.5000000 0.5454545 0.5833333 0.6153846 0.6428571
[15] 0.6666667 0.6875000 0.7058824
[1] "MSMterm_vals last"
[1] 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
[7] 0.00000000 0.00000000 0.00000000 0.00000000 0.09090909 0.16666667
[13] 0.23076923 0.28571429 0.33333333 0.37500000 0.41176471
[1] "MSMterm_vals last"
[1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
done successfully.
Executing test function test.long.wide.simobs ... done successfully.
Executing test function test.networkgen1 ... [1] "automatic Kmax: "
[1] 2
[1] "NetInd_k"
[,1] [,2]
[1,] NA NA
[2,] 4 NA
[3,] NA NA
[4,] NA NA
[5,] 6 8
[6,] 2 10
existing node Net.sample was modified
simulating network with ER model using m: 10
[1] "automatic Kmax: "
[1] 3
[1] "NetInd_k"
[,1] [,2] [,3]
[1,] 2 NA NA
[2,] NA NA NA
[3,] 9 NA NA
[4,] NA NA NA
[5,] 7 NA NA
[6,] NA NA NA
existing node Net.sample was modified
simulating network with ER model using m: 10
[1] "automatic Kmax: "
[1] 3
[1] "NetInd_k"
[,1] [,2] [,3]
[1,] NA NA NA
[2,] NA NA NA
[3,] 4 5 NA
[4,] NA NA NA
[5,] 2 6 7
[6,] NA NA NA
simulating network with ER model using m: 10
[1] "automatic Kmax: "
[1] 3
[1] "NetInd_k"
[,1] [,2] [,3]
[1,] NA NA NA
[2,] 8 NA NA
[3,] 6 NA NA
[4,] 5 6 8
[5,] 1 NA NA
[6,] NA NA NA
simulating network with ER model using m: 500000
[1] "automatic Kmax: "
[1] 551
[1] "NetInd_k"
[,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13] [,14]
[1,] 2 3 8 10 14 16 17 18 19 20 21 22 23 24
[2,] 1 3 5 7 8 9 10 11 12 16 17 18 21 22
[3,] 1 2 7 8 9 11 12 14 15 17 22 23 25 26
[4,] 5 6 8 9 10 12 13 15 17 18 20 22 24 25
[5,] 1 6 7 9 11 15 18 20 23 26 27 28 30 33
[6,] 9 12 13 15 16 18 21 23 24 26 27 28 29 30
[,15] [,16] [,17] [,18] [,19] [,20] [,21] [,22] [,23] [,24] [,25] [,26]
[1,] 28 33 35 38 40 43 44 45 47 49 51 53
[2,] 27 28 34 37 40 42 43 45 46 48 51 52
[3,] 27 32 35 36 37 41 44 45 47 49 50 53
[4,] 26 29 31 36 38 39 41 42 45 49 51 52
[5,] 35 36 43 44 45 46 48 50 58 60 61 62
[6,] 34 38 39 40 41 42 43 45 46 49 50 53
[,27] [,28] [,29] [,30] [,31] [,32] [,33] [,34] [,35] [,36] [,37] [,38]
[1,] 56 57 58 59 62 63 66 67 68 69 72 75
[2,] 55 56 57 63 64 66 67 69 70 72 75 76
[3,] 56 57 58 59 61 63 64 65 67 69 71 74
[4,] 54 56 59 60 61 65 67 70 73 75 76 77
[5,] 63 66 72 76 77 81 84 85 86 87 89 90
[6,] 54 57 60 61 64 65 66 67 68 69 73 75
[,39] [,40] [,41] [,42] [,43] [,44] [,45] [,46] [,47] [,48] [,49] [,50]
[1,] 77 78 79 81 83 84 88 89 91 92 93 95
[2,] 78 79 80 81 83 87 88 90 96 101 107 110
[3,] 75 76 77 82 83 84 86 87 89 94 95 96
[4,] 78 80 81 82 84 87 91 92 95 97 98 102
[5,] 91 94 95 99 100 101 103 105 106 108 109 111
[6,] 76 77 81 83 84 85 86 88 89 100 102 105
[,51] [,52] [,53] [,54] [,55] [,56] [,57] [,58] [,59] [,60] [,61] [,62]
[1,] 97 98 99 100 101 105 106 107 109 110 111 115
[2,] 113 114 115 116 117 123 124 125 127 135 136 138
[3,] 97 100 101 102 106 107 112 113 117 119 120 121
[4,] 103 105 107 108 110 112 114 120 121 124 125 126
[5,] 114 117 118 120 121 123 124 125 128 129 130 131
[6,] 106 108 110 111 114 115 116 118 119 121 122 123
[,63] [,64] [,65] [,66] [,67] [,68] [,69] [,70] [,71] [,72] [,73] [,74]
[1,] 118 119 120 122 127 128 131 136 137 138 141 145
[2,] 139 141 142 143 144 145 147 148 149 150 151 153
[3,] 122 124 127 128 130 132 133 134 137 138 140 141
[4,] 129 131 132 133 134 135 136 137 138 140 141 142
[5,] 133 134 135 137 139 144 145 147 148 149 151 152
[6,] 125 127 128 131 137 138 139 142 144 148 149 150
[,75] [,76] [,77] [,78] [,79] [,80] [,81] [,82] [,83] [,84] [,85] [,86]
[1,] 146 148 150 151 152 153 154 155 156 159 160 161
[2,] 154 156 157 159 160 161 164 165 168 171 173 175
[3,] 143 144 146 150 153 156 157 158 159 162 163 165
[4,] 153 154 156 157 158 162 163 164 165 166 168 170
[5,] 155 157 158 159 161 164 167 168 169 171 172 173
[6,] 151 153 156 157 158 159 163 165 166 167 168 169
[,87] [,88] [,89] [,90] [,91] [,92] [,93] [,94] [,95] [,96] [,97] [,98]
[1,] 163 164 165 167 168 170 171 177 180 182 184 186
[2,] 176 182 183 184 185 189 191 192 194 195 197 198
[3,] 167 169 171 173 175 179 180 183 184 185 186 187
[4,] 175 178 179 180 181 182 183 184 185 187 188 190
[5,] 179 182 183 187 189 190 192 194 199 203 204 205
[6,] 170 174 176 177 179 185 188 190 194 196 197 199
[,99] [,100] [,101] [,102] [,103] [,104] [,105] [,106] [,107] [,108]
[1,] 187 188 189 191 192 196 197 198 200 201
[2,] 200 202 203 205 206 209 211 214 217 218
[3,] 188 189 190 194 195 198 199 200 201 202
[4,] 192 193 194 195 199 200 201 203 204 208
[5,] 206 207 209 211 212 214 217 219 220 221
[6,] 200 201 204 208 209 216 217 218 222 223
[,109] [,110] [,111] [,112] [,113] [,114] [,115] [,116] [,117] [,118]
[1,] 202 203 204 206 209 212 213 215 216 219
[2,] 219 220 222 223 224 225 226 227 231 232
[3,] 203 204 205 209 211 212 213 215 216 220
[4,] 211 212 217 220 221 224 226 230 232 233
[5,] 222 223 224 225 227 228 234 235 236 238
[6,] 224 225 227 228 229 233 236 237 238 239
[,119] [,120] [,121] [,122] [,123] [,124] [,125] [,126] [,127] [,128]
[1,] 220 227 228 230 232 236 237 239 242 244
[2,] 233 234 235 236 238 239 240 241 242 243
[3,] 221 224 225 227 228 229 231 232 235 239
[4,] 235 241 242 246 247 248 249 251 253 254
[5,] 240 241 243 247 248 249 251 252 254 255
[6,] 241 242 246 247 252 253 254 260 261 262
[,129] [,130] [,131] [,132] [,133] [,134] [,135] [,136] [,137] [,138]
[1,] 246 247 250 251 252 253 254 255 258 260
[2,] 244 245 247 249 250 254 255 256 262 264
[3,] 241 243 245 246 247 251 252 255 256 258
[4,] 256 258 259 260 266 269 270 272 273 274
[5,] 259 261 263 264 266 267 268 269 270 271
[6,] 266 268 269 271 273 276 281 283 284 285
[,139] [,140] [,141] [,142] [,143] [,144] [,145] [,146] [,147] [,148]
[1,] 261 262 264 265 269 272 276 279 280 281
[2,] 265 266 267 269 271 272 275 279 281 282
[3,] 261 263 265 266 267 270 271 272 273 274
[4,] 277 281 283 286 287 289 293 294 295 296
[5,] 273 274 280 281 282 285 289 291 294 295
[6,] 287 288 289 290 291 293 294 295 298 300
[,149] [,150] [,151] [,152] [,153] [,154] [,155] [,156] [,157] [,158]
[1,] 283 286 289 290 292 293 298 299 301 302
[2,] 285 287 292 294 297 299 300 301 302 304
[3,] 275 277 279 280 281 282 283 284 286 287
[4,] 298 300 301 302 304 305 306 307 308 309
[5,] 296 297 298 300 301 303 305 306 307 308
[6,] 302 307 311 312 314 315 318 319 320 323
[,159] [,160] [,161] [,162] [,163] [,164] [,165] [,166] [,167] [,168]
[1,] 304 305 306 308 311 313 315 316 319 322
[2,] 308 316 318 320 326 327 329 330 331 334
[3,] 288 289 290 291 292 294 295 301 302 303
[4,] 313 318 320 321 322 325 327 328 329 330
[5,] 309 312 313 317 319 320 321 322 323 324
[6,] 326 328 332 335 336 338 342 343 344 346
[,169] [,170] [,171] [,172] [,173] [,174] [,175] [,176] [,177] [,178]
[1,] 324 325 327 329 331 332 335 336 343 345
[2,] 335 337 340 341 343 344 345 347 348 349
[3,] 306 307 308 310 312 316 317 320 324 327
[4,] 331 332 336 337 341 342 344 345 346 348
[5,] 325 326 327 329 331 333 334 335 337 338
[6,] 347 348 349 351 352 353 354 355 358 361
[,179] [,180] [,181] [,182] [,183] [,184] [,185] [,186] [,187] [,188]
[1,] 347 349 350 352 354 356 357 358 359 360
[2,] 350 351 352 353 354 355 356 359 360 362
[3,] 330 331 332 334 338 339 340 341 344 345
[4,] 351 352 356 360 361 365 366 367 368 372
[5,] 339 341 343 344 346 348 349 353 354 356
[6,] 362 364 368 371 372 374 375 376 378 381
[,189] [,190] [,191] [,192] [,193] [,194] [,195] [,196] [,197] [,198]
[1,] 362 365 367 369 372 374 375 380 383 385
[2,] 364 367 372 373 379 380 381 383 385 386
[3,] 346 348 353 354 355 357 359 360 362 364
[4,] 375 376 377 378 379 380 383 389 390 391
[5,] 359 360 361 362 365 371 373 374 375 376
[6,] 382 384 385 387 388 390 391 392 393 394
[,199] [,200] [,201] [,202] [,203] [,204] [,205] [,206] [,207] [,208]
[1,] 389 391 397 399 400 401 404 405 418 420
[2,] 389 391 396 397 398 399 402 403 405 407
[3,] 366 367 369 371 372 373 376 381 385 386
[4,] 395 397 399 400 402 403 405 406 407 408
[5,] 379 380 381 382 383 384 385 388 389 390
[6,] 395 396 397 398 400 402 403 404 405 407
[,209] [,210] [,211] [,212] [,213] [,214] [,215] [,216] [,217] [,218]
[1,] 421 423 427 431 432 435 436 437 438 439
[2,] 409 411 414 421 422 424 425 426 429 430
[3,] 388 390 391 395 399 400 401 403 404 405
[4,] 412 415 416 417 422 423 424 425 429 430
[5,] 397 399 400 401 402 405 406 407 408 409
[6,] 408 409 412 414 415 416 419 420 421 425
[,219] [,220] [,221] [,222] [,223] [,224] [,225] [,226] [,227] [,228]
[1,] 440 442 444 448 449 450 451 455 460 462
[2,] 432 436 438 439 440 445 449 452 454 455
[3,] 406 407 408 411 412 413 414 416 418 419
[4,] 442 443 444 448 449 450 451 454 455 457
[5,] 411 412 416 418 420 421 422 426 429 430
[6,] 426 428 430 431 432 435 438 439 443 446
[,229] [,230] [,231] [,232] [,233] [,234] [,235] [,236] [,237] [,238]
[1,] 469 472 477 478 479 481 486 491 494 495
[2,] 457 458 462 463 464 466 468 469 470 471
[3,] 422 424 425 429 430 431 433 435 440 441
[4,] 458 461 463 468 469 475 476 477 482 487
[5,] 433 435 439 441 443 446 449 450 451 462
[6,] 447 448 451 453 455 457 458 460 463 464
[,239] [,240] [,241] [,242] [,243] [,244] [,245] [,246] [,247] [,248]
[1,] 496 497 499 500 501 503 504 505 506 508
[2,] 472 473 474 475 478 481 485 488 489 490
[3,] 443 445 447 448 451 453 455 457 458 459
[4,] 489 490 491 492 494 497 498 501 502 503
[5,] 463 468 469 472 473 474 477 479 480 483
[6,] 466 468 470 473 475 479 481 484 487 489
[,249] [,250] [,251] [,252] [,253] [,254] [,255] [,256] [,257] [,258]
[1,] 509 510 511 512 515 518 522 523 524 526
[2,] 491 492 493 494 496 497 500 503 505 508
[3,] 461 463 468 469 470 471 472 474 479 480
[4,] 509 512 513 517 520 521 523 524 526 527
[5,] 484 486 487 488 489 490 491 492 494 496
[6,] 491 492 494 495 497 498 500 504 505 508
[,259] [,260] [,261] [,262] [,263] [,264] [,265] [,266] [,267] [,268]
[1,] 529 531 534 535 537 539 542 543 545 546
[2,] 510 511 516 517 519 520 521 522 523 524
[3,] 486 488 491 492 494 496 497 499 500 501
[4,] 528 529 530 531 536 537 539 540 542 544
[5,] 497 500 501 502 505 507 508 513 514 517
[6,] 510 511 512 513 515 519 520 521 523 524
[,269] [,270] [,271] [,272] [,273] [,274] [,275] [,276] [,277] [,278]
[1,] 551 553 554 555 556 560 562 564 568 569
[2,] 526 528 531 533 534 535 540 541 543 544
[3,] 502 503 506 509 511 512 514 515 516 518
[4,] 545 546 549 550 556 557 559 560 563 565
[5,] 518 520 522 529 530 531 532 536 537 540
[6,] 526 529 530 531 533 534 536 539 540 542
[,279] [,280] [,281] [,282] [,283] [,284] [,285] [,286] [,287] [,288]
[1,] 570 571 574 576 578 581 582 583 585 588
[2,] 545 549 550 552 558 559 561 562 563 564
[3,] 520 521 523 525 530 532 533 535 541 542
[4,] 567 569 571 573 578 579 580 582 583 586
[5,] 542 545 547 550 557 558 559 560 561 563
[6,] 543 545 546 547 548 549 550 552 553 554
[,289] [,290] [,291] [,292] [,293] [,294] [,295] [,296] [,297] [,298]
[1,] 589 590 596 599 600 602 604 607 608 609
[2,] 565 569 570 573 574 575 578 579 580 583
[3,] 543 546 548 550 554 555 557 558 559 560
[4,] 587 588 589 595 596 597 599 600 601 603
[5,] 566 568 570 571 573 575 576 579 584 586
[6,] 556 560 561 562 566 567 571 572 573 574
[,299] [,300] [,301] [,302] [,303] [,304] [,305] [,306] [,307] [,308]
[1,] 615 616 618 621 623 624 627 629 631 633
[2,] 584 588 593 594 595 597 600 601 602 605
[3,] 561 562 563 564 565 567 568 577 578 579
[4,] 605 606 610 611 612 613 615 616 617 618
[5,] 588 589 592 594 598 599 602 603 604 605
[6,] 575 576 577 581 583 589 593 594 595 596
[,309] [,310] [,311] [,312] [,313] [,314] [,315] [,316] [,317] [,318]
[1,] 634 635 636 637 639 640 642 645 646 647
[2,] 610 611 613 618 619 621 624 629 631 633
[3,] 581 582 583 585 587 588 589 591 594 596
[4,] 619 620 623 624 625 626 629 634 637 640
[5,] 608 611 613 616 617 620 624 625 627 632
[6,] 598 600 601 602 604 609 610 611 614 615
[,319] [,320] [,321] [,322] [,323] [,324] [,325] [,326] [,327] [,328]
[1,] 648 649 652 653 655 656 657 659 663 666
[2,] 634 636 637 638 640 643 644 645 650 651
[3,] 597 599 601 602 603 604 605 606 608 611
[4,] 642 649 654 655 656 657 658 660 661 664
[5,] 634 639 642 646 647 650 653 662 663 665
[6,] 616 618 619 620 622 624 628 630 631 634
[,329] [,330] [,331] [,332] [,333] [,334] [,335] [,336] [,337] [,338]
[1,] 668 669 672 674 675 677 678 679 685 689
[2,] 653 656 660 664 666 667 668 670 671 673
[3,] 613 614 622 623 624 628 629 630 631 632
[4,] 665 666 668 669 670 673 674 675 676 677
[5,] 666 667 669 672 673 674 675 678 679 681
[6,] 636 638 641 644 646 647 648 649 650 652
[,339] [,340] [,341] [,342] [,343] [,344] [,345] [,346] [,347] [,348]
[1,] 691 694 697 698 701 705 707 708 709 710
[2,] 674 675 678 679 680 681 685 686 687 688
[3,] 633 636 637 641 644 646 651 652 653 654
[4,] 678 679 680 682 685 686 688 689 690 691
[5,] 682 687 690 691 694 698 701 705 708 711
[6,] 653 654 656 658 659 661 662 668 669 671
[,349] [,350] [,351] [,352] [,353] [,354] [,355] [,356] [,357] [,358]
[1,] 711 714 715 717 719 721 726 728 730 731
[2,] 693 694 697 699 700 704 706 708 709 712
[3,] 655 657 658 659 660 661 663 668 670 671
[4,] 693 695 696 699 700 701 702 703 704 706
[5,] 712 713 714 715 716 717 718 719 720 721
[6,] 672 674 675 678 680 681 683 684 689 691
[,359] [,360] [,361] [,362] [,363] [,364] [,365] [,366] [,367] [,368]
[1,] 732 733 734 735 738 739 740 745 749 750
[2,] 714 715 716 719 721 722 726 727 730 731
[3,] 673 678 679 680 681 682 684 685 686 689
[4,] 707 710 711 715 717 718 720 721 722 723
[5,] 722 725 726 730 731 732 733 734 735 738
[6,] 693 697 698 701 703 705 706 710 712 713
[,369] [,370] [,371] [,372] [,373] [,374] [,375] [,376] [,377] [,378]
[1,] 751 752 753 757 758 760 762 765 768 769
[2,] 733 734 735 737 738 739 740 742 743 744
[3,] 692 693 695 701 706 710 711 715 716 718
[4,] 724 725 726 727 728 729 731 733 734 735
[5,] 739 740 741 742 744 746 749 750 751 753
[6,] 717 720 721 722 724 725 726 729 730 734
[,379] [,380] [,381] [,382] [,383] [,384] [,385] [,386] [,387] [,388]
[1,] 771 774 775 778 779 780 781 782 784 785
[2,] 745 747 748 749 750 751 752 755 756 757
[3,] 719 721 724 725 728 730 732 734 739 741
[4,] 739 744 745 747 749 751 753 754 759 760
[5,] 757 760 763 765 766 769 771 772 776 779
[6,] 735 736 738 739 740 741 744 745 747 748
[,389] [,390] [,391] [,392] [,393] [,394] [,395] [,396] [,397] [,398]
[1,] 786 787 788 792 794 795 796 797 803 804
[2,] 758 759 760 762 763 764 766 767 769 770
[3,] 742 744 745 748 750 753 758 759 763 766
[4,] 762 767 769 770 772 774 775 776 777 780
[5,] 780 781 783 786 787 788 789 792 794 795
[6,] 751 752 755 756 758 759 760 761 763 765
[,399] [,400] [,401] [,402] [,403] [,404] [,405] [,406] [,407] [,408]
[1,] 806 808 812 814 815 819 820 823 826 830
[2,] 772 773 778 781 782 783 784 785 788 789
[3,] 768 769 771 772 773 776 777 778 780 783
[4,] 786 787 792 793 794 796 798 799 800 803
[5,] 798 801 802 804 805 806 807 808 810 812
[6,] 766 767 769 774 775 779 782 783 784 786
[,409] [,410] [,411] [,412] [,413] [,414] [,415] [,416] [,417] [,418]
[1,] 831 833 835 836 838 843 845 847 848 849
[2,] 791 792 793 794 795 797 799 801 803 804
[3,] 784 785 786 788 789 792 793 794 795 799
[4,] 808 809 810 811 814 816 818 819 820 821
[5,] 814 816 822 824 825 826 828 830 836 838
[6,] 787 789 792 793 794 795 798 803 804 806
[,419] [,420] [,421] [,422] [,423] [,424] [,425] [,426] [,427] [,428]
[1,] 851 852 854 855 857 858 859 860 861 863
[2,] 805 807 809 810 811 814 815 817 819 820
[3,] 802 804 806 807 808 810 811 815 816 818
[4,] 824 825 826 827 830 831 832 835 836 837
[5,] 839 840 841 842 843 848 850 851 852 857
[6,] 807 808 809 812 813 814 815 816 817 821
[,429] [,430] [,431] [,432] [,433] [,434] [,435] [,436] [,437] [,438]
[1,] 865 866 867 868 870 874 876 878 879 883
[2,] 821 822 825 826 827 828 829 830 832 833
[3,] 819 823 824 827 828 831 833 837 839 840
[4,] 840 842 843 844 845 846 849 850 851 852
[5,] 859 863 864 866 867 868 871 873 878 879
[6,] 824 827 833 834 835 838 840 841 843 844
[,439] [,440] [,441] [,442] [,443] [,444] [,445] [,446] [,447] [,448]
[1,] 885 886 888 889 890 892 893 894 896 897
[2,] 834 837 839 840 842 843 845 848 849 850
[3,] 844 845 846 847 849 853 855 858 859 860
[4,] 854 863 864 868 872 874 877 879 881 885
[5,] 880 881 882 886 887 889 890 891 892 893
[6,] 845 849 850 851 855 857 863 864 866 869
[,449] [,450] [,451] [,452] [,453] [,454] [,455] [,456] [,457] [,458]
[1,] 898 899 901 902 908 909 912 916 918 920
[2,] 851 852 855 856 857 858 859 864 865 866
[3,] 861 862 864 866 871 872 876 877 879 880
[4,] 888 890 891 899 900 901 903 904 906 907
[5,] 895 896 898 904 905 907 909 914 916 918
[6,] 870 871 873 874 877 878 879 881 882 885
[,459] [,460] [,461] [,462] [,463] [,464] [,465] [,466] [,467] [,468]
[1,] 921 922 923 930 931 933 934 935 938 939
[2,] 868 875 876 878 879 881 882 883 884 885
[3,] 882 886 887 888 889 890 891 897 898 900
[4,] 911 912 913 915 916 918 920 921 922 924
[5,] 920 921 922 923 924 926 928 932 933 934
[6,] 887 888 890 892 899 900 901 903 905 909
[,469] [,470] [,471] [,472] [,473] [,474] [,475] [,476] [,477] [,478]
[1,] 945 946 949 952 953 954 955 956 961 962
[2,] 886 887 888 892 893 895 898 900 901 902
[3,] 902 903 906 907 909 911 912 913 917 918
[4,] 926 927 929 931 933 936 937 939 946 947
[5,] 936 937 938 939 941 944 946 947 951 953
[6,] 910 911 912 914 916 921 922 923 926 930
[,479] [,480] [,481] [,482] [,483] [,484] [,485] [,486] [,487] [,488]
[1,] 963 965 969 972 974 975 976 977 979 981
[2,] 903 905 908 910 911 912 920 921 922 924
[3,] 922 924 929 930 932 933 934 935 937 939
[4,] 950 951 952 953 954 956 957 959 960 964
[5,] 954 961 962 963 965 970 974 976 979 980
[6,] 931 932 935 937 940 941 942 943 944 947
[,489] [,490] [,491] [,492] [,493] [,494] [,495] [,496] [,497] [,498]
[1,] 986 989 990 992 994 997 1000 NA NA NA
[2,] 925 926 929 931 932 934 936 937 938 939
[3,] 940 941 945 946 947 951 954 955 956 957
[4,] 965 966 969 971 973 978 983 984 985 986
[5,] 981 982 985 986 990 991 992 995 997 998
[6,] 948 951 953 954 956 958 961 962 963 964
[,499] [,500] [,501] [,502] [,503] [,504] [,505] [,506] [,507] [,508]
[1,] NA NA NA NA NA NA NA NA NA NA
[2,] 940 943 945 948 950 953 956 957 960 961
[3,] 958 960 962 963 966 967 968 970 972 973
[4,] 987 991 992 993 997 999 NA NA NA NA
[5,] 999 1000 NA NA NA NA NA NA NA NA
[6,] 965 967 968 970 973 974 977 978 981 982
[,509] [,510] [,511] [,512] [,513] [,514] [,515] [,516] [,517] [,518]
[1,] NA NA NA NA NA NA NA NA NA NA
[2,] 963 965 968 969 970 971 972 973 974 977
[3,] 974 975 978 980 983 984 985 986 990 991
[4,] NA NA NA NA NA NA NA NA NA NA
[5,] NA NA NA NA NA NA NA NA NA NA
[6,] 983 986 987 988 992 993 994 995 998 999
[,519] [,520] [,521] [,522] [,523] [,524] [,525] [,526] [,527] [,528]
[1,] NA NA NA NA NA NA NA NA NA NA
[2,] 978 982 983 984 985 986 987 988 989 991
[3,] 992 993 994 995 998 1000 NA NA NA NA
[4,] NA NA NA NA NA NA NA NA NA NA
[5,] NA NA NA NA NA NA NA NA NA NA
[6,] 1000 NA NA NA NA NA NA NA NA NA
[,529] [,530] [,531] [,532] [,533] [,534] [,535] [,536] [,537] [,538]
[1,] NA NA NA NA NA NA NA NA NA NA
[2,] 992 994 1000 NA NA NA NA NA NA NA
[3,] NA NA NA NA NA NA NA NA NA NA
[4,] NA NA NA NA NA NA NA NA NA NA
[5,] NA NA NA NA NA NA NA NA NA NA
[6,] NA NA NA NA NA NA NA NA NA NA
[,539] [,540] [,541] [,542] [,543] [,544] [,545] [,546] [,547] [,548]
[1,] NA NA NA NA NA NA NA NA NA NA
[2,] NA NA NA NA NA NA NA NA NA NA
[3,] NA NA NA NA NA NA NA NA NA NA
[4,] NA NA NA NA NA NA NA NA NA NA
[5,] NA NA NA NA NA NA NA NA NA NA
[6,] NA NA NA NA NA NA NA NA NA NA
[,549] [,550] [,551]
[1,] NA NA NA
[2,] NA NA NA
[3,] NA NA NA
[4,] NA NA NA
[5,] NA NA NA
[6,] NA NA NA
done successfully.
Executing test function test.networkgen2 ... [1] "K in genNET: 6"
[1] "K in genNET: 6"
done successfully.
Executing test function test.networkgen_time ... Error in W2[[0]] : variable W2 doesn't exist
In addition: Warning messages:
1: In RNGkind(kind = testSuite$rngKind, normal.kind = testSuite$rngNormalKind) :
RNGkind: Marsaglia-Multicarry has poor statistical properties
2: In RNGkind(kind = testSuite$rngKind, normal.kind = testSuite$rngNormalKind) :
RNGkind: severe deviations from normality for Kinderman-Ramage + Marsaglia-Multicarry
3: In RNGkind(kind = testSuite$rngKind, normal.kind = testSuite$rngNormalKind) :
RNGkind: Marsaglia-Multicarry has poor statistical properties
4: In RNGkind(kind = testSuite$rngKind, normal.kind = testSuite$rngNormalKind) :
RNGkind: severe deviations from normality for Kinderman-Ramage + Marsaglia-Multicarry
5: In (function (n, probs) :
This function is deprecated, please use rcat.b1() instead.
6: In (function (n, probs) :
This function is deprecated, please use rcat.b1() instead.
Error : error while evaluating node F.W2_0 formula:
W2[[0]].
Check syntax specification.
Error in set.DAG(D) :
...attempt to simulate data from DAG failed...
Error in F.W1[0:1][[0]] :
...network indexing variable evaluated to more than one column ...
Error : error while evaluating node F.W1_2 formula:
F.W1[0:1][[0]].
Check syntax specification.
Error in set.DAG(D) :
...attempt to simulate data from DAG failed...
Error : Undefined variable: F.A
Error in set.DAG(D) :
...attempt to simulate data from DAG failed...
done successfully.
Executing test function test.tKmaxnet ... Error in eval.nodeform.full(expr_call = expr_call, expr_str = expr_str, :
reference A[...] at node B is not allowed; node A was defined as time-invariant
Error in set.DAG(D.t) :
...attempt to simulate data from DAG failed...
Error : Undefined variable: xyz
Error in set.DAG(D.t) :
...attempt to simulate data from DAG failed...
Error in eval.nodeform.full(expr_call = expr_call, expr_str = expr_str, :
reference A[...] at node B is not allowed; node A was defined as time-invariant
In addition: Warning message:
In FUN(X[[i]], ...) : the argument inside [...] cannot be parsed: A[rnorm]
Error in set.DAG(D.t) :
...attempt to simulate data from DAG failed...
done successfully.
Executing test function test.MV ... Loading required package: mvtnorm
...automatically assigning order attribute to some nodes...
node X1.X2.X3, order:1
node Y1.Y2, order:2
node A, order:3
Loading required package: copula
Attaching package: 'copula'
The following object is masked from 'package:simcausal':
A
Don't know the expression result type S4FALSE
Don't know the expression result type S4FALSE
Don't know the expression result type S4FALSE
Don't know the expression result type S4FALSE
Don't know the expression result type S4FALSE
Don't know the expression result type S4FALSE
Don't know the expression result type S4FALSE
Don't know the expression result type S4FALSE
Don't know the expression result type S4FALSE
Don't know the expression result type S4FALSE
[1] "current list of user-defined vectorized functions: qbinom"
Loading required package: bindata
done successfully.
Executing test function test.NSEbug ... [1] "s"
[1] 1
[1] "t"
[1] 0
[1] "variable"
[1] "(Intercept)"
[1] "s"
[1] 1
[1] "t"
[1] 0
[1] "variable"
[1] "mse"
[1] "s"
[1] 2
[1] "t"
[1] 0
[1] "variable"
[1] "(Intercept)"
[1] "s"
[1] 2
[1] "t"
[1] 0
[1] "variable"
[1] "mse"
existing node L1_0 was modified
[1] "s"
[1] 3
[1] "t"
[1] 0
[1] "variable"
[1] "(Intercept)"
[1] "s"
[1] 3
[1] "t"
[1] 0
[1] "variable"
[1] "mse"
existing node L1_0 was modified
[1] "s"
[1] 1
[1] "t"
[1] 1
[1] "variable"
[1] "(Intercept)"
[1] "s"
[1] 1
[1] "t"
[1] 1
[1] "variable"
[1] "mse"
[1] "s"
[1] 2
[1] "t"
[1] 1
[1] "variable"
[1] "(Intercept)"
[1] "s"
[1] 2
[1] "t"
[1] 1
[1] "variable"
[1] "mse"
[1] "s"
[1] 3
[1] "t"
[1] 1
[1] "variable"
[1] "(Intercept)"
[1] "s"
[1] 3
[1] "t"
[1] 1
[1] "variable"
[1] "mse"
[1] "s"
[1] 1
[1] "t"
[1] 2
[1] "variable"
[1] "(Intercept)"
[1] "s"
[1] 1
[1] "t"
[1] 2
[1] "variable"
[1] "mse"
existing node L1_2 was modified
[1] "s"
[1] 2
[1] "t"
[1] 2
[1] "variable"
[1] "(Intercept)"
[1] "s"
[1] 2
[1] "t"
[1] 2
[1] "variable"
[1] "mse"
[1] "s"
[1] 3
[1] "t"
[1] 2
[1] "variable"
[1] "(Intercept)"
[1] "s"
[1] 3
[1] "t"
[1] 2
[1] "variable"
[1] "mse"
done successfully.
------------------- UNIT TEST SUMMARY ---------------------
RUNIT TEST PROTOCOL -- Thu Jul 18 09:11:53 2024
Number of test functions: 28
Number of errors: 1
Number of failures: 0
1 Test Suite :
simcausal unit testing - 28 test functions, 1 error, 0 failures
ERROR in test.latent: Error : from() was deprecated in igraph 2.0.4 and is now defunct.
ℹ Please use .from() instead.
Error:
unit testing failed (#test failures: 0, #R errors: 1)
In addition: Warning messages:
1: In RNGkind(kind = testSuite$rngKind, normal.kind = testSuite$rngNormalKind) :
RNGkind: Marsaglia-Multicarry has poor statistical properties
2: In RNGkind(kind = testSuite$rngKind, normal.kind = testSuite$rngNormalKind) :
RNGkind: severe deviations from normality for Kinderman-Ramage + Marsaglia-Multicarry
3: In RNGkind(kind = testSuite$rngKind, normal.kind = testSuite$rngNormalKind) :
RNGkind: Marsaglia-Multicarry has poor statistical properties
4: In RNGkind(kind = testSuite$rngKind, normal.kind = testSuite$rngNormalKind) :
RNGkind: severe deviations from normality for Kinderman-Ramage + Marsaglia-Multicarry
Execution halted
```
In both
-
checking re-building of vignette outputs ... WARNING
``
Error(s) in re-building vignettes:
...
--- re-building ‘simcausal_vignette.Rnw’ using knitr
Error: processing vignette 'simcausal_vignette.Rnw' failed with diagnostics:
Running 'texi2dvi' on 'simcausal_vignette.tex' failed.
LaTeX errors:
! LaTeX Error: Filecolortbl.sty' not found.
Type X to quit or to proceed,
or enter new name. (Default extension: sty)
! Emergency stop.
l.55 \RequirePackage
[T1]{fontenc}^^M
! ==> Fatal error occurred, no output PDF file produced!
--- failed re-building ‘simcausal_vignette.Rnw’
SUMMARY: processing the following file failed:
‘simcausal_vignette.Rnw’
Error: Vignette re-building failed.
Execution halted
```
skynet
- Version: 1.4.3
- GitHub: https://github.com/ropensci/skynet
- Source code: https://github.com/cran/skynet
- Date/Publication: 2022-06-17 13:00:02 UTC
- Number of recursive dependencies: 98
Run revdepcheck::cloud_details(, "skynet") for more info
Newly broken
-
checking tests ... ERROR
```
Running ‘testthat.R’
Running the tests in ‘tests/testthat.R’ failed.
Complete output:
> library(testthat)
> library(skynet)
>
> test_check("skynet")
[ FAIL 1 | WARN 24 | SKIP 4 | PASS 69 ]
══ Skipped tests (4) ═══════════════════════════════════════════════════════════
• On CRAN (4): 'test_download_db1b.R:6:3', 'test_download_ontime.R:6:3',
'test_download_t100.R:6:3', 'test_download_t100.R:13:3'
══ Failed tests ════════════════════════════════════════════════════════════════
── Error ('test_smallerfunctions.R:16:3'): From To function ────────────────────
Error: from() was deprecated in igraph 2.0.4 and is now defunct.
ℹ Please use .from() instead.
Backtrace:
▆
1. ├─testthat::expect_output(...) at test_smallerfunctions.R:16:3
2. │ └─testthat:::quasi_capture(...)
3. │ ├─testthat (local) .capture(...)
4. │ │ └─testthat::capture_output_lines(code, print, width = width)
5. │ │ └─testthat:::eval_with_output(code, print = print, width = width)
6. │ │ ├─withr::with_output_sink(path, withVisible(code))
7. │ │ │ └─base::force(code)
8. │ │ └─base::withVisible(code)
9. │ └─rlang::eval_bare(quo_get_expr(.quo), quo_get_env(.quo))
10. ├─base::print(from_to_stats(test$gDir, "JFK", orig = "from"))
11. └─skynet::from_to_stats(test$gDir, "JFK", orig = "from")
12. ├─E(x)[from(V(x)[y])]
13. └─igraph:::[.igraph.es(E(x), from(V(x)[y]))
14. └─igraph:::lazy_eval(...)
15. └─base::lapply(x, lazy_eval, data = data)
16. └─igraph (local) FUN(X[[i]], ...)
17. └─base::eval(x$expr, data, x$env)
18. └─base::eval(x$expr, data, x$env)
19. └─igraph (local) from(V(x)[y])
20. └─lifecycle::deprecate_stop("2.0.4", "from()", ".from()")
21. └─lifecycle:::deprecate_stop0(msg)
22. └─rlang::cnd_signal(...)
[ FAIL 1 | WARN 24 | SKIP 4 | PASS 69 ]
Error: Test failures
Execution halted
```
In both
- checking dependencies in R code ... NOTE
Namespace in Imports field not imported from: ‘maps’
All declared Imports should be used.
igraph/rigraph documentation built on Aug. 16, 2024, 1:38 p.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
BCT
- Version: 1.2
- GitHub: NA
- Source code: https://github.com/cran/BCT
- Date/Publication: 2022-05-12 14:00:05 UTC
- Number of recursive dependencies: 13
Run revdepcheck::cloud_details(, "BCT") for more info
Newly broken
-
checking examples ... ERROR ``` Running examples in ‘BCT-Ex.R’ failed The error most likely occurred in:
Name: draw_models
Title: Plot the results of the BCT and kBCT functions
Aliases: draw_models
** Examples
Use the pewee dataset as an example:
q <- BCT(pewee, 5) # maximum depth of 5
draw_models(q) Error in getval(w, name) : argument "newval" is missing, with no default Calls: draw_models ... .igraph.graph.union.or.intersection -> rename.attr.if.needed -> getval Execution halted ```
In both
-
checking C++ specification ... NOTE
Specified C++11: please drop specification unless essential -
checking Rd files ... NOTE
checkRd: (-1) gene_s.Rd:17: Lost braces; missing escapes or markup? 17 | giving the virus access to the cell and thus facilitating the COVID-19 disease. The gene sequence is mapped to the alphabet {0,1,2,3} via the obvious map A->0, C->1, G->2, T->3. | ^ checkRd: (-1) sars_cov_2.Rd:16: Lost braces; missing escapes or markup? 16 | It consists of n = 29903 base pairs. The gene sequence is mapped to the alphabet {0,1,2,3} via the obvious map A->0, C->1, G->2, T->3. | ^
netropy
- Version: 0.1.0
- GitHub: NA
- Source code: https://github.com/cran/netropy
- Date/Publication: 2022-02-02 08:20:02 UTC
- Number of recursive dependencies: 85
Run revdepcheck::cloud_details(, "netropy") for more info
Newly broken
-
checking examples ... ERROR ``` Running examples in ‘netropy-Ex.R’ failed The error most likely occurred in:
Name: assoc_graph
Title: Association Graphs
Aliases: assoc_graph
** Examples
library(ggraph) Loading required package: ggplot2
use internal data set
data(lawdata) df.att <- lawdata[[4]]
three steps of data editing:
1. categorize variables 'years' and 'age' based on
approximately three equally size groups (values based on cdf)
2. make sure all outcomes start from the value 0 (optional)
3. remove variable 'senior' as it consists of only unique values (thus redundant)
df.att.ed <- data.frame( + status = df.att$status, + gender = df.att$gender, + office = df.att$office-1, + years = ifelse(df.att$years<=3,0, + ifelse(df.att$years<=13,1,2)), + age = ifelse(df.att$age<=35,0, + ifelse(df.att$age<=45,1,2)), + practice = df.att$practice, + lawschool= df.att$lawschool-1)
association graph based on cutoff 0.15
assoc_graph(df.att.ed, 0.15) Error in igraph::distances(g) : At vendor/cigraph/src/paths/dijkstra.c:128 : Weights must not contain NaN values. Invalid value Calls: assoc_graph ... layout_with_stress -> .layout_with_stress_dim -> Execution halted ```
-
checking re-building of vignette outputs ... ERROR ``` Error(s) in re-building vignettes: ... --- re-building ‘joint_entropies.Rmd’ using rmarkdown
Quitting from lines 96-98 [assoc_g] (joint_entropies.Rmd) Error: processing vignette 'joint_entropies.Rmd' failed with diagnostics: At vendor/cigraph/src/paths/dijkstra.c:128 : Weights must not contain NaN values. Invalid value --- failed re-building ‘joint_entropies.Rmd’
--- re-building ‘prediction_power.Rmd’ using rmarkdown --- finished re-building ‘prediction_power.Rmd’
--- re-building ‘univariate_bivariate_trivariate.Rmd’ using rmarkdown --- finished re-building ‘univariate_bivariate_trivariate.Rmd’
--- re-building ‘variable_domains.Rmd’ using rmarkdown --- finished re-building ‘variable_domains.Rmd’
SUMMARY: processing the following file failed: ‘joint_entropies.Rmd’
Error: Vignette re-building failed. Execution halted ```
In both
- checking Rd files ... NOTE
checkRd: (-1) lawdata.Rd:15: Lost braces in \itemize; meant \describe ? checkRd: (-1) lawdata.Rd:16: Lost braces in \itemize; meant \describe ? checkRd: (-1) lawdata.Rd:17: Lost braces in \itemize; meant \describe ? checkRd: (-1) lawdata.Rd:18: Lost braces in \itemize; meant \describe ? checkRd: (-1) lawdata.Rd:19: Lost braces in \itemize; meant \describe ? checkRd: (-1) lawdata.Rd:20: Lost braces in \itemize; meant \describe ? checkRd: (-1) lawdata.Rd:21: Lost braces in \itemize; meant \describe ? checkRd: (-1) lawdata.Rd:22: Lost braces in \itemize; meant \describe ?
rmcfs
- Version: 1.3.5
- GitHub: NA
- Source code: https://github.com/cran/rmcfs
- Date/Publication: 2021-09-18 13:30:02 UTC
- Number of recursive dependencies: 65
Run revdepcheck::cloud_details(, "rmcfs") for more info
Newly broken
-
checking tests ... ERROR ``` Running ‘test-all.R’ Running the tests in ‘tests/test-all.R’ failed. Complete output: > library(testthat) > library(rmcfs) Loading required package: rJava
####################### # rmcfs version 1.3.5 # ####################### If used please cite the following paper: M. Draminski, J. Koronacki (2018), rmcfs: An R Package for Monte Carlo Feature Selection and Interdependency Discovery, Journal of Statistical Software, vol 85(12), 1-28, doi:10.18637/jss.v085.i12.test_check("rmcfs") X1 X2 X3 X4 X5 X6 X7 1 0.3390729 0.6827881 0.9614099 0.02778712 0.48614910 0.43471764 0.02274122 2 0.8394404 0.6015412 0.1001408 0.52731078 0.06380247 0.51473265 0.93913671 3 0.3466835 0.2388687 0.7632227 0.88031907 0.78454623 0.66301097 0.29294872 4 0.3337749 0.2581659 0.9479664 0.37306337 0.41832164 0.14316659 0.16432657 5 0.4763512 0.7293096 0.8186347 0.04795913 0.98101808 0.34448739 0.39910256 6 0.8921983 0.4525708 0.3082923 0.13862825 0.28288396 0.40576358 0.45957541 7 0.8643395 0.1751268 0.6495795 0.32149212 0.84788215 0.08531101 0.43403085 8 0.3899895 0.7466983 0.9533555 0.15483161 0.08223923 0.93257193 0.51700983 9 0.7773207 0.1049876 0.9537327 0.13222817 0.88645875 0.83838407 0.84624575 10 0.9606180 0.8645449 0.3399792 0.22130593 0.47193073 0.87943330 0.05516429 X8 X9 X10 1 0.6547329 0.85750154 0.92974321 2 0.1328278 0.37088354 0.90093927 3 0.3418099 0.31420183 0.75088219 4 0.7313716 0.82853436 0.67656877 5 0.9072914 0.45184151 0.64801345 6 0.6961970 0.31587841 0.07324687 7 0.2415792 0.09780854 0.42355842 8 0.6441072 0.06490054 0.53082436 9 0.2807502 0.68945737 0.94270476 10 0.9576365 0.66805060 0.71222456
X27 X28 X29 X30 A1 A2 B1 B2 C1 C2 class60 0.22730045 0.32639993 0.53409931 0.3797047 0 0 B B 0 0 B 61 0.16826293 0.29429765 0.81091625 0.7799683 0 0 0 0 C C C 62 0.14976251 0.82289798 0.65090651 0.9615772 0 0 0 0 0 0 C 63 0.64809716 0.41434742 0.77098111 0.4439984 0 0 0 0 C C C 64 0.48557255 0.01017122 0.03455832 0.2765765 0 0 0 0 0 0 C 65 0.69324432 0.05240839 0.55866028 0.6528749 0 0 0 0 C C C 66 0.28966946 0.73037038 0.12008737 0.7547240 0 0 0 0 0 0 C 67 0.97867166 0.07339495 0.68629963 0.5887064 0 0 0 0 C C C 68 0.28812633 0.07035663 0.91412566 0.3668981 0 0 0 0 C C C 69 0.52576452 0.63717197 0.56034976 0.5093910 0 0 0 0 0 0 C 70 0.07782227 0.41330587 0.57557604 0.1442291 0 0 0 0 C C C class: 'data.frame' size: 70 x 37 X1 X2 X3 X4 X5 X6 X7 1 0.53509718 0.7440799 0.70465551 0.58037379 0.62995162 0.45447665 0.33625348 2 0.24689369 0.8384462 0.91905136 0.09076585 0.45587724 0.49380877 0.14465036 3 0.92234682 0.7993568 0.61013655 0.65609916 0.25400483 0.52620712 0.86807104 4 0.75348962 0.8936403 0.05585486 0.35750658 0.05748599 0.38301289 0.05432263 5 0.90350731 0.8311370 0.73615214 0.23402548 0.43530320 0.92889790 0.61388308 6 0.65154816 0.3707501 0.90396792 0.91643034 0.77113498 0.43341785 0.78563030 7 0.02077916 0.8719798 0.70181488 0.03055500 0.12915545 0.24476248 0.57944665 8 0.09587903 0.8007616 0.57769951 0.43907404 0.98391626 0.87625727 0.52716948 9 0.94958696 0.1772845 0.56583925 0.97369032 0.81417693 0.18265992 0.41051341 10 0.88817659 0.1632777 0.36717551 0.39914081 0.98168090 0.09032375 0.73052316 X8 X9 X10 1 0.74771232 0.9503260 0.6835156 2 0.71771862 0.9001683 0.5493564 3 0.09516487 0.6939464 0.6202255 4 0.47922473 0.8362239 0.2962362 5 0.12714205 0.4631887 0.2844997 6 0.26423701 0.6290997 0.6019828 7 0.22704179 0.4391008 0.1676721 8 0.18450321 0.9699211 0.9173871 9 0.01725596 0.2795489 0.9909104 10 0.93482027 0.9794688 0.7476684
X7 X8 X9 X10 A1 A2 B1 B2 C1 C2 class60 0.82457067 0.748439821 0.5923369 0.53276926 0 0 B B 0 0 B 61 0.52929045 0.952518457 0.9101575 0.11142191 0 0 0 0 C C C 62 0.43723560 0.362614568 0.3939510 0.77589348 0 0 0 0 0 0 C 63 0.05642579 0.346293530 0.9642003 0.06293980 0 0 0 0 C C C 64 0.24777458 0.454230331 0.2550940 0.70528527 0 0 0 0 C C C 65 0.39808635 0.668704697 0.1407718 0.04925588 0 0 0 0 C C C 66 0.11121328 0.529091748 0.5252544 0.78131666 0 0 0 0 0 0 C 67 0.42162257 0.002821188 0.4540667 0.78413926 0 0 0 0 0 0 C 68 0.23918923 0.861873356 0.5217224 0.55257574 0 0 0 0 0 0 C 69 0.85053229 0.556885670 0.0856974 0.85510841 0 0 0 0 C C C 70 0.22702517 0.901919867 0.5975495 0.20987204 0 0 0 0 C C C class: 'data.frame' size: 70 x 17Checking input data... Exporting params... Exporting input data... Running MCFS-ID... ################################################## ##### dmLab 2.3.4 [2021.07.08] ##### ################################################## Created by Michal Draminski [michal.draminski@ipipan.waw.pl] http://www.ipipan.eu/staff/m.draminski/ Polish Academy of Sciences - Institute of Computer Science ##################################################
Warning! Value of cutoffPermutations = 0 and cutoffMethod = 'permutations'. Using cutoffMethod = 'mean'.
MCFS-ID Experiment **** Loading header: 'input.adh'... Loading data: 'input.csv'... 70 objects and 17 attributes to load... Done Nominal target detected - using J48 model MCFS-ID param: ID-Graph is ON MCFS-ID param: balance classes is AUTO Classes = ["A", "B", "C"], Sizes = [40, 20, 10], classSizeRatio = 0.25, balanceValue = 1.0 Calculation of DecisionValuesTable... Starting MCFS-ID Procedure: projectionSize(m) = 4, projections(s) = 200, splits(t) = 5 Start time: Thu Jul 18 09:10:57 UTC 2024 Running: 2 threads... [ ] 0% Time: 00:00 ETA: --:-- [======> ] 10% Time: 00:00 ETA: --:-- [=============> ] 20% Time: 00:00 ETA: --:-- [====================> ] 30% Time: 00:00 ETA: --:-- [===========================> ] 40% Time: 00:00 ETA: --:-- [==================================> ] 50% Time: 00:00 ETA: --:-- [=========================================> ] 60% Time: 00:00 ETA: --:-- [================================================> ] 70% Time: 00:00 ETA: --:-- [=======================================================> ] 80% Time: 00:00 ETA: --:-- [==============================================================> ] 90% Time: 00:00 ETA: --:-- [=====================================================================>] 100% Time: 00:00 ETA: --:-- [=====================================================================>] 100% Time: 00:01 1000 trees built within 1.0 s.
Prediction Summary on a Random Subsample (st): Accuracy = 80.46% WeightedAccuracy = 70.92%
Cutoff RI (based on linear regression angle) = 0.0451185 Cutoff RI (based on k-means clustering) = 0.3971068 Cutoff RI (based on mean cutoff value) = 0.0451185 Important attributes (based on mean cutoff value) = 6 MCFS-ID Processing is done. Time: 1.1 s.
Reading results... Done. Selected 6 nodes and 12 edges. X1 X2 X3 X4 X5 X6 1 0.212941627 0.32259978 0.068299279 0.49303546 0.08453311 0.197323455 2 0.946402830 0.61844805 0.130542241 0.10290711 0.33361065 0.792430558 3 0.345473791 0.59392818 0.146773063 0.40479039 0.07040867 0.947512934 4 0.405422623 0.04812483 0.860316695 0.05246878 0.70507985 0.071240221 5 0.536541352 0.39381200 0.949276015 0.03656734 0.07419950 0.371038995 6 0.275828688 0.31159812 0.896443296 0.23109615 0.32501378 0.669765127 7 0.405671409 0.43268042 0.113651731 0.97980273 0.35894192 0.009998357 8 0.435509188 0.35949101 0.793385952 0.04629398 0.38833262 0.749893881 9 0.322054159 0.74693284 0.004317772 0.13195064 0.78544337 0.985846242 10 0.003435689 0.95245622 0.698139910 0.92844045 0.60434824 0.273909658 X7 X8 X9 X10 1 0.755911492 0.621500159 0.3899180 0.2825466 2 0.899729525 0.715610913 0.1276254 0.0551484 3 0.308047227 0.006459725 0.9787065 0.9982455 4 0.556854023 0.150810371 0.1611711 0.9606097 5 0.999194679 0.390337652 0.1158990 0.1403129 6 0.202094245 0.714161876 0.7860697 0.9566023 7 0.005399961 0.290325600 0.9052935 0.8806996 8 0.304086372 0.837331551 0.5998701 0.2326705 9 0.496753415 0.809496423 0.3001367 0.8077412 10 0.028377163 0.181867694 0.2260461 0.9273090
X7 X8 X9 X10 A1 A2 B1 B2 C1 C2 class60 0.99415022 0.52001874 0.7344113 0.14549320 0 0 0 0 0 0 B 61 0.83573589 0.22437866 0.2762943 0.42770355 0 0 0 0 C C C 62 0.72685534 0.42713241 0.3515303 0.28976193 0 0 0 0 C C C 63 0.40742995 0.76241285 0.8684239 0.33320559 0 0 0 0 0 0 C 64 0.95852635 0.59382830 0.7705493 0.38924415 0 0 0 0 C C C 65 0.80196490 0.71950937 0.9914391 0.33556065 0 0 0 0 C C C 66 0.70485195 0.28409134 0.9776807 0.10902903 0 0 0 0 C C C 67 0.74343472 0.44167497 0.3433687 0.08789696 0 0 0 0 C C C 68 0.04116168 0.75138835 0.8932992 0.90448572 0 0 0 0 0 0 C 69 0.05585319 0.93409234 0.6443492 0.42207226 0 0 0 0 0 0 C 70 0.89679041 0.07141334 0.2940574 0.03089679 0 0 0 0 0 0 C class: 'data.frame' size: 70 x 17Checking input data... Exporting params... Exporting input data... Running MCFS-ID... ################################################## ##### dmLab 2.3.4 [2021.07.08] ##### ################################################## Created by Michal Draminski [michal.draminski@ipipan.waw.pl] http://www.ipipan.eu/staff/m.draminski/ Polish Academy of Sciences - Institute of Computer Science ##################################################
MCFS-ID Cutoff Permutation Experiment #1/3 ********* Loading header: 'input.adh'... Loading data: 'input.csv'... 70 objects and 17 attributes to load... Done Pearson's correlation of shuffled decision: -0.0199 Nominal target detected - using J48 model MCFS-ID param: ID-Graph is ON MCFS-ID param: balance classes is AUTO Classes = ["A", "B", "C"], Sizes = [40, 20, 10], classSizeRatio = 0.25, balanceValue = 1.0 Calculation of DecisionValuesTable... Starting MCFS-ID Procedure: projectionSize(m) = 4, projections(s) = 200, splits(t) = 5 Start time: Thu Jul 18 09:10:58 UTC 2024 Running: 2 threads... [ ] 0% Time: 00:00 ETA: --:-- [======> ] 10% Time: 00:00 ETA: --:-- [=============> ] 20% Time: 00:00 ETA: --:-- [====================> ] 30% Time: 00:00 ETA: --:-- [===========================> ] 40% Time: 00:00 ETA: --:-- [==================================> ] 50% Time: 00:00 ETA: --:-- [=========================================> ] 60% Time: 00:00 ETA: --:-- [================================================> ] 70% Time: 00:00 ETA: --:-- [=======================================================> ] 80% Time: 00:00 ETA: --:-- [==============================================================> ] 90% Time: 00:00 ETA: --:-- [=====================================================================>] 100% Time: 00:00 ETA: --:-- [=====================================================================>] 100% Time: 00:00 1000 trees built within 0.481 s.
Prediction Summary on a Random Subsample (st): Accuracy = 49.63% WeightedAccuracy = 32.55%
Cutoff RI (based on linear regression angle) = 0.0210824 Cutoff RI (based on k-means clustering) = 0.0174957 Cutoff RI (based on mean cutoff value) = 0.0174957 Important attributes (based on mean cutoff value) = 6
MCFS-ID Cutoff Permutation Experiment #2/3 ********* Loading header: 'input.adh'... Loading data: 'input.csv'... 70 objects and 17 attributes to load... Done Pearson's correlation of shuffled decision: -0.1199 Nominal target detected - using J48 model MCFS-ID param: ID-Graph is ON MCFS-ID param: balance classes is AUTO Classes = ["A", "B", "C"], Sizes = [40, 20, 10], classSizeRatio = 0.25, balanceValue = 1.0 Calculation of DecisionValuesTable... Starting MCFS-ID Procedure: projectionSize(m) = 4, projections(s) = 200, splits(t) = 5 Start time: Thu Jul 18 09:10:59 UTC 2024 Running: 2 threads... [ ] 0% Time: 00:00 ETA: --:-- [======> ] 10% Time: 00:00 ETA: --:-- [=============> ] 20% Time: 00:00 ETA: --:-- [====================> ] 30% Time: 00:00 ETA: --:-- [===========================> ] 40% Time: 00:00 ETA: --:-- [==================================> ] 50% Time: 00:00 ETA: --:-- [=========================================> ] 60% Time: 00:00 ETA: --:-- [================================================> ] 70% Time: 00:00 ETA: --:-- [=======================================================> ] 80% Time: 00:00 ETA: --:-- [==============================================================> ] 90% Time: 00:00 ETA: --:-- [=====================================================================>] 100% Time: 00:00 ETA: --:-- [=====================================================================>] 100% Time: 00:00 1000 trees built within 0.352 s.
Prediction Summary on a Random Subsample (st): Accuracy = 50.93% WeightedAccuracy = 32.25%
Cutoff RI (based on linear regression angle) = 0.0085784 Cutoff RI (based on k-means clustering) = 0.0165298 Cutoff RI (based on mean cutoff value) = 0.0085784 Important attributes (based on mean cutoff value) = 6
MCFS-ID Cutoff Permutation Experiment #3/3 ********* Loading header: 'input.adh'... Loading data: 'input.csv'... 70 objects and 17 attributes to load... Done Pearson's correlation of shuffled decision: 0.2000 Nominal target detected - using J48 model MCFS-ID param: ID-Graph is ON MCFS-ID param: balance classes is AUTO Classes = ["A", "B", "C"], Sizes = [40, 20, 10], classSizeRatio = 0.25, balanceValue = 1.0 Calculation of DecisionValuesTable... Starting MCFS-ID Procedure: projectionSize(m) = 4, projections(s) = 200, splits(t) = 5 Start time: Thu Jul 18 09:10:59 UTC 2024 Running: 2 threads... [ ] 0% Time: 00:00 ETA: --:-- [======> ] 10% Time: 00:00 ETA: --:-- [=============> ] 20% Time: 00:00 ETA: --:-- [====================> ] 30% Time: 00:00 ETA: --:-- [===========================> ] 40% Time: 00:00 ETA: --:-- [==================================> ] 50% Time: 00:00 ETA: --:-- [=========================================> ] 60% Time: 00:00 ETA: --:-- [================================================> ] 70% Time: 00:00 ETA: --:-- [=======================================================> ] 80% Time: 00:00 ETA: --:-- [==============================================================> ] 90% Time: 00:00 ETA: --:-- [=====================================================================>] 100% Time: 00:00 ETA: --:-- [=====================================================================>] 100% Time: 00:00 1000 trees built within 0.425 s.
Prediction Summary on a Random Subsample (st): Accuracy = 50.74% WeightedAccuracy = 33.26%
Cutoff RI (based on linear regression angle) = 0.0138890 Cutoff RI (based on k-means clustering) = 0.0206007 Cutoff RI (based on mean cutoff value) = 0.0138890 Important attributes (based on mean cutoff value) = 6
MCFS-ID Experiment **** Loading header: 'input.adh'... Loading data: 'input.csv'... 70 objects and 17 attributes to load... Done Nominal target detected - using J48 model MCFS-ID param: ID-Graph is ON MCFS-ID param: balance classes is AUTO Classes = ["A", "B", "C"], Sizes = [40, 20, 10], classSizeRatio = 0.25, balanceValue = 1.0 Calculation of DecisionValuesTable... Starting MCFS-ID Procedure: projectionSize(m) = 4, projections(s) = 200, splits(t) = 5 Start time: Thu Jul 18 09:11:00 UTC 2024 Running: 2 threads... [ ] 0% Time: 00:00 ETA: --:-- [======> ] 10% Time: 00:00 ETA: --:-- [=============> ] 20% Time: 00:00 ETA: --:-- [====================> ] 30% Time: 00:00 ETA: --:-- [===========================> ] 40% Time: 00:00 ETA: --:-- [==================================> ] 50% Time: 00:00 ETA: --:-- [=========================================> ] 60% Time: 00:00 ETA: --:-- [================================================> ] 70% Time: 00:00 ETA: --:-- [=======================================================> ] 80% Time: 00:00 ETA: --:-- [==============================================================> ] 90% Time: 00:00 ETA: --:-- [=====================================================================>] 100% Time: 00:00 ETA: --:-- [=====================================================================>] 100% Time: 00:00 1000 trees built within 0.317 s.
Prediction Summary on a Random Subsample (st): Accuracy = 82.05% WeightedAccuracy = 71.88%
Cutoff RI (based on linear regression angle) = 0.0249342 Cutoff RI (based on k-means clustering) = 0.4032865 Cutoff RI (based on mean cutoff value) = 0.0249342 Important attributes (based on mean cutoff value) = 6 Calculation of cutoff RI (based on permutations) Max RI (raw data) = 0.75569206 Max RI (after permutations) = [0.026800117, 0.025955085, 0.03071283] Anderson-Darling normality test p-value = 0.2157031 Confidence Interval: 0.0215170 ; 0.0341283 Cutoff RI (based on permutations) = 0.0341283 Important attributes (based on permutations) = 6 Calculation of cutoff ID Anderson-Darling normality test p-value = 0.2467775 Confidence Interval: 5.9950792 ; 7.6353525 Cutoff ID (based on permutations) = 7.6353525 Final Important attributes (based on permutations) = 6 MCFS-ID Processing is done. Time: 1.6 s. ***
Reading results... Done. ##### MCFS-ID result (s = auto, t = 5, m = auto) ##### Target feature: 'class'
Top 6 features: position attribute RI 1 A2 0.7556921 2 A1 0.6967780 3 B2 0.4196845 4 B1 0.4032865 5 C1 0.2791012 6 C2 0.2322030
################################# Cutoff values: method minRI size minID criticalAngle 0.02493421 7 NA kmeans 0.40328652 4 NA permutations 0.03412831 6 7.635353 mean 0.02493421 6 NA
################################# Confusion matrix obtained on randomly selected (st) datasets: Confusion Matrix:
A B CA 13578 362 200 B 1364 5389 317 C 1403 884 1753
TPR (sensitivity/recall):
TPR1 96 % 2 76.2 % 3 43.4 %
Accuracy: 82.1 % wAccuracy: 71.9 %
################################# MCFS-ID execution time: 2 secs method minRI size minID 1 criticalAngle 0.02493421 7 NA 2 kmeans 0.40328652 4 NA 3 permutations 0.03412831 6 7.635353 4 mean 0.02493421 6 NA [1] 6 projection distance commonPart mAvg beta1 1 30 0.625 1 0 0 2 40 0.750 1 0 0 3 50 0.500 1 0 0 4 60 0.750 1 0 0 5 70 0.750 1 0 0 6 80 0.375 1 0 0 7 90 0.125 1 0 0 8 100 0.375 1 0 0 9 110 0.125 1 0 0 10 120 0.000 1 0 0 11 130 0.250 1 0 0 12 140 0.250 1 0 0 13 150 0.125 1 0 0 14 160 0.250 1 0 0 15 170 0.000 1 0 0 16 180 0.000 1 0 0 17 190 0.125 1 0 0 18 200 0.250 1 0 0 position attribute projections classifiers nodes RI 12 1 A2 62 0.91290325 0.9129033 0.755692060 11 2 A1 48 0.84166664 0.8416666 0.696778000 14 3 B2 59 0.86101690 0.8610169 0.419684530 13 4 B1 57 0.88771930 0.8877193 0.403286520 15 5 C1 53 0.86037740 0.8603774 0.279101220 16 6 C2 40 0.75500000 0.7550000 0.232203050 2 7 X2 48 0.38333333 0.6000000 0.024934206 8 8 X8 46 0.35652173 0.5478261 0.024855249 3 9 X3 40 0.21000000 0.4150000 0.012116533 7 10 X7 47 0.21702127 0.2851064 0.010706335 10 11 X10 58 0.21034482 0.3793103 0.010647866 5 12 X5 57 0.21052632 0.2842105 0.009043122 9 13 X9 35 0.15428571 0.2571429 0.007862807 1 14 X1 57 0.18596491 0.2701754 0.007755438 6 15 X6 54 0.18888889 0.2666667 0.006814953 4 16 X4 47 0.09361702 0.1276596 0.003441651 position edge_a edge_b weight 1 1 A2 B1 25.3522010 2 2 B1 C2 14.5552830 3 3 B1 C1 13.6161410 4 4 A1 B2 11.5978510 5 5 A1 C2 10.8978290 6 6 A2 C1 9.9805440 7 7 A2 B2 9.0048310 8 8 B2 C1 8.5823240 9 9 A1 B1 7.3013086 10 10 A1 C1 7.1974344 11 11 B2 C2 6.4786950 12 12 C1 X8 5.7917280 13 13 A2 C2 5.4840190 14 14 B1 X2 3.3558383 15 15 B2 X2 3.1769930 16 16 B2 A2 2.8444445 17 17 X3 X5 2.2166020 18 18 C2 X8 2.0978600 19 19 A2 X2 1.9983566 20 20 B2 X10 1.7701454 21 21 X2 X7 1.7524443 22 22 B2 X1 1.6774335 23 23 X8 X1 1.6554034 24 24 B1 X7 1.6185875 25 25 X10 X7 1.6069815 26 26 X10 X6 1.5277632 27 27 X1 X10 1.5185722 28 28 X8 X10 1.4838791 29 29 X2 X5 1.4619606 30 30 X10 X5 1.4578505 31 31 X5 X3 1.4528583 32 32 B1 X1 1.4239199 33 33 X5 X7 1.3917996 34 34 X2 X9 1.3689568 35 35 C1 X2 1.3621721 36 36 X2 X3 1.3203249 37 37 X6 X3 1.3187371 38 38 X7 X10 1.2841835 39 39 X10 X9 1.2303705 40 40 X10 X3 1.2095273 41 41 X1 X7 1.1508476 42 42 X3 X6 1.0868700 43 43 B1 X3 1.0831480 44 44 B2 X5 1.0708183 45 45 X2 X4 1.0487859 46 46 C1 X3 1.0222663 47 47 X1 X9 0.9942785 48 48 C1 X7 0.9647899 49 49 X7 X1 0.9580313 50 50 X9 X5 0.9557424 Selected 6 nodes and 17 edges. X1 X2 X3 X4 X5 X6 X7 1 0.98902563 0.23116906 0.78483548 0.821803022 0.71088721 0.58821497 0.7953894 2 0.61944825 0.35058203 0.15437349 0.901695312 0.94776216 0.16505500 0.2390703 3 0.23737288 0.45833595 0.01229192 0.982507341 0.98118909 0.04182482 0.1220222 4 0.36166464 0.07219098 0.08223544 0.265951080 0.05589952 0.46236715 0.3614041 5 0.74262232 0.24336907 0.51909300 0.787098370 0.24540161 0.16827440 0.9249142 6 0.19731734 0.51825450 0.06829250 0.190747030 0.27093381 0.28979740 0.9916906 7 0.02621423 0.50125444 0.81059233 0.003749102 0.90914956 0.33389940 0.5886450 8 0.07198397 0.67452979 0.07985544 0.556995859 0.37703354 0.40293417 0.7091866 9 0.58183258 0.19954013 0.15471898 0.409749276 0.27505538 0.58770520 0.9662458 10 0.60420706 0.52295278 0.06729615 0.922948800 0.17823044 0.25630289 0.8941283 X8 X9 X10 1 0.26332041 0.65081538 0.67427900 2 0.38240303 0.90531400 0.33779017 3 0.54840561 0.74012889 0.93212196 4 0.48009363 0.84171712 0.48498619 5 0.17678015 0.04423991 0.48679400 6 0.19423089 0.42327280 0.88244506 7 0.19061488 0.87393955 0.48127444 8 0.30924269 0.17536434 0.45792697 9 0.39395822 0.34876159 0.02776961 10 0.08136956 0.62184478 0.58021427
X7 X8 X9 X10 A1 A2 B1 B2 C1 C2 class60 0.4571018 0.65831274 0.9851356 0.097424099 0 0 B B 0 0 B 61 0.3793650 0.82178514 0.6029888 0.008888587 0 0 0 0 C C C 62 0.3447140 0.77345445 0.9146233 0.335543330 0 0 0 0 0 0 C 63 0.2475398 0.03647023 0.2361436 0.951405642 0 0 0 0 C C C 64 0.9009008 0.04317233 0.3525739 0.344889228 0 0 0 0 0 0 C 65 0.6683702 0.05624382 0.9630998 0.969437987 0 0 0 0 C C C 66 0.4350037 0.57622864 0.2180650 0.523872372 0 0 0 0 C C C 67 0.5990060 0.48291037 0.8727669 0.013034772 0 0 0 0 C C C 68 0.8610901 0.12267192 0.8969003 0.751962328 0 0 0 0 0 0 C 69 0.9639909 0.39747332 0.3908880 0.088308024 0 0 0 0 0 0 C 70 0.7672535 0.26049222 0.6209638 0.941725152 0 0 0 0 C C C class: 'data.frame' size: 70 x 17Checking input data... Exporting params... Exporting input data... Running MCFS-ID... ################################################## ##### dmLab 2.3.4 [2021.07.08] ##### ################################################## Created by Michal Draminski [michal.draminski@ipipan.waw.pl] http://www.ipipan.eu/staff/m.draminski/ Polish Academy of Sciences - Institute of Computer Science ##################################################
Warning! Value of cutoffPermutations = 0 and cutoffMethod = 'permutations'. Using cutoffMethod = 'mean'.
MCFS-ID Experiment **** Loading header: 'input.adh'... Loading data: 'input.csv'... 70 objects and 17 attributes to load... Done Nominal target detected - using J48 model MCFS-ID param: ID-Graph is ON MCFS-ID param: balance classes is AUTO Classes = ["A", "B", "C"], Sizes = [40, 20, 10], classSizeRatio = 0.25, balanceValue = 1.0 Calculation of DecisionValuesTable... Starting MCFS-ID Procedure: projectionSize(m) = 4, projections(s) = 200, splits(t) = 5 Start time: Thu Jul 18 09:11:01 UTC 2024 Running: 2 threads... [ ] 0% Time: 00:00 ETA: --:-- [======> ] 10% Time: 00:00 ETA: --:-- [=============> ] 20% Time: 00:00 ETA: --:-- [====================> ] 30% Time: 00:00 ETA: --:-- [===========================> ] 40% Time: 00:00 ETA: --:-- [==================================> ] 50% Time: 00:00 ETA: --:-- [=========================================> ] 60% Time: 00:00 ETA: --:-- [================================================> ] 70% Time: 00:00 ETA: --:-- [=======================================================> ] 80% Time: 00:00 ETA: --:-- [==============================================================> ] 90% Time: 00:00 ETA: --:-- [=====================================================================>] 100% Time: 00:00 ETA: --:-- [=====================================================================>] 100% Time: 00:00 1000 trees built within 0.28 s.
Prediction Summary on a Random Subsample (st): Accuracy = 78.90% WeightedAccuracy = 68.28%
Cutoff RI (based on linear regression angle) = 0.0200903 Cutoff RI (based on k-means clustering) = 0.4499447 Cutoff RI (based on mean cutoff value) = 0.0200903 Important attributes (based on mean cutoff value) = 6 MCFS-ID Processing is done. Time: 0.287 s.
Reading results... Done. Selected 6 nodes and 12 edges. X1 X2 X3 X4 X5 X6 X7 1 0.3390729 0.6827881 0.9614099 0.02778712 0.48614910 0.43471764 0.02274122 2 0.8394404 0.6015412 0.1001408 0.52731078 0.06380247 0.51473265 0.93913671 3 0.3466835 0.2388687 0.7632227 0.88031907 0.78454623 0.66301097 0.29294872 4 0.3337749 0.2581659 0.9479664 0.37306337 0.41832164 0.14316659 0.16432657 5 0.4763512 0.7293096 0.8186347 0.04795913 0.98101808 0.34448739 0.39910256 6 0.8921983 0.4525708 0.3082923 0.13862825 0.28288396 0.40576358 0.45957541 7 0.8643395 0.1751268 0.6495795 0.32149212 0.84788215 0.08531101 0.43403085 8 0.3899895 0.7466983 0.9533555 0.15483161 0.08223923 0.93257193 0.51700983 9 0.7773207 0.1049876 0.9537327 0.13222817 0.88645875 0.83838407 0.84624575 10 0.9606180 0.8645449 0.3399792 0.22130593 0.47193073 0.87943330 0.05516429 X8 X9 X10 1 0.6547329 0.85750154 0.92974321 2 0.1328278 0.37088354 0.90093927 3 0.3418099 0.31420183 0.75088219 4 0.7313716 0.82853436 0.67656877 5 0.9072914 0.45184151 0.64801345 6 0.6961970 0.31587841 0.07324687 7 0.2415792 0.09780854 0.42355842 8 0.6441072 0.06490054 0.53082436 9 0.2807502 0.68945737 0.94270476 10 0.9576365 0.66805060 0.71222456
X97 X98 X99 X100 A1 A2 B1 B2 C1 C2 class60 0.22378404 0.51307358 0.3994570 0.02202729 0 0 B B 0 0 B 61 0.69587201 0.77525486 0.9689147 0.55346841 0 0 0 0 C C C 62 0.14077874 0.86028153 0.9072225 0.07312673 0 0 0 0 0 0 C 63 0.03859708 0.15871952 0.8472281 0.86110955 0 0 0 0 C C C 64 0.63851974 0.05270203 0.2219598 0.63500276 0 0 0 0 0 0 C 65 0.25135768 0.17265011 0.4159029 0.90253739 0 0 0 0 C C C 66 0.33987375 0.18344931 0.9690545 0.04770292 0 0 0 0 0 0 C 67 0.60909188 0.48718376 0.5835705 0.90106259 0 0 0 0 C C C 68 0.55731016 0.83232775 0.4722076 0.51572658 0 0 0 0 C C C 69 0.66711758 0.06494922 0.6722287 0.36741652 0 0 0 0 0 0 C 70 0.31903838 0.79192239 0.3533426 0.35998763 0 0 0 0 C C C class: 'data.frame' size: 70 x 107Checking input data... Exporting params... Exporting input data... Running MCFS-ID... ################################################## ##### dmLab 2.3.4 [2021.07.08] ##### ################################################## Created by Michal Draminski [michal.draminski@ipipan.waw.pl] http://www.ipipan.eu/staff/m.draminski/ Polish Academy of Sciences - Institute of Computer Science ##################################################
Warning! Value of cutoffPermutations = 0 and cutoffMethod = 'permutations'. Using cutoffMethod = 'mean'.
MCFS-ID Experiment **** Loading header: 'input.adh'... Loading data: 'input.csv'... 70 objects and 107 attributes to load... Done Numeric target detected - using M5 model MCFS-ID param: ID-Graph is ON MCFS-ID param: finalCV is ON Starting MCFS-ID Procedure: projectionSize(m) = 10, projections(s) = 212, splits(t) = 5 Start time: Thu Jul 18 09:11:02 UTC 2024 Running: 1 threads... [ ] 0% Time: 00:00 ETA: --:-- [=====> ] 9% Time: 00:00 ETA: --:-- [============> ] 19% Time: 00:01 ETA: --:-- [======================> ] 33% Time: 00:01 ETA: --:-- [================================> ] 47% Time: 00:02 ETA: 00:04 [======================================> ] 57% Time: 00:02 ETA: 00:02 [================================================> ] 71% Time: 00:02 ETA: 00:02 [==========================================================> ] 85% Time: 00:03 ETA: 00:03 [====================================================================> ] 99% Time: 00:03 ETA: 00:03 [=====================================================================>] 100% Time: 00:03 1060 trees built within 3.6 s.
Prediction Summary on a Random Subsample (st): pearson: mean = 0.19714685679046492 median = 0.0 stdev = 0.3785735601222462 MAE: mean = 0.5822268205674695 median = 0.6394927501678467 stdev = 0.16084495727069972 RMSE: mean = 0.6723319524165355 median = 0.707329511642456 stdev = 0.1559868227285496 SMAPE: mean = 0.18326054566350333 median = 0.20413188636302948 stdev = 0.049676804451907905
Cutoff RI (based on linear regression angle) = 0.0061645 Cutoff RI (based on k-means clustering) = 0.4609433 Cutoff RI (based on mean cutoff value) = 0.0082593 Important attributes (based on mean cutoff value) = 9
Running CV experiment on input data limited to the top [2, 5, 7, 9, 11, 14, 18] attributes Evaluating model performance using 10 fold CV. Model: m5 Evaluating model performance using 10 fold CV. Model: m5 Evaluating model performance using 10 fold CV. Model: m5 Evaluating model performance using 10 fold CV. Model: m5 Evaluating model performance using 10 fold CV. Model: m5 Evaluating model performance using 10 fold CV. Model: m5 Evaluating model performance using 10 fold CV. Model: m5 MCFS-ID Processing is done. Time: 3.9 s.
Reading results... Done. X1 X2 X3 X4 X5 X6 X7 1 0.3390729 0.6827881 0.9614099 0.02778712 0.48614910 0.43471764 0.02274122 2 0.8394404 0.6015412 0.1001408 0.52731078 0.06380247 0.51473265 0.93913671 3 0.3466835 0.2388687 0.7632227 0.88031907 0.78454623 0.66301097 0.29294872 4 0.3337749 0.2581659 0.9479664 0.37306337 0.41832164 0.14316659 0.16432657 5 0.4763512 0.7293096 0.8186347 0.04795913 0.98101808 0.34448739 0.39910256 6 0.8921983 0.4525708 0.3082923 0.13862825 0.28288396 0.40576358 0.45957541 7 0.8643395 0.1751268 0.6495795 0.32149212 0.84788215 0.08531101 0.43403085 8 0.3899895 0.7466983 0.9533555 0.15483161 0.08223923 0.93257193 0.51700983 9 0.7773207 0.1049876 0.9537327 0.13222817 0.88645875 0.83838407 0.84624575 10 0.9606180 0.8645449 0.3399792 0.22130593 0.47193073 0.87943330 0.05516429 X8 X9 X10 1 0.6547329 0.85750154 0.92974321 2 0.1328278 0.37088354 0.90093927 3 0.3418099 0.31420183 0.75088219 4 0.7313716 0.82853436 0.67656877 5 0.9072914 0.45184151 0.64801345 6 0.6961970 0.31587841 0.07324687 7 0.2415792 0.09780854 0.42355842 8 0.6441072 0.06490054 0.53082436 9 0.2807502 0.68945737 0.94270476 10 0.9576365 0.66805060 0.71222456
X7 X8 X9 X10 A1 A2 B1 B2 C1 C2 class60 0.73271802 0.05737268 0.91933177 0.4484275 0 0 B B 0 0 B 61 0.87080555 0.85166764 0.07943969 0.7771450 0 0 0 0 C C C 62 0.57217026 0.21264535 0.50737425 0.1582198 0 0 0 0 0 0 C 63 0.01103607 0.53946203 0.82017162 0.8668086 0 0 0 0 C C C 64 0.90631526 0.13648759 0.59839542 0.2061456 0 0 0 0 0 0 C 65 0.77065363 0.32486514 0.42415353 0.1779497 0 0 0 0 C C C 66 0.38250462 0.62107629 0.55931027 0.1648911 0 0 0 0 0 0 C 67 0.09404589 0.25598225 0.78909447 0.5652690 0 0 0 0 C C C 68 0.04965358 0.63487580 0.16771526 0.7271810 0 0 0 0 C C C 69 0.82116232 0.48567211 0.97045173 0.8759190 0 0 0 0 0 0 C 70 0.82932430 0.93817692 0.47350310 0.7084244 0 0 0 0 C C C class: 'data.frame' size: 70 x 17Checking input data... Exporting params... Exporting input data... Running MCFS-ID... ################################################## ##### dmLab 2.3.4 [2021.07.08] ##### ################################################## Created by Michal Draminski [michal.draminski@ipipan.waw.pl] http://www.ipipan.eu/staff/m.draminski/ Polish Academy of Sciences - Institute of Computer Science ##################################################
MCFS-ID Cutoff Permutation Experiment #1/3 ********* Loading header: 'input.adh'... Loading data: 'input.csv'... 70 objects and 17 attributes to load... Done Pearson's correlation of shuffled decision: -0.0399 Nominal target detected - using J48 model MCFS-ID param: ID-Graph is ON MCFS-ID param: balance classes is AUTO Classes = ["A", "B", "C"], Sizes = [40, 20, 10], classSizeRatio = 0.25, balanceValue = 1.0 Calculation of DecisionValuesTable... Starting MCFS-ID Procedure: projectionSize(m) = 4, projections(s) = 200, splits(t) = 5 Start time: Thu Jul 18 09:11:06 UTC 2024 Running: 1 threads... [ ] 0% Time: 00:00 ETA: --:-- [======> ] 10% Time: 00:00 ETA: --:-- [=============> ] 20% Time: 00:00 ETA: --:-- [====================> ] 30% Time: 00:00 ETA: --:-- [===========================> ] 40% Time: 00:00 ETA: --:-- [==================================> ] 50% Time: 00:00 ETA: --:-- [=========================================> ] 60% Time: 00:00 ETA: --:-- [================================================> ] 70% Time: 00:00 ETA: --:-- [=======================================================> ] 80% Time: 00:00 ETA: --:-- [==============================================================> ] 90% Time: 00:00 ETA: --:-- [=====================================================================>] 100% Time: 00:00 ETA: --:-- [=====================================================================>] 100% Time: 00:00 1000 trees built within 0.829 s.
Prediction Summary on a Random Subsample (st): Accuracy = 50.94% WeightedAccuracy = 35.38%
Cutoff RI (based on linear regression angle) = 0.0177116 Cutoff RI (based on k-means clustering) = 0.0365365 Cutoff RI (based on mean cutoff value) = 0.0211711 Important attributes (based on mean cutoff value) = 5
MCFS-ID Cutoff Permutation Experiment #2/3 ********* Loading header: 'input.adh'... Loading data: 'input.csv'... 70 objects and 17 attributes to load... Done Pearson's correlation of shuffled decision: 0.2599 Nominal target detected - using J48 model MCFS-ID param: ID-Graph is ON MCFS-ID param: balance classes is AUTO Classes = ["A", "B", "C"], Sizes = [40, 20, 10], classSizeRatio = 0.25, balanceValue = 1.0 Calculation of DecisionValuesTable... Starting MCFS-ID Procedure: projectionSize(m) = 4, projections(s) = 200, splits(t) = 5 Start time: Thu Jul 18 09:11:06 UTC 2024 Running: 1 threads... [ ] 0% Time: 00:00 ETA: --:-- [======> ] 10% Time: 00:00 ETA: --:-- [=============> ] 20% Time: 00:00 ETA: --:-- [====================> ] 30% Time: 00:00 ETA: --:-- [===========================> ] 40% Time: 00:00 ETA: --:-- [==================================> ] 50% Time: 00:00 ETA: --:-- [=========================================> ] 60% Time: 00:00 ETA: --:-- [================================================> ] 70% Time: 00:00 ETA: --:-- [=======================================================> ] 80% Time: 00:00 ETA: --:-- [==============================================================> ] 90% Time: 00:00 ETA: --:-- [=====================================================================>] 100% Time: 00:00 ETA: --:-- [=====================================================================>] 100% Time: 00:00 1000 trees built within 0.384 s.
Prediction Summary on a Random Subsample (st): Accuracy = 51.68% WeightedAccuracy = 35.42%
Cutoff RI (based on linear regression angle) = 0.0213335 Cutoff RI (based on k-means clustering) = 0.0320616 Cutoff RI (based on mean cutoff value) = 0.0213335 Important attributes (based on mean cutoff value) = 6
MCFS-ID Cutoff Permutation Experiment #3/3 ********* Loading header: 'input.adh'... Loading data: 'input.csv'... 70 objects and 17 attributes to load... Done Pearson's correlation of shuffled decision: 0.0 Nominal target detected - using J48 model MCFS-ID param: ID-Graph is ON MCFS-ID param: balance classes is AUTO Classes = ["A", "B", "C"], Sizes = [40, 20, 10], classSizeRatio = 0.25, balanceValue = 1.0 Calculation of DecisionValuesTable... Starting MCFS-ID Procedure: projectionSize(m) = 4, projections(s) = 200, splits(t) = 5 Start time: Thu Jul 18 09:11:07 UTC 2024 Running: 1 threads... [ ] 0% Time: 00:00 ETA: --:-- [======> ] 10% Time: 00:00 ETA: --:-- [=============> ] 20% Time: 00:00 ETA: --:-- [====================> ] 30% Time: 00:00 ETA: --:-- [===========================> ] 40% Time: 00:00 ETA: --:-- [==================================> ] 50% Time: 00:00 ETA: --:-- [=========================================> ] 60% Time: 00:00 ETA: --:-- [================================================> ] 70% Time: 00:00 ETA: --:-- [=======================================================> ] 80% Time: 00:00 ETA: --:-- [==============================================================> ] 90% Time: 00:00 ETA: --:-- [=====================================================================>] 100% Time: 00:00 ETA: --:-- [=====================================================================>] 100% Time: 00:00 1000 trees built within 0.344 s.
Prediction Summary on a Random Subsample (st): Accuracy = 50.35% WeightedAccuracy = 34.41%
Cutoff RI (based on linear regression angle) = 0.0273366 Cutoff RI (based on k-means clustering) = 0.0199981 Cutoff RI (based on mean cutoff value) = 0.0165373 Important attributes (based on mean cutoff value) = 6
MCFS-ID Experiment **** Loading header: 'input.adh'... Loading data: 'input.csv'... 70 objects and 17 attributes to load... Done Nominal target detected - using J48 model MCFS-ID param: ID-Graph is ON MCFS-ID param: finalCV is ON MCFS-ID param: balance classes is AUTO Classes = ["A", "B", "C"], Sizes = [40, 20, 10], classSizeRatio = 0.25, balanceValue = 1.0 Calculation of DecisionValuesTable... Starting MCFS-ID Procedure: projectionSize(m) = 4, projections(s) = 200, splits(t) = 5 Start time: Thu Jul 18 09:11:07 UTC 2024 Running: 1 threads... [ ] 0% Time: 00:00 ETA: --:-- [======> ] 10% Time: 00:00 ETA: --:-- [=============> ] 20% Time: 00:00 ETA: --:-- [====================> ] 30% Time: 00:00 ETA: --:-- [===========================> ] 40% Time: 00:00 ETA: --:-- [==================================> ] 50% Time: 00:00 ETA: --:-- [=========================================> ] 60% Time: 00:00 ETA: --:-- [================================================> ] 70% Time: 00:00 ETA: --:-- [=======================================================> ] 80% Time: 00:00 ETA: --:-- [==============================================================> ] 90% Time: 00:00 ETA: --:-- [=====================================================================>] 100% Time: 00:00 ETA: --:-- [=====================================================================>] 100% Time: 00:00 1000 trees built within 0.247 s.
Prediction Summary on a Random Subsample (st): Accuracy = 79.01% WeightedAccuracy = 69.00%
Cutoff RI (based on linear regression angle) = 0.0181059 Cutoff RI (based on k-means clustering) = 0.4120377 Cutoff RI (based on mean cutoff value) = 0.0181059 Important attributes (based on mean cutoff value) = 6 Calculation of cutoff RI (based on permutations) Max RI (raw data) = 0.72376907 Max RI (after permutations) = [0.055015575, 0.05414359, 0.035604194] Anderson-Darling normality test p-value = 0.0813281 Confidence Interval: 0.0210180 ; 0.0754908 Cutoff RI (based on permutations) = 0.0754908 Important attributes (based on permutations) = 6 Calculation of cutoff ID Anderson-Darling normality test p-value = 0.6287300 Confidence Interval: 9.7308185 ; 12.8343097 Cutoff ID (based on permutations) = 12.8343097 *** Final Important attributes (based on permutations) = 6
Running CV experiment on input data limited to the top [2, 3, 5, 6, 8, 9, 12] attributes Evaluating model performance using 10 fold CV. Model: j48, rf, nb, svm, knn, logistic, ripper Evaluating model performance using 10 fold CV. Model: j48, rf, nb, svm, knn, logistic, ripper Evaluating model performance using 10 fold CV. Model: j48, rf, nb, svm, knn, logistic, ripper Evaluating model performance using 10 fold CV. Model: j48, rf, nb, svm, knn, logistic, ripper Evaluating model performance using 10 fold CV. Model: j48, rf, nb, svm, knn, logistic, ripper Evaluating model performance using 10 fold CV. Model: j48, rf, nb, svm, knn, logistic, ripper Evaluating model performance using 10 fold CV. Model: j48, rf, nb, svm, knn, logistic, ripper MCFS-ID Processing is done. Time: 6.6 s.
Reading results... Done. Checking input data... Error: The names of the following attributes: MCFS,contrast_1attr_abds, MCFS'contrast_2attr_abds, MCFS#contrast_3attr_abds, MCFS()contrast_attr_abds, MCFS[]contrast_attr_abds, MCFS{}contrast_attr_abds, MCFS{}'#contrast,attr.abds contain forbidden characters. Please run fix.data() function before running mcfs(). Fixing names... Fixing values... Fixing types... Checking input data... Exporting params... Exporting input data... Running MCFS-ID... ################################################## ##### dmLab 2.3.4 [2021.07.08] ##### ################################################## Created by Michal Draminski [michal.draminski@ipipan.waw.pl] http://www.ipipan.eu/staff/m.draminski/ Polish Academy of Sciences - Institute of Computer Science ##################################################
MCFS-ID Cutoff Permutation Experiment #1/3 ********* Loading header: 'input.adh'... Loading data: 'input.csv'... 70 objects and 1007 attributes to load... Done Pearson's correlation of shuffled decision: 0.0799 Nominal target detected - using J48 model MCFS-ID param: ID-Graph is ON MCFS-ID param: balance classes is AUTO Classes = ["A", "B", "C"], Sizes = [40, 20, 10], classSizeRatio = 0.25, balanceValue = 1.0 Calculation of DecisionValuesTable... Starting MCFS-ID Procedure: projectionSize(m) = 32, projections(s) = 314, splits(t) = 5 Start time: Thu Jul 18 09:11:13 UTC 2024 Running: 6 threads... [ ] 0% Time: 00:00 ETA: --:-- [===> ] 6% Time: 00:00 ETA: --:-- [==========> ] 16% Time: 00:00 ETA: --:-- [===================> ] 29% Time: 00:01 ETA: --:-- [===========================> ] 41% Time: 00:01 ETA: --:-- [==================================> ] 51% Time: 00:01 ETA: --:-- [=========================================> ] 61% Time: 00:01 ETA: --:-- [==================================================> ] 73% Time: 00:02 ETA: 00:02 [========================================================> ] 83% Time: 00:02 ETA: 00:02 [=================================================================> ] 96% Time: 00:02 ETA: 00:02 [=====================================================================>] 100% Time: 00:03 1570 trees built within 3.0 s.
Prediction Summary on a Random Subsample (st): Accuracy = 42.61% WeightedAccuracy = 33.71%
Cutoff RI (based on linear regression angle) = 0.0327596 Cutoff RI (based on k-means clustering) = 0.0167396 Cutoff RI (based on mean cutoff value) = 0.0214019 Important attributes (based on mean cutoff value) = 50
MCFS-ID Cutoff Permutation Experiment #2/3 ********* Loading header: 'input.adh'... Loading data: 'input.csv'... 70 objects and 1007 attributes to load... Done Pearson's correlation of shuffled decision: 0.2199 Nominal target detected - using J48 model MCFS-ID param: ID-Graph is ON MCFS-ID param: balance classes is AUTO Classes = ["A", "B", "C"], Sizes = [40, 20, 10], classSizeRatio = 0.25, balanceValue = 1.0 Calculation of DecisionValuesTable... Starting MCFS-ID Procedure: projectionSize(m) = 32, projections(s) = 314, splits(t) = 5 Start time: Thu Jul 18 09:11:16 UTC 2024 Running: 6 threads... [ ] 0% Time: 00:00 ETA: --:-- [===> ] 6% Time: 00:00 ETA: --:-- [==========> ] 16% Time: 00:00 ETA: --:-- [===================> ] 29% Time: 00:00 ETA: --:-- [===========================> ] 41% Time: 00:01 ETA: --:-- [==================================> ] 51% Time: 00:01 ETA: --:-- [=========================================> ] 61% Time: 00:01 ETA: --:-- [==================================================> ] 73% Time: 00:01 ETA: --:-- [========================================================> ] 83% Time: 00:01 ETA: --:-- [=================================================================> ] 96% Time: 00:02 ETA: 00:02 [=====================================================================>] 100% Time: 00:02 1570 trees built within 2.3 s.
Prediction Summary on a Random Subsample (st): Accuracy = 42.88% WeightedAccuracy = 33.73%
Cutoff RI (based on linear regression angle) = 0.0349667 Cutoff RI (based on k-means clustering) = 0.0140571 Cutoff RI (based on mean cutoff value) = 0.0185226 Important attributes (based on mean cutoff value) = 70
MCFS-ID Cutoff Permutation Experiment #3/3 ********* Loading header: 'input.adh'... Loading data: 'input.csv'... 70 objects and 1007 attributes to load... Done Pearson's correlation of shuffled decision: -0.1199 Nominal target detected - using J48 model MCFS-ID param: ID-Graph is ON MCFS-ID param: balance classes is AUTO Classes = ["A", "B", "C"], Sizes = [40, 20, 10], classSizeRatio = 0.25, balanceValue = 1.0 Calculation of DecisionValuesTable... Starting MCFS-ID Procedure: projectionSize(m) = 32, projections(s) = 314, splits(t) = 5 Start time: Thu Jul 18 09:11:18 UTC 2024 Running: 6 threads... [ ] 0% Time: 00:00 ETA: --:-- [===> ] 6% Time: 00:00 ETA: --:-- [==========> ] 16% Time: 00:00 ETA: --:-- [===================> ] 29% Time: 00:00 ETA: --:-- [===========================> ] 41% Time: 00:00 ETA: --:-- [==================================> ] 51% Time: 00:01 ETA: --:-- [=========================================> ] 61% Time: 00:01 ETA: --:-- [==================================================> ] 73% Time: 00:01 ETA: --:-- [========================================================> ] 83% Time: 00:01 ETA: --:-- [=================================================================> ] 96% Time: 00:01 ETA: --:-- [=====================================================================>] 100% Time: 00:02 1570 trees built within 2.2 s.
Prediction Summary on a Random Subsample (st): Accuracy = 41.99% WeightedAccuracy = 33.01%
Cutoff RI (based on linear regression angle) = 0.0270371 Cutoff RI (based on k-means clustering) = 0.0130469 Cutoff RI (based on mean cutoff value) = 0.0169665 Important attributes (based on mean cutoff value) = 78
MCFS-ID Experiment **** Loading header: 'input.adh'... Loading data: 'input.csv'... 70 objects and 1007 attributes to load... Done Nominal target detected - using J48 model MCFS-ID param: ID-Graph is ON MCFS-ID param: balance classes is AUTO Classes = ["A", "B", "C"], Sizes = [40, 20, 10], classSizeRatio = 0.25, balanceValue = 1.0 Calculation of DecisionValuesTable... Starting MCFS-ID Procedure: projectionSize(m) = 32, projections(s) = 314, splits(t) = 5 Start time: Thu Jul 18 09:11:21 UTC 2024 Running: 6 threads... [ ] 0% Time: 00:00 ETA: --:-- [===> ] 6% Time: 00:00 ETA: --:-- [==========> ] 16% Time: 00:00 ETA: --:-- [===================> ] 29% Time: 00:00 ETA: --:-- [===========================> ] 41% Time: 00:00 ETA: --:-- [==================================> ] 51% Time: 00:00 ETA: --:-- [=========================================> ] 61% Time: 00:01 ETA: --:-- [==================================================> ] 73% Time: 00:01 ETA: --:-- [========================================================> ] 83% Time: 00:01 ETA: --:-- [=================================================================> ] 96% Time: 00:01 ETA: --:-- [=====================================================================>] 100% Time: 00:01 1570 trees built within 1.9 s.
Prediction Summary on a Random Subsample (st): Accuracy = 47.16% WeightedAccuracy = 38.15%
Cutoff RI (based on linear regression angle) = 0.0288587 Cutoff RI (based on k-means clustering) = 0.3288324 Cutoff RI (based on mean cutoff value) = 0.0389880 Important attributes (based on mean cutoff value) = 14 Calculation of cutoff RI (based on permutations) Max RI (raw data) = 0.6696969 Max RI (after permutations) = [0.06497993, 0.057736687, 0.059140034] Anderson-Darling normality test p-value = 0.2396656 Confidence Interval: 0.0510763 ; 0.0701614 Cutoff RI (based on permutations) = 0.0701614 Important attributes (based on permutations) = 6 Calculation of cutoff ID Anderson-Darling normality test p-value = 0.1113444 Confidence Interval: 0.2776522 ; 2.4266361 Cutoff ID (based on permutations) = 2.4266361 Final Important attributes (based on permutations) = 6 MCFS-ID Processing is done. Time: 10.1 s. ***
Reading results... Done. X1 X2 X3 X4 X5 X6 X7 1 0.3390729 0.6827881 0.9614099 0.02778712 0.48614910 0.43471764 0.02274122 2 0.8394404 0.6015412 0.1001408 0.52731078 0.06380247 0.51473265 0.93913671 3 0.3466835 0.2388687 0.7632227 0.88031907 0.78454623 0.66301097 0.29294872 4 0.3337749 0.2581659 0.9479664 0.37306337 0.41832164 0.14316659 0.16432657 5 0.4763512 0.7293096 0.8186347 0.04795913 0.98101808 0.34448739 0.39910256 6 0.8921983 0.4525708 0.3082923 0.13862825 0.28288396 0.40576358 0.45957541 7 0.8643395 0.1751268 0.6495795 0.32149212 0.84788215 0.08531101 0.43403085 8 0.3899895 0.7466983 0.9533555 0.15483161 0.08223923 0.93257193 0.51700983 9 0.7773207 0.1049876 0.9537327 0.13222817 0.88645875 0.83838407 0.84624575 10 0.9606180 0.8645449 0.3399792 0.22130593 0.47193073 0.87943330 0.05516429 X8 X9 X10 1 0.6547329 0.85750154 0.92974321 2 0.1328278 0.37088354 0.90093927 3 0.3418099 0.31420183 0.75088219 4 0.7313716 0.82853436 0.67656877 5 0.9072914 0.45184151 0.64801345 6 0.6961970 0.31587841 0.07324687 7 0.2415792 0.09780854 0.42355842 8 0.6441072 0.06490054 0.53082436 9 0.2807502 0.68945737 0.94270476 10 0.9576365 0.66805060 0.71222456
X97 X98 X99 X100 A1 A2 B1 B2 C1 C2 class60 0.22378404 0.51307358 0.3994570 0.02202729 0 0 B B 0 0 B 61 0.69587201 0.77525486 0.9689147 0.55346841 0 0 0 0 C C C 62 0.14077874 0.86028153 0.9072225 0.07312673 0 0 0 0 0 0 C 63 0.03859708 0.15871952 0.8472281 0.86110955 0 0 0 0 C C C 64 0.63851974 0.05270203 0.2219598 0.63500276 0 0 0 0 0 0 C 65 0.25135768 0.17265011 0.4159029 0.90253739 0 0 0 0 C C C 66 0.33987375 0.18344931 0.9690545 0.04770292 0 0 0 0 0 0 C 67 0.60909188 0.48718376 0.5835705 0.90106259 0 0 0 0 C C C 68 0.55731016 0.83232775 0.4722076 0.51572658 0 0 0 0 C C C 69 0.66711758 0.06494922 0.6722287 0.36741652 0 0 0 0 0 0 C 70 0.31903838 0.79192239 0.3533426 0.35998763 0 0 0 0 C C C class: 'data.frame' size: 70 x 107Checking input data... Exporting params... Exporting input data... Running MCFS-ID... ################################################## ##### dmLab 2.3.4 [2021.07.08] ##### ################################################## Created by Michal Draminski [michal.draminski@ipipan.waw.pl] http://www.ipipan.eu/staff/m.draminski/ Polish Academy of Sciences - Institute of Computer Science ##################################################
Running Phase I - Initial MCFS-ID filtering **** MCFS-ID Experiment **** Loading header: 'input.adh'... Loading data: 'input.csv'... 70 objects and 107 attributes to load... Done Nominal target detected - using J48 model MCFS-ID param: balance classes is AUTO Classes = ["A", "B", "C"], Sizes = [40, 20, 10], classSizeRatio = 0.25, balanceValue = 1.0 Adding Contrast Attributes... Data size: attributes: 118 objects: 70 Calculation of DecisionValuesTable... Starting MCFS-ID Procedure: projectionSize(m) = 10, projections(s) = 530, splits(t) = 5 Start time: Thu Jul 18 09:11:23 UTC 2024 Running: 1 threads... [ ] 0% Time: 00:00 ETA: --:-- [=> ] 4% Time: 00:00 ETA: --:-- [=========> ] 15% Time: 00:00 ETA: --:-- [================> ] 25% Time: 00:00 ETA: --:-- [========================> ] 36% Time: 00:00 ETA: --:-- [================================> ] 47% Time: 00:00 ETA: --:-- [======================================> ] 57% Time: 00:00 ETA: --:-- [==============================================> ] 68% Time: 00:00 ETA: --:-- [======================================================> ] 79% Time: 00:00 ETA: --:-- [=============================================================> ] 89% Time: 00:01 ETA: --:-- [=====================================================================>] 100% Time: 00:01 ETA: --:-- [=====================================================================>] 100% Time: 00:01 2650 trees built within 1.1 s.
Prediction Summary on a Random Subsample (st): Accuracy = 56.02% WeightedAccuracy = 45.49%
Cutoff RI (based on linear regression angle) = 0.0224505 Cutoff RI (based on k-means clustering) = 0.4918377 Cutoff RI (based on contrast attributes) = 0.0253349 Cutoff RI (based on mean cutoff value) = 0.0253349 Important attributes (based on mean cutoff value) = 13 MCFS-ID Processing is done. Time: 1.1 s.
Running Phase II - Final MCFS-ID filtering ******* MCFS-ID Cutoff Permutation Experiment #1/3 ******* Loading data: 'input.adx'... 70 objects and 15 attributes to load... Done Pearson's correlation of shuffled decision: -0.1307 Nominal target detected - using J48 model MCFS-ID param: ID-Graph is ON MCFS-ID param: balance classes is AUTO Classes = ["A", "B", "C"], Sizes = [40, 20, 10], classSizeRatio = 0.25, balanceValue = 1.0 Calculation of DecisionValuesTable... Starting MCFS-ID Procedure: projectionSize(m) = 4, projections(s) = 175, splits(t) = 5 Start time: Thu Jul 18 09:11:24 UTC 2024 Running: 1 threads... [ ] 0% Time: 00:00 ETA: --:-- [=======> ] 11% Time: 00:00 ETA: --:-- [===============> ] 23% Time: 00:00 ETA: --:-- [=======================> ] 34% Time: 00:00 ETA: --:-- [===============================> ] 46% Time: 00:00 ETA: --:-- [=======================================> ] 57% Time: 00:00 ETA: --:-- [===============================================> ] 69% Time: 00:00 ETA: --:-- [=======================================================> ] 80% Time: 00:00 ETA: --:-- [===============================================================> ] 91% Time: 00:00 ETA: --:-- [=====================================================================>] 100% Time: 00:00 875 trees built within 0.162 s.
Prediction Summary on a Random Subsample (st): Accuracy = 49.85% WeightedAccuracy = 32.44%
Cutoff RI (based on linear regression angle) = 0.0155160 Cutoff RI (based on k-means clustering) = 0.0155160 Cutoff RI (based on mean cutoff value) = 0.0116263 Important attributes (based on mean cutoff value) = 5
MCFS-ID Cutoff Permutation Experiment #2/3 ********* Loading data: 'input.adx'... 70 objects and 15 attributes to load... Done Pearson's correlation of shuffled decision: -0.0230 Nominal target detected - using J48 model MCFS-ID param: ID-Graph is ON MCFS-ID param: balance classes is AUTO Classes = ["A", "B", "C"], Sizes = [40, 20, 10], classSizeRatio = 0.25, balanceValue = 1.0 Calculation of DecisionValuesTable... Starting MCFS-ID Procedure: projectionSize(m) = 4, projections(s) = 175, splits(t) = 5 Start time: Thu Jul 18 09:11:24 UTC 2024 Running: 1 threads... [ ] 0% Time: 00:00 ETA: --:-- [=======> ] 11% Time: 00:00 ETA: --:-- [===============> ] 23% Time: 00:00 ETA: --:-- [=======================> ] 34% Time: 00:00 ETA: --:-- [===============================> ] 46% Time: 00:00 ETA: --:-- [=======================================> ] 57% Time: 00:00 ETA: --:-- [===============================================> ] 69% Time: 00:00 ETA: --:-- [=======================================================> ] 80% Time: 00:00 ETA: --:-- [===============================================================> ] 91% Time: 00:00 ETA: --:-- [=====================================================================>] 100% Time: 00:00 875 trees built within 0.239 s.
Prediction Summary on a Random Subsample (st): Accuracy = 50.57% WeightedAccuracy = 32.59%
Cutoff RI (based on linear regression angle) = 0.0152615 Cutoff RI (based on k-means clustering) = 0.0134873 Cutoff RI (based on mean cutoff value) = 0.0134873 Important attributes (based on mean cutoff value) = 7
MCFS-ID Cutoff Permutation Experiment #3/3 ********* Loading data: 'input.adx'... 70 objects and 15 attributes to load... Done Pearson's correlation of shuffled decision: 0.1923 Nominal target detected - using J48 model MCFS-ID param: ID-Graph is ON MCFS-ID param: balance classes is AUTO Classes = ["A", "B", "C"], Sizes = [40, 20, 10], classSizeRatio = 0.25, balanceValue = 1.0 Calculation of DecisionValuesTable... Starting MCFS-ID Procedure: projectionSize(m) = 4, projections(s) = 175, splits(t) = 5 Start time: Thu Jul 18 09:11:24 UTC 2024 Running: 1 threads... [ ] 0% Time: 00:00 ETA: --:-- [=======> ] 11% Time: 00:00 ETA: --:-- [===============> ] 23% Time: 00:00 ETA: --:-- [=======================> ] 34% Time: 00:00 ETA: --:-- [===============================> ] 46% Time: 00:00 ETA: --:-- [=======================================> ] 57% Time: 00:00 ETA: --:-- [===============================================> ] 69% Time: 00:00 ETA: --:-- [=======================================================> ] 80% Time: 00:00 ETA: --:-- [===============================================================> ] 91% Time: 00:00 ETA: --:-- [=====================================================================>] 100% Time: 00:00 875 trees built within 0.164 s.
Prediction Summary on a Random Subsample (st): Accuracy = 50.89% WeightedAccuracy = 32.12%
Cutoff RI (based on linear regression angle) = 0.0125553 Cutoff RI (based on k-means clustering) = 0.0086137 Cutoff RI (based on mean cutoff value) = 0.0098696 Important attributes (based on mean cutoff value) = 6
MCFS-ID Experiment **** Loading data: 'input.adx'... 70 objects and 15 attributes to load... Done Nominal target detected - using J48 model MCFS-ID param: ID-Graph is ON MCFS-ID param: finalCV is ON MCFS-ID param: balance classes is AUTO Classes = ["A", "B", "C"], Sizes = [40, 20, 10], classSizeRatio = 0.25, balanceValue = 1.0 Calculation of DecisionValuesTable... Starting MCFS-ID Procedure: projectionSize(m) = 4, projections(s) = 175, splits(t) = 5 Start time: Thu Jul 18 09:11:25 UTC 2024 Running: 1 threads... [ ] 0% Time: 00:00 ETA: --:-- [=======> ] 11% Time: 00:00 ETA: --:-- [===============> ] 23% Time: 00:00 ETA: --:-- [=======================> ] 34% Time: 00:00 ETA: --:-- [===============================> ] 46% Time: 00:00 ETA: --:-- [=======================================> ] 57% Time: 00:00 ETA: --:-- [===============================================> ] 69% Time: 00:00 ETA: --:-- [=======================================================> ] 80% Time: 00:00 ETA: --:-- [===============================================================> ] 91% Time: 00:00 ETA: --:-- [=====================================================================>] 100% Time: 00:00 875 trees built within 0.188 s.
Prediction Summary on a Random Subsample (st): Accuracy = 83.77% WeightedAccuracy = 76.18%
Cutoff RI (based on linear regression angle) = 0.2454500 Cutoff RI (based on k-means clustering) = 0.4110613 Cutoff RI (based on mean cutoff value) = 0.2454500 Important attributes (based on mean cutoff value) = 5 Calculation of cutoff RI (based on permutations) Max RI (raw data) = 0.7629547 Max RI (after permutations) = [0.023562135, 0.022775238, 0.013791701] Anderson-Darling normality test p-value = 0.1076272 Confidence Interval: 0.0065589 ; 0.0335271 Cutoff RI (based on permutations) = 0.0335271 Important attributes (based on permutations) = 7 Calculation of cutoff ID Anderson-Darling normality test p-value = 0.5356377 Confidence Interval: 2.6019662 ; 11.1905529 Cutoff ID (based on permutations) = 11.1905529 *** Final Important attributes (based on permutations) = 7
Running CV experiment on input data limited to the top [2, 4, 5, 7, 9, 11, 14] attributes Evaluating model performance using 10 fold CV. Model: j48, rf, nb, svm, knn, logistic, ripper Evaluating model performance using 10 fold CV. Model: j48, rf, nb, svm, knn, logistic, ripper Evaluating model performance using 10 fold CV. Model: j48, rf, nb, svm, knn, logistic, ripper Evaluating model performance using 10 fold CV. Model: j48, rf, nb, svm, knn, logistic, ripper Evaluating model performance using 10 fold CV. Model: j48, rf, nb, svm, knn, logistic, ripper Evaluating model performance using 10 fold CV. Model: j48, rf, nb, svm, knn, logistic, ripper Evaluating model performance using 10 fold CV. Model: j48, rf, nb, svm, knn, logistic, ripper MCFS-ID Processing is done. Time: 5.0 s.
Reading results... Done. [ FAIL 3 | WARN 6 | SKIP 0 | PASS 35 ]
══ Failed tests ════════════════════════════════════════════════════════════════ ── Error ('test-man.build.idgraph.R:21:3'): man build.idgraph ────────────────── Error:
from()was deprecated in igraph 2.0.4 and is now defunct. ℹ Please use.from()instead. Backtrace: ▆ 1. └─rmcfs::build.idgraph(result, size = 6, size_ID = 12, orphan_nodes = TRUE) at test-man.build.idgraph.R:21:3 2. └─base::sapply(m, function(x) length(igraph::E(g)[from(x)])) 3. └─base::lapply(X = X, FUN = FUN, ...) 4. └─rmcfs (local) FUN(X[[i]], ...) 5. ├─igraph::E(g)[from(x)] 6. └─igraph:::[.igraph.es(igraph::E(g), from(x)) 7. └─igraph:::lazy_eval(...) 8. └─base::lapply(x, lazy_eval, data = data) 9. └─igraph (local) FUN(X[[i]], ...) 10. └─base::eval(x$expr, data, x$env) 11. └─base::eval(x$expr, data, x$env) 12. └─igraph (local) from(x) 13. └─lifecycle::deprecate_stop("2.0.4", "from()", ".from()") 14. └─lifecycle:::deprecate_stop0(msg) 15. └─rlang::cnd_signal(...) ── Error ('test-man.mcfs.R:57:3'): man mcfs artificial ───────────────────────── Error:from()was deprecated in igraph 2.0.4 and is now defunct. ℹ Please use.from()instead. Backtrace: ▆ 1. └─rmcfs::build.idgraph(result) at test-man.mcfs.R:57:3 2. └─base::sapply(m, function(x) length(igraph::E(g)[from(x)])) 3. └─base::lapply(X = X, FUN = FUN, ...) 4. └─rmcfs (local) FUN(X[[i]], ...) 5. ├─igraph::E(g)[from(x)] 6. └─igraph:::[.igraph.es(igraph::E(g), from(x)) 7. └─igraph:::lazy_eval(...) 8. └─base::lapply(x, lazy_eval, data = data) 9. └─igraph (local) FUN(X[[i]], ...) 10. └─base::eval(x$expr, data, x$env) 11. └─base::eval(x$expr, data, x$env) 12. └─igraph (local) from(x) 13. └─lifecycle::deprecate_stop("2.0.4", "from()", ".from()") 14. └─lifecycle:::deprecate_stop0(msg) 15. └─rlang::cnd_signal(...) ── Error ('test-man.plot.idgraph.R:19:3'): man build.idgraph ─────────────────── Error:from()was deprecated in igraph 2.0.4 and is now defunct. ℹ Please use.from()instead. Backtrace: ▆ 1. └─rmcfs::build.idgraph(result, size = 6, size_ID = 12, orphan_nodes = TRUE) at test-man.plot.idgraph.R:19:3 2. └─base::sapply(m, function(x) length(igraph::E(g)[from(x)])) 3. └─base::lapply(X = X, FUN = FUN, ...) 4. └─rmcfs (local) FUN(X[[i]], ...) 5. ├─igraph::E(g)[from(x)] 6. └─igraph:::[.igraph.es(igraph::E(g), from(x)) 7. └─igraph:::lazy_eval(...) 8. └─base::lapply(x, lazy_eval, data = data) 9. └─igraph (local) FUN(X[[i]], ...) 10. └─base::eval(x$expr, data, x$env) 11. └─base::eval(x$expr, data, x$env) 12. └─igraph (local) from(x) 13. └─lifecycle::deprecate_stop("2.0.4", "from()", ".from()") 14. └─lifecycle:::deprecate_stop0(msg) 15. └─rlang::cnd_signal(...)[ FAIL 3 | WARN 6 | SKIP 0 | PASS 35 ] Error: Test failures Execution halted ```
In both
- checking Rd files ... NOTE
checkRd: (-1) plot.mcfs.Rd:24: Lost braces; missing escapes or markup? 24 | \item\code{ri} {plots top features set with their RIs as well as max RI obtained from permutation experiments. Red color denotes important features.} | ^
simcausal
- Version: 0.5.6
- GitHub: https://github.com/osofr/simcausal
- Source code: https://github.com/cran/simcausal
- Date/Publication: 2022-10-28 11:52:27 UTC
- Number of recursive dependencies: 83
Run revdepcheck::cloud_details(, "simcausal") for more info
Newly broken
-
checking tests ... ERROR ``` Running ‘test-all.R’ Running the tests in ‘tests/test-all.R’ failed. Complete output: > ## unit tests will not be done if RUnit is not available > # setwd("..") > # getwd() > # library(RUnit) > if(require("RUnit", quietly=TRUE)) {
-
--- Setup ---
- pkg <- "simcausal" # <-- Tested package name
- if(Sys.getenv("RCMDCHECK") == "FALSE") {
-
Path to unit tests for standalone running under Makefile (not R CMD check)
-
PKG/tests/../inst/unitTests
-
path <- file.path(getwd(), "..", "inst", "unitTests")
- } else {
-
Path to unit tests for R CMD check
-
PKG.Rcheck/tests/../PKG/unitTests
-
path <- system.file(package=pkg, "RUnit")
-
REPLACED WITH:
- path <- file.path(getwd(), "RUnit")
- }
- cat("\nRunning unit tests\n")
- print(list(pkg=pkg, getwd=getwd(), pathToUnitTests=path))
- library(package=pkg, character.only=TRUE)
-
If desired, load the name space to allow testing of private functions
-
if (is.element(pkg, loadedNamespaces()))
-
attach(loadNamespace(pkg), name=paste("namespace", pkg, sep=":"), pos=3)
-
or simply call PKG:::myPrivateFunction() in tests
-
--- Testing ---
-
Define tests
- test.suite <- defineTestSuite(name=paste(pkg, "unit testing"),
-
dirs="./RUnit",
- dirs=path,
- testFileRegexp = "^RUnit_tests_+",
- testFuncRegexp = "^test.+",
- rngKind = "Marsaglia-Multicarry",
- rngNormalKind = "Kinderman-Ramage")
-
Run
- tests <- runTestSuite(test.suite)
-
Default report name
- pathReport <- file.path(path, "report")
-
Report to stdout and text files
- cat("------------------- UNIT TEST SUMMARY ---------------------\n\n")
- printTextProtocol(tests, showDetails=FALSE)
- printTextProtocol(tests, showDetails=FALSE,
- fileName=paste0(pathReport, "Summary.txt"))
- printTextProtocol(tests, showDetails=TRUE,
- fileName=paste0(pathReport, ".txt"))
-
Report to HTML file
- printHTMLProtocol(tests, fileName=paste0(pathReport, ".html"))
-
Return stop() to cause R CMD check stop in case of
-
- failures i.e. FALSE to unit tests or
-
- errors i.e. R errors
- tmp <- getErrors(tests)
- if(tmp$nFail > 0 | tmp$nErr > 0) {
- stop(paste("\n\nunit testing failed (#test failures: ", tmp$nFail,
- ", #R errors: ", tmp$nErr, ")\n\n", sep=""))
- }
- } else {
- warning("cannot run unit tests -- package RUnit is not available")
- }
Running unit tests $pkg [1] "simcausal"
$getwd [1] "/tmp/workdir/simcausal/new/simcausal.Rcheck/tests"
$pathToUnitTests [1] "/tmp/workdir/simcausal/new/simcausal.Rcheck/tests/RUnit"
Executing test function test.EFUeval ... ...automatically assigning order attribute to some nodes... node C.time_0, order:1 node Y_0, order:2 node D_0, order:3 node C.time_1, order:4 node Y_1, order:5 node D_1, order:6 node C.time_2, order:7 node Y_2, order:8 node D_2, order:9 node C.time_3, order:10 node Y_3, order:11 node D_3, order:12 node C.time_4, order:13 node Y_4, order:14 node D_4, order:15 node C.time_5, order:16 node Y_5, order:17 node D_5, order:18 simulating observed dataset from the DAG object ...automatically assigning order attribute to some nodes... node C.time_0, order:1 node Y_0, order:2 node D_0, order:3 node C.time_1, order:4 node Y_1, order:5 node D_1, order:6 node C.time_2, order:7 node Y_2, order:8 node D_2, order:9 node C.time_3, order:10 node Y_3, order:11 node D_3, order:12 node C.time_4, order:13 node Y_4, order:14 node D_4, order:15 node C.time_5, order:16 node Y_5, order:17 node D_5, order:18 using the following vertex attributes: NAdarkbluenone70.50 using the following edge attributes: black0.210.30.2 simulating observed dataset from the DAG object simulating observed dataset from the DAG object done successfully.
Executing test function test.Nsamp.n.test ... ...automatically assigning order attribute to some nodes... node A, order:1 node N, order:2 simulating observed dataset from the DAG object ...automatically assigning order attribute to some nodes... node A, order:1 node N, order:2 simulating observed dataset from the DAG object ...automatically assigning order attribute to some nodes... node A, order:1 node N, order:2 done successfully.
Executing test function test.bugfixes ... ...automatically assigning order attribute to some nodes... node const, order:1 node W1, order:2 node W2, order:3 node W3, order:4 node A, order:5 node Y, order:6 simulating observed dataset from the DAG object ...automatically assigning order attribute to some nodes... node L2_0, order:1 node L1_0, order:2 node A1_0, order:3 node L2_1, order:5 node L2_2, order:6 node L2_3, order:7 node L2_4, order:8 node L2_5, order:9 node L2_6, order:10 node L2_7, order:11 node L2_8, order:12 node L2_9, order:13 node L2_10, order:14 node L2_11, order:15 node L2_12, order:16 node L2_13, order:17 node L2_14, order:18 node L2_15, order:19 node L2_16, order:20 ...automatically assigning order attribute to some nodes... node L2_0, order:1 node L1_0, order:2 node L2_1, order:3 node L1_1, order:4 node L2_2, order:5 node L1_2, order:6 node L2_3, order:7 node L1_3, order:8 node L2_4, order:9 node L1_4, order:10 node L2_5, order:11 node L1_5, order:12 node L2_6, order:13 node L1_6, order:14 node L2_7, order:15 node L1_7, order:16 node L2_8, order:17 node L1_8, order:18 node L2_9, order:19 node L1_9, order:20 node L2_10, order:21 node L1_10, order:22 node L2_11, order:23 node L1_11, order:24 node L2_12, order:25 node L1_12, order:26 node L2_13, order:27 node L1_13, order:28 node L2_14, order:29 node L1_14, order:30 node L2_15, order:31 node L1_15, order:32 node L2_16, order:33 node L1_16, order:34 Error in add.nodes(DAG = obj1, nodes = obj2) : DAG object is locked: nodes in this DAG cannot be modified or added after set.DAG() In addition: Warning messages: 1: In RNGkind(kind = testSuite$rngKind, normal.kind = testSuite$rngNormalKind) : RNGkind: Marsaglia-Multicarry has poor statistical properties 2: In RNGkind(kind = testSuite$rngKind, normal.kind = testSuite$rngNormalKind) : RNGkind: severe deviations from normality for Kinderman-Ramage + Marsaglia-Multicarry 3: In add.nodes(DAG = obj1, nodes = obj2) : existing non-time-varying node L2 was overwritten with a time-varying node 4: In add.nodes(DAG = obj1, nodes = obj2) : existing non-time-varying node L2 was overwritten with a time-varying node 5: In set.DAG(D) : trying to lock an empty DAG, add nodes before calling set.DAG() simulating observed dataset from the DAG object simulating observed dataset from the DAG object simulating observed dataset from the DAG object simulating observed dataset from the DAG object simulating observed dataset from the DAG object simulating observed dataset from the DAG object ...automatically assigning order attribute to some nodes... node L0, order:1 node L1, order:2 node L2, order:3 simulating observed dataset from the DAG object simulating observed dataset from the DAG object ...automatically assigning order attribute to some nodes... node L0, order:1 node L1, order:2 node L2, order:3 simulating observed dataset from the DAG object ...automatically assigning order attribute to some nodes... node L0, order:1 node L1, order:2 simulating observed dataset from the DAG object simulating observed dataset from the DAG object ...automatically assigning order attribute to some nodes... node L0, order:1 node L1, order:2 simulating observed dataset from the DAG object simulating observed dataset from the DAG object done successfully.
Executing test function test.condrcat.factor ... ...automatically assigning order attribute to some nodes... node W, order:1 node Cat3, order:2 ...automatically assigning order attribute to some nodes... node W, order:1 node Cat3, order:2 ...automatically assigning order attribute to some nodes... node W, order:1 node Cat3, order:2 Error in eval(x[[2]], envir = data.env, enclos = user.env) : dims [product 3] do not match the length of object [10] In addition: Warning messages: 1: In rcat.b1(n = n, probs = probs) : some categorical probabilities add up to more than 1, normalizing to add to 1 2: In rcat.b1(n = n, probs = probs) : some categorical probabilities add up to more than 1, normalizing to add to 1 3: In rcat.b1(n = n, probs = probs) : some categorical probabilities add up to more than 1, normalizing to add to 1 4: In rcat.b1(n = n, probs = probs) : some categorical probabilities add up to more than 1, normalizing to add to 1 5: In rcat.b1(n = n, probs = probs) : some categorical probabilities add up to more than 1, normalizing to add to 1 6: In (W == 0) * catprob.W0 : longer object length is not a multiple of shorter object length Error in set.DAG(D) : ...attempt to simulate data from DAG failed... ...automatically assigning order attribute to some nodes... node W, order:1 node Cat3, order:2 ...automatically assigning order attribute to some nodes... node W, order:1 node Cat3, order:2 simulating observed dataset from the DAG object done successfully.
Executing test function test.distr ... All custom distributions defined in SimCausal:
[1] "rbern" "rcat.b0" "rcat.b1" "rcat.factor" [5] "rcategor" "rcategor.int" "rconst" "rdistr.template" Error in rdistr.template(n = 100, arg1 = rep(0.5, 100), arg2 = rep(0.3, : inputs arguments should all have the same length done successfully.
Executing test function test.experimental_parsingMSMs ... Error in simcausal:::parse.MSMform(msm.form = msm.form_3_error, t_vec = t_vec, : unable to map some of S() expressions in MSM formula, check that all of the summary measure expressions have been previously defined data not specified, simulating full data no actions specified, sampling full data for ALL actions from the DAG evaluating the target on 100 simulated samples per action evaluating MSM summary measures and converting full data to long format for MSM target parameter MSM: fitting glm to full data simulating action-specific datasets for action(s): A1_th0 A1_th1 simulating action-specific datasets for action(s): A1_th0 A1_th1 evaluating MSM summary measures and converting full data to long format for MSM target parameter MSM: fitting glm to full data data not specified, simulating full data no actions specified, sampling full data for ALL actions from the DAG evaluating the target on 100 simulated samples per action evaluating MSM summary measures and converting full data to long format for MSM target parameter MSM: fitting glm to full data simulating action-specific datasets for action(s): A1_th0 A1_th1 evaluating MSM summary measures and converting full data to long format for MSM target parameter MSM: fitting glm to full data simulating action-specific datasets for action(s): A1_th0 A1_th1 evaluating MSM summary measures and converting full data to long format for MSM target parameter MSM: fitting glm to full data for df_full in long format new summary measures cannot be calculated, using whatever summary measures already exist in df_full for df_full in long format outcome is pooled over the same t vector as defined in the first MSM that generated the long format data, changing pooling t requires re-generating the full data assuming the data is based on the following map of MSM terms to variable names S_exprs_vec XMSMterms 1 A1[max(0, t - 2)] XMSMterm.4 MSM: fitting glm to full data for df_full in long format new summary measures cannot be calculated, using whatever summary measures already exist in df_full for df_full in long format outcome is pooled over the same t vector as defined in the first MSM that generated the long format data, changing pooling t requires re-generating the full data assuming the data is based on the following map of MSM terms to variable names S_exprs_vec XMSMterms 1 A1[max(0, t - 2)] XMSMterm.4 2 A1[max(0, t - 1)] XMSMterm.1 MSM: fitting glm to full data Error in parse.MSMform(msm.form = form, t_vec = t_vec, old.DAG = DAG, : unable to map some of S() expressions in MSM formula, check that all of the summary measure expressions have been previously defined done successfully.
Executing test function test.faster_tolongdata ... simulating observed dataset from the DAG object simulating observed dataset from the DAG object simulating observed dataset from the DAG object done successfully.
Executing test function test.latent ... ...automatically assigning order attribute to some nodes... node I, order:1 node W1, order:2 node W2, order:3 node W3, order:4 node A, order:5 node U.Y, order:6 node Y, order:7 using the following vertex attributes: NAdarkbluenone100.50 using the following edge attributes: black0.210.60.5 Timing stopped at: 0.063 0 0.063 Error :
from()was deprecated in igraph 2.0.4 and is now defunct. ℹ Please use.from()instead. done successfully.Executing test function test.longparse ... ...automatically assigning order attribute to some nodes... node group, order:1 simulating observed dataset from the DAG object ...automatically assigning order attribute to some nodes... node group, order:1 simulating observed dataset from the DAG object ...automatically assigning order attribute to some nodes... node A, order:1 node group, order:2 evaluating node group expression(s): rep(A, 3). One of the distribution parameters evaluated to non-standard vector length (its neither 1 nor n), make sure the distribution function knows how to handle it. simulating observed dataset from the DAG object evaluating node group expression(s): rep(A, 3). One of the distribution parameters evaluated to non-standard vector length (its neither 1 nor n), make sure the distribution function knows how to handle it. ...automatically assigning order attribute to some nodes... node A, order:1 node group, order:2 simulating observed dataset from the DAG object done successfully.
Executing test function test.node ... ...automatically assigning order attribute to some nodes... node L0, order:1 node L1, order:2 simulating observed dataset from the DAG object ...automatically assigning order attribute to some nodes... node L0, order:1 node L1, order:2 simulating observed dataset from the DAG object ...automatically assigning order attribute to some nodes... node L0, order:1 simulating observed dataset from the DAG object ...automatically assigning order attribute to some nodes... node L0, order:1 node L1, order:2 node L2, order:3 simulating observed dataset from the DAG object ...automatically assigning order attribute to some nodes... node W1, order:1 node W2, order:2 node W3, order:3 simulating observed dataset from the DAG object existing node W1 was modified existing node W2 was modified existing node W3 was modified ...automatically assigning order attribute to some nodes... node W1, order:1 node W2, order:2 node W3, order:3 simulating observed dataset from the DAG object ...automatically assigning order attribute to some nodes... node W1, order:1 node W2, order:2 node W3, order:3 simulating observed dataset from the DAG object ...automatically assigning order attribute to some nodes... node L1_0, order:1 node L2_0, order:2 Error in add.nodes(DAG = obj1, nodes = obj2) : cannot define nodes with missing t after nodes with t non-missing were already defined In addition: Warning messages: 1: In (function (n, probs) : some categorical probabilities add up to more than 1, normalizing to add to 1 2: In (function (n, probs) : some categorical probabilities add up to more than 1, normalizing to add to 1 3: In rcat.b1(n = n, probs = probs) : some categorical probabilities add up to more than 1, normalizing to add to 1 4: In rcat.b1(n = n, probs = probs) : some categorical probabilities add up to more than 1, normalizing to add to 1 existing node L1_3 was modified existing node L1_4 was modified existing node L1_5 was modified existing node L1_4 was modified existing node L1_5 was modified ...automatically assigning order attribute to some nodes... node L1_0, order:1 node L2_0, order:2 node L1_1, order:3 node L2_1, order:4 node L1_2, order:5 node L2_2, order:6 node L1_3, order:7 node L2_3, order:8 node L1_4, order:9 node L2_4, order:10 node L1_5, order:11 node L2_5, order:12 simulating observed dataset from the DAG object existing node L1_2 was modified existing node L1_3 was modified existing node L1_4 was modified existing node L1_5 was modified ...automatically assigning order attribute to some nodes... node L3_0, order:1 node L3_1, order:2 node L1_2, order:3 node L2_2, order:4 node L3_2, order:5 node L1_3, order:6 node L2_3, order:7 node L3_3, order:8 node L1_4, order:9 node L2_4, order:10 node L3_4, order:11 node L1_5, order:12 node L2_5, order:13 node L3_5, order:14 simulating observed dataset from the DAG object ...automatically assigning order attribute to some nodes... node L3_0, order:1 node L3_1, order:2 node L1_2, order:3 node L2_2, order:4 node L3_2, order:5 node L1_3, order:6 node L2_3, order:7 node L3_3, order:8 node L1_4, order:9 node L2_4, order:10 node L3_4, order:11 node L1_5, order:12 node L2_5, order:13 node L3_5, order:14 simulating observed dataset from the DAG object ...automatically assigning order attribute to some nodes... node L2_0, order:1 node L1_0, order:2 node A1_0, order:3 node A2_0, order:4 node Y_0, order:5 node L2_1, order:6 node A1_1, order:7 node A2_1, order:8 node Y_1, order:9 node L2_2, order:10 node A1_2, order:11 node A2_2, order:12 node Y_2, order:13 node L2_3, order:14 node A1_3, order:15 node A2_3, order:16 node Y_3, order:17 node L2_4, order:18 node A1_4, order:19 node A2_4, order:20 node Y_4, order:21 node L2_5, order:22 node A1_5, order:23 node A2_5, order:24 node Y_5, order:25 node L2_6, order:26 node A1_6, order:27 node A2_6, order:28 node Y_6, order:29 node L2_7, order:30 node A1_7, order:31 node A2_7, order:32 node Y_7, order:33 node L2_8, order:34 node A1_8, order:35 node A2_8, order:36 node Y_8, order:37 node L2_9, order:38 node A1_9, order:39 node A2_9, order:40 node Y_9, order:41 node L2_10, order:42 node A1_10, order:43 node A2_10, order:44 node Y_10, order:45 node L2_11, order:46 node A1_11, order:47 node A2_11, order:48 node Y_11, order:49 node L2_12, order:50 node A1_12, order:51 node A2_12, order:52 node Y_12, order:53 node L2_13, order:54 node A1_13, order:55 node A2_13, order:56 node Y_13, order:57 node L2_14, order:58 node A1_14, order:59 node A2_14, order:60 node Y_14, order:61 node L2_15, order:62 node A1_15, order:63 node A2_15, order:64 node Y_15, order:65 node L2_16, order:66 node A1_16, order:67 node A2_16, order:68 node Y_16, order:69 simulating observed dataset from the DAG object simulating observed dataset from the DAG object done successfully.
Executing test function test.noexistdistr ... ...automatically assigning order attribute to some nodes... node W1, order:1 rbinom2: note this distribution could not be located in package namespace, simulating from user-defined distribution found under the same name ...automatically assigning order attribute to some nodes... node W1, order:1 rbinom2: note this distribution could not be located in package namespace, simulating from user-defined distribution found under the same name Error in loadNamespace(name) : there is no package called 'rbinom3' Error in network("net", netfun = "rbinom3", Kmax = 5, size = 4, prob = c(0.4, : rbinom3: this network generator function could not be located ...automatically assigning order attribute to some nodes... node W1, order:1 simulating observed dataset from the DAG object done successfully.
Executing test function test.plotting ... ...automatically assigning order attribute to some nodes... node L2_0, order:1 node L1_0, order:2 node A1_0, order:3 node A2_0, order:4 node Y_0, order:5 node L2_1, order:6 node A1_1, order:7 node A2_1, order:8 node Y_1, order:9 node L2_2, order:10 node A1_2, order:11 node A2_2, order:12 node Y_2, order:13 node L2_3, order:14 node A1_3, order:15 node A2_3, order:16 node Y_3, order:17 node L2_4, order:18 node A1_4, order:19 node A2_4, order:20 node Y_4, order:21 node L2_5, order:22 node A1_5, order:23 node A2_5, order:24 node Y_5, order:25 node L2_6, order:26 node A1_6, order:27 node A2_6, order:28 node Y_6, order:29 node L2_7, order:30 node A1_7, order:31 node A2_7, order:32 node Y_7, order:33 node L2_8, order:34 node A1_8, order:35 node A2_8, order:36 node Y_8, order:37 node L2_9, order:38 node A1_9, order:39 node A2_9, order:40 node Y_9, order:41 node L2_10, order:42 node A1_10, order:43 node A2_10, order:44 node Y_10, order:45 node L2_11, order:46 node A1_11, order:47 node A2_11, order:48 node Y_11, order:49 node L2_12, order:50 node A1_12, order:51 node A2_12, order:52 node Y_12, order:53 node L2_13, order:54 node A1_13, order:55 node A2_13, order:56 node Y_13, order:57 node L2_14, order:58 node A1_14, order:59 node A2_14, order:60 node Y_14, order:61 node L2_15, order:62 node A1_15, order:63 node A2_15, order:64 node Y_15, order:65 node L2_16, order:66 node A1_16, order:67 node A2_16, order:68 node Y_16, order:69 done successfully.
Executing test function test.set.DAG_DAG1 ... simulating observed dataset from the DAG object some outcome nodes have EFU=TRUE, applying Last Time Point Carry Forward function: doLTCF() data not specified, simulating full data no actions specified, sampling full data for ALL actions from the DAG evaluating the target on 500 simulated samples per action some outcome nodes have EFU=TRUE, applying Last Time Point Carry Forward function: doLTCF() some outcome nodes have EFU=TRUE, applying Last Time Point Carry Forward function: doLTCF() data not specified, simulating full data no actions specified, sampling full data for ALL actions from the DAG evaluating the target on 500 simulated samples per action some outcome nodes have EFU=TRUE, applying Last Time Point Carry Forward function: doLTCF() some outcome nodes have EFU=TRUE, applying Last Time Point Carry Forward function: doLTCF() data not specified, simulating full data no actions specified, sampling full data for ALL actions from the DAG evaluating the target on 500 simulated samples per action some outcome nodes have EFU=TRUE, applying Last Time Point Carry Forward function: doLTCF() some outcome nodes have EFU=TRUE, applying Last Time Point Carry Forward function: doLTCF() some outcome nodes have EFU=TRUE, applying Last Time Point Carry Forward function: doLTCF() some outcome nodes have EFU=TRUE, applying Last Time Point Carry Forward function: doLTCF() data not specified, simulating full data no actions specified, sampling full data for ALL actions from the DAG evaluating the target on 500 simulated samples per action some outcome nodes have EFU=TRUE, applying Last Time Point Carry Forward function: doLTCF() some outcome nodes have EFU=TRUE, applying Last Time Point Carry Forward function: doLTCF() some outcome nodes have EFU=TRUE, applying Last Time Point Carry Forward function: doLTCF() some outcome nodes have EFU=TRUE, applying Last Time Point Carry Forward function: doLTCF() done successfully.
Executing test function test.set.DAG_DAG2_errors ... Error in node("A_2", distr = "rbern", prob = 0.5, order = 1) : node names with underscore character '' are not allowed Error in L2[0] : undefined time-dependent variable(s): L2_0 Error : error while evaluating node L2_0 formula: ifelse(L2[0] == 1, 0.5, 0.1). Check syntax specification. Error in set.DAG(c(L2_0, L1_0)) : ...attempt to simulate data from DAG failed... ...automatically assigning order attribute to some nodes... node , order:1 node , order:2 node , order:3 node , order:4 Error in strsplit(cur.node$name, "") : non-character argument Error in set.DAG(testDAG_2_err1a) : ...attempt to simulate data from DAG failed... ...automatically assigning order attribute to some nodes... node , order:1 node , order:2 node , order:3 node , order:4 node , order:5 Error in set.DAG(testDAG_2_err1b) : All DAG nodes must have unique name attributes done successfully.
Executing test function test.set.DAG_DAG2b_newactions ... ...automatically assigning order attribute to some nodes... node W1, order:1 node W2, order:2 node W3, order:3 node A, order:4 node Y, order:5 ...automatically assigning order attribute to some nodes... node W1, order:1 node W2, order:2 node W3, order:3 node A, order:4 node Y, order:5 ...automatically assigning order attribute to some nodes... node W1_0, order:1 node W2_0, order:2 node W3_0, order:3 node A_0, order:4 node Y_0, order:5 node W1_1, order:6 node W2_1, order:7 node W3_1, order:8 node A_1, order:9 node Y_1, order:10 ...automatically assigning order attribute to some nodes... node W1_0, order:1 node W2_0, order:2 node W3_0, order:3 node A_0, order:4 node Y_0, order:5 node W1_1, order:6 node W2_1, order:7 node W3_1, order:8 node A_1, order:9 node Y_1, order:10 simulating observed dataset from the DAG object using the following vertex attributes: NAdarkbluenone100.50 using the following edge attributes: black0.210.60.5 simulating observed dataset from the DAG object simulating observed dataset from the DAG object simulating action-specific datasets for action(s): A1 A0 simulating action-specific datasets for action(s): A1 A0 simulating action-specific datasets for action(s): A1 some outcome nodes have EFU=TRUE, applying Last Time Point Carry Forward function: doLTCF() Error : n is not a count (a single positive integer) data not specified, simulating full data no actions specified, sampling full data for ALL actions from the DAG evaluating the target on 500 simulated samples per action some outcome nodes have EFU=TRUE, applying Last Time Point Carry Forward function: doLTCF() some outcome nodes have EFU=TRUE, applying Last Time Point Carry Forward function: doLTCF() data not specified, simulating full data no actions specified, sampling full data for ALL actions from the DAG evaluating the target on 500 simulated samples per action some outcome nodes have EFU=TRUE, applying Last Time Point Carry Forward function: doLTCF() some outcome nodes have EFU=TRUE, applying Last Time Point Carry Forward function: doLTCF() data not specified, simulating full data no actions specified, sampling full data for ALL actions from the DAG evaluating the target on 500 simulated samples per action some outcome nodes have EFU=TRUE, applying Last Time Point Carry Forward function: doLTCF() simulating observed dataset from the DAG object simulating observed dataset from the DAG object simulating action-specific datasets for action(s): A1 A0 simulating action-specific datasets for action(s): A1 A0 simulating action-specific datasets for action(s): A1 A0 Error in getactions(DAG, actions) : Couldn't locate action: A4 , first define action by adding it to the DAG object with DAG+action In addition: There were 13 warnings (use warnings() to see them) simulating observed dataset from the DAG object simulating observed dataset from the DAG object simulating action-specific datasets for action(s): A1 A0 simulating action-specific datasets for action(s): A1 A0 simulating observed dataset from the DAG object simulating observed dataset from the DAG object user system elapsed 0.17 0.00 0.17 simulating observed dataset from the DAG object user system elapsed 0.185 0.000 0.186 simulating observed dataset from the DAG object user system elapsed 0.204 0.000 0.205 Error in X_dat_th0[[2]] : subscript out of bounds In addition: There were 11 warnings (use warnings() to see them) some outcome nodes have EFU=TRUE, applying Last Time Point Carry Forward function: doLTCF() data not specified, simulating full data no actions specified, sampling full data for ALL actions from the DAG evaluating the target on 500 simulated samples per action some outcome nodes have EFU=TRUE, applying Last Time Point Carry Forward function: doLTCF() data not specified, simulating full data evaluating the target on 500 simulated samples per action some outcome nodes have EFU=TRUE, applying Last Time Point Carry Forward function: doLTCF() data not specified, simulating full data evaluating the target on 500 simulated samples per action Error in eval.target(D, n = 500, actions = "A1_th1", rndseed = 123) : some of the actions in param argument could not be found in the simulated full data some outcome nodes have EFU=TRUE, applying Last Time Point Carry Forward function: doLTCF() some outcome nodes have EFU=TRUE, applying Last Time Point Carry Forward function: doLTCF() some outcome nodes have EFU=TRUE, applying Last Time Point Carry Forward function: doLTCF() some outcome nodes have EFU=TRUE, applying Last Time Point Carry Forward function: doLTCF() some outcome nodes have EFU=TRUE, applying Last Time Point Carry Forward function: doLTCF() some outcome nodes have EFU=TRUE, applying Last Time Point Carry Forward function: doLTCF() MSM: fitting glm to full data data not specified, simulating full data no actions specified, sampling full data for ALL actions from the DAG evaluating the target on 500 simulated samples per action MSM: fitting glm to full data data not specified, simulating full data evaluating the target on 500 simulated samples per action MSM: fitting glm to full data MSM: fitting glm to full data data not specified, simulating full data no actions specified, sampling full data for ALL actions from the DAG evaluating the target on 500 simulated samples per action MSM: fitting glm to full data simulating observed dataset from the DAG object done successfully.
Executing test function test.set.DAG_DAG3_wlong ... simulating observed dataset from the DAG object simulating observed dataset from the DAG object done successfully.
Executing test function test.set.DAG_DAG_informcens ... simulating observed dataset from the DAG object simulating observed dataset from the DAG object simulating observed dataset from the DAG object data not specified, simulating full data no actions specified, sampling full data for ALL actions from the DAG evaluating the target on 100 simulated samples per action some outcome nodes have EFU=TRUE, applying Last Time Point Carry Forward function: doLTCF() data not specified, simulating full data no actions specified, sampling full data for ALL actions from the DAG evaluating the target on 100 simulated samples per action some outcome nodes have EFU=TRUE, applying Last Time Point Carry Forward function: doLTCF() some outcome nodes have EFU=TRUE, applying Last Time Point Carry Forward function: doLTCF() data not specified, simulating full data no actions specified, sampling full data for ALL actions from the DAG evaluating the target on 500 simulated samples per action MSM: fitting glm to full data MSM: fitting glm to full data data not specified, simulating full data no actions specified, sampling full data for ALL actions from the DAG evaluating the target on 500 simulated samples per action MSM: fitting glm to full data MSM: fitting glm to full data data not specified, simulating full data no actions specified, sampling full data for ALL actions from the DAG evaluating the target on 500 simulated samples per action evaluating MSM summary measures and converting full data to long format for MSM target parameter MSM: fitting glm to full data simulating observed dataset from the DAG object simulating observed dataset from the DAG object done successfully.
Executing test function test.set.DAG_general ... Error in set.DAG(testDAG_listobj1) : DAG must be a list In addition: Warning message: In predict.lm(object, newdata, se.fit, scale = 1, type = if (type == : prediction from rank-deficient fit; attr(*, "non-estim") has doubtful cases Error in set.DAG(testDAG_listobj2) : each of DAG items must be a list specifying DAG node(s) ...automatically assigning order attribute to some nodes... node nm, order:1 node nm, order:2 node nm, order:3 Error : !is.null(user.env) is not TRUE Error in set.DAG(testDAG_names1) : ...attempt to simulate data from DAG failed... ...automatically assigning order attribute to some nodes... node nm, order:2 node nm, order:3 Error : !is.null(user.env) is not TRUE Error in set.DAG(testDAG_names2) : ...attempt to simulate data from DAG failed... Error in set.DAG(testDAG_dist) : All DAG nodes must have unique name attributes done successfully.
Executing test function test.substitute ... ...automatically assigning order attribute to some nodes... node I, order:1 node W1, order:2 node W2, order:3 node W3, order:4 node A, order:5 simulating observed dataset from the DAG object done successfully.
Executing test function test.t.error ... Error in add.nodes(DAG = obj1, nodes = obj2) : cannot define nodes with missing t after nodes with t non-missing were already defined done successfully.
Executing test function test.tswitch_2MSMs ... [1] 0 [1] "abar" [1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 [1] 3 [1] "abar" [1] 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 [1] 6 [1] "abar" [1] 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 [1] 10 [1] "abar" [1] 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 [1] 13 [1] "abar" [1] 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 [1] 17 [1] "abar" [1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 [1] "tswitch_i" [1] 0 [1] "meanExposureVec" [1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 [1] "tswitch_i" [1] 3 [1] "meanExposureVec" [1] 0.000 0.000 0.000 0.250 0.400 0.500 0.571 0.625 0.667 0.700 0.727 0.750 [13] 0.769 0.786 0.800 0.812 0.824 [1] "tswitch_i" [1] 6 [1] "meanExposureVec" [1] 0.000 0.000 0.000 0.000 0.000 0.000 0.143 0.250 0.333 0.400 0.455 0.500 [13] 0.538 0.571 0.600 0.625 0.647 [1] "tswitch_i" [1] 10 [1] "meanExposureVec" [1] 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.091 0.167 [13] 0.231 0.286 0.333 0.375 0.412 [1] "tswitch_i" [1] 13 [1] "meanExposureVec" [1] 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 [13] 0.000 0.071 0.133 0.188 0.235 [1] "tswitch_i" [1] 17 [1] "meanExposureVec" [1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 data not specified, simulating full data no actions specified, sampling full data for ALL actions from the DAG evaluating the target on 500 simulated samples per action evaluating MSM summary measures and converting full data to long format for MSM target parameter MSM: fitting glm to full data data not specified, simulating full data no actions specified, sampling full data for ALL actions from the DAG evaluating the target on 500 simulated samples per action evaluating MSM summary measures and converting full data to long format for MSM target parameter MSM: fitting glm to full data data not specified, simulating full data no actions specified, sampling full data for ALL actions from the DAG evaluating the target on 500 simulated samples per action evaluating MSM summary measures and converting full data to long format for MSM target parameter MSM: fitting glm to full data data not specified, simulating full data no actions specified, sampling full data for ALL actions from the DAG evaluating the target on 500 simulated samples per action evaluating MSM summary measures and converting full data to long format for MSM target parameter MSM: fitting glm to full data data not specified, simulating full data no actions specified, sampling full data for ALL actions from the DAG evaluating the target on 500 simulated samples per action MSM: fitting glm to full data [1] "MSMtermName used" [1] "XMSMterm.1" [1] "MSMterm_vals last" [1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 [1] "MSMterm_vals last" [1] 0.0000000 0.0000000 0.0000000 0.2500000 0.4000000 0.5000000 0.5714286 [8] 0.6250000 0.6666667 0.7000000 0.7272727 0.7500000 0.7692308 0.7857143 [15] 0.8000000 0.8125000 0.8235294 [1] "MSMterm_vals last" [1] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.1428571 [8] 0.2500000 0.3333333 0.4000000 0.4545455 0.5000000 0.5384615 0.5714286 [15] 0.6000000 0.6250000 0.6470588 [1] "MSMterm_vals last" [1] 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 [7] 0.00000000 0.00000000 0.00000000 0.00000000 0.09090909 0.16666667 [13] 0.23076923 0.28571429 0.33333333 0.37500000 0.41176471 [1] "MSMterm_vals last" [1] 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 [7] 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 [13] 0.00000000 0.07142857 0.13333333 0.18750000 0.23529412 [1] "MSMterm_vals last" [1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 [1] "MSMtermName used" [1] "meanExposure" [1] "MSMterm_vals last" [1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 [1] "MSMterm_vals last" [1] 0.0000000 0.0000000 0.0000000 0.2500000 0.4000000 0.5000000 0.5714286 [8] 0.6250000 0.6666667 0.7000000 0.7272727 0.7500000 0.7692308 0.7857143 [15] 0.8000000 0.8125000 0.8235294 [1] "MSMterm_vals last" [1] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.1428571 [8] 0.2500000 0.3333333 0.4000000 0.4545455 0.5000000 0.5384615 0.5714286 [15] 0.6000000 0.6250000 0.6470588 [1] "MSMterm_vals last" [1] 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 [7] 0.00000000 0.00000000 0.00000000 0.00000000 0.09090909 0.16666667 [13] 0.23076923 0.28571429 0.33333333 0.37500000 0.41176471 [1] "MSMterm_vals last" [1] 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 [7] 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 [13] 0.00000000 0.07142857 0.13333333 0.18750000 0.23529412 [1] "MSMterm_vals last" [1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 data not specified, simulating full data no actions specified, sampling full data for ALL actions from the DAG evaluating the target on 500 simulated samples per action evaluating MSM summary measures and converting full data to long format for MSM target parameter MSM: fitting glm to full data data not specified, simulating full data no actions specified, sampling full data for ALL actions from the DAG evaluating the target on 500 simulated samples per action evaluating MSM summary measures and converting full data to long format for MSM target parameter MSM: fitting glm to full data data not specified, simulating full data no actions specified, sampling full data for ALL actions from the DAG evaluating the target on 500 simulated samples per action MSM: fitting glm to full data [1] "MSMtermName used" [1] "XMSMterm.1" [1] "MSMterm_vals last" [1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 [1] "MSMterm_vals last" [1] 0.0000000 0.0000000 0.0000000 0.2500000 0.4000000 0.5000000 0.5714286 [8] 0.6250000 0.6666667 0.7000000 0.7272727 0.7500000 0.7692308 0.7857143 [15] 0.8000000 0.8125000 0.8235294 [1] "MSMterm_vals last" [1] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.1428571 [8] 0.2500000 0.3333333 0.4000000 0.4545455 0.5000000 0.5384615 0.5714286 [15] 0.6000000 0.6250000 0.6470588 [1] "MSMterm_vals last" [1] 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 [7] 0.00000000 0.00000000 0.00000000 0.00000000 0.09090909 0.16666667 [13] 0.23076923 0.28571429 0.33333333 0.37500000 0.41176471 [1] "MSMterm_vals last" [1] 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 [7] 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 [13] 0.00000000 0.07142857 0.13333333 0.18750000 0.23529412 [1] "MSMterm_vals last" [1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 [1] "MSMtermName used" [1] "XMSMterm.1" [1] "MSMterm_vals last" [1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 [1] "MSMterm_vals last" [1] 0.0000000 0.0000000 0.0000000 0.2500000 0.4000000 0.5000000 0.5714286 [8] 0.6250000 0.6666667 0.7000000 0.7272727 0.7500000 0.7692308 0.7857143 [15] 0.8000000 0.8125000 0.8235294 [1] "MSMterm_vals last" [1] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.1428571 [8] 0.2500000 0.3333333 0.4000000 0.4545455 0.5000000 0.5384615 0.5714286 [15] 0.6000000 0.6250000 0.6470588 [1] "MSMterm_vals last" [1] 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 [7] 0.00000000 0.00000000 0.00000000 0.00000000 0.09090909 0.16666667 [13] 0.23076923 0.28571429 0.33333333 0.37500000 0.41176471 [1] "MSMterm_vals last" [1] 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 [7] 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 [13] 0.00000000 0.07142857 0.13333333 0.18750000 0.23529412 [1] "MSMterm_vals last" [1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 [1] "MSMtermName used" [1] "meanExposure" [1] "MSMterm_vals last" [1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 [1] "MSMterm_vals last" [1] 0.0000000 0.0000000 0.0000000 0.2500000 0.4000000 0.5000000 0.5714286 [8] 0.6250000 0.6666667 0.7000000 0.7272727 0.7500000 0.7692308 0.7857143 [15] 0.8000000 0.8125000 0.8235294 [1] "MSMterm_vals last" [1] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.1428571 [8] 0.2500000 0.3333333 0.4000000 0.4545455 0.5000000 0.5384615 0.5714286 [15] 0.6000000 0.6250000 0.6470588 [1] "MSMterm_vals last" [1] 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 [7] 0.00000000 0.00000000 0.00000000 0.00000000 0.09090909 0.16666667 [13] 0.23076923 0.28571429 0.33333333 0.37500000 0.41176471 [1] "MSMterm_vals last" [1] 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 [7] 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 [13] 0.00000000 0.07142857 0.13333333 0.18750000 0.23529412 [1] "MSMterm_vals last" [1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 [1] 0 [1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 [1] 5 [1] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.1666667 0.2857143 [8] 0.3750000 0.4444444 0.5000000 0.5454545 0.5833333 0.6153846 0.6428571 [15] 0.6666667 0.6875000 0.7058824 [1] 10 [1] 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 [7] 0.00000000 0.00000000 0.00000000 0.00000000 0.09090909 0.16666667 [13] 0.23076923 0.28571429 0.33333333 0.37500000 0.41176471 [1] 17 [1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 data not specified, simulating full data no actions specified, sampling full data for ALL actions from the DAG evaluating the target on 200 simulated samples per action MSM: fitting glm to full data [1] "MSMtermName used" [1] "meanExposure" [1] "MSMterm_vals last" [1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 [1] "MSMterm_vals last" [1] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.1666667 0.2857143 [8] 0.3750000 0.4444444 0.5000000 0.5454545 0.5833333 0.6153846 0.6428571 [15] 0.6666667 0.6875000 0.7058824 [1] "MSMterm_vals last" [1] 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 [7] 0.00000000 0.00000000 0.00000000 0.00000000 0.09090909 0.16666667 [13] 0.23076923 0.28571429 0.33333333 0.37500000 0.41176471 [1] "MSMterm_vals last" [1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 done successfully.
Executing test function test.long.wide.simobs ... done successfully.
Executing test function test.networkgen1 ... [1] "automatic Kmax: " [1] 2 [1] "NetInd_k" [,1] [,2] [1,] NA NA [2,] 4 NA [3,] NA NA [4,] NA NA [5,] 6 8 [6,] 2 10 existing node Net.sample was modified simulating network with ER model using m: 10 [1] "automatic Kmax: " [1] 3 [1] "NetInd_k" [,1] [,2] [,3] [1,] 2 NA NA [2,] NA NA NA [3,] 9 NA NA [4,] NA NA NA [5,] 7 NA NA [6,] NA NA NA existing node Net.sample was modified simulating network with ER model using m: 10 [1] "automatic Kmax: " [1] 3 [1] "NetInd_k" [,1] [,2] [,3] [1,] NA NA NA [2,] NA NA NA [3,] 4 5 NA [4,] NA NA NA [5,] 2 6 7 [6,] NA NA NA simulating network with ER model using m: 10 [1] "automatic Kmax: " [1] 3 [1] "NetInd_k" [,1] [,2] [,3] [1,] NA NA NA [2,] 8 NA NA [3,] 6 NA NA [4,] 5 6 8 [5,] 1 NA NA [6,] NA NA NA simulating network with ER model using m: 500000 [1] "automatic Kmax: " [1] 551 [1] "NetInd_k" [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13] [,14] [1,] 2 3 8 10 14 16 17 18 19 20 21 22 23 24 [2,] 1 3 5 7 8 9 10 11 12 16 17 18 21 22 [3,] 1 2 7 8 9 11 12 14 15 17 22 23 25 26 [4,] 5 6 8 9 10 12 13 15 17 18 20 22 24 25 [5,] 1 6 7 9 11 15 18 20 23 26 27 28 30 33 [6,] 9 12 13 15 16 18 21 23 24 26 27 28 29 30 [,15] [,16] [,17] [,18] [,19] [,20] [,21] [,22] [,23] [,24] [,25] [,26] [1,] 28 33 35 38 40 43 44 45 47 49 51 53 [2,] 27 28 34 37 40 42 43 45 46 48 51 52 [3,] 27 32 35 36 37 41 44 45 47 49 50 53 [4,] 26 29 31 36 38 39 41 42 45 49 51 52 [5,] 35 36 43 44 45 46 48 50 58 60 61 62 [6,] 34 38 39 40 41 42 43 45 46 49 50 53 [,27] [,28] [,29] [,30] [,31] [,32] [,33] [,34] [,35] [,36] [,37] [,38] [1,] 56 57 58 59 62 63 66 67 68 69 72 75 [2,] 55 56 57 63 64 66 67 69 70 72 75 76 [3,] 56 57 58 59 61 63 64 65 67 69 71 74 [4,] 54 56 59 60 61 65 67 70 73 75 76 77 [5,] 63 66 72 76 77 81 84 85 86 87 89 90 [6,] 54 57 60 61 64 65 66 67 68 69 73 75 [,39] [,40] [,41] [,42] [,43] [,44] [,45] [,46] [,47] [,48] [,49] [,50] [1,] 77 78 79 81 83 84 88 89 91 92 93 95 [2,] 78 79 80 81 83 87 88 90 96 101 107 110 [3,] 75 76 77 82 83 84 86 87 89 94 95 96 [4,] 78 80 81 82 84 87 91 92 95 97 98 102 [5,] 91 94 95 99 100 101 103 105 106 108 109 111 [6,] 76 77 81 83 84 85 86 88 89 100 102 105 [,51] [,52] [,53] [,54] [,55] [,56] [,57] [,58] [,59] [,60] [,61] [,62] [1,] 97 98 99 100 101 105 106 107 109 110 111 115 [2,] 113 114 115 116 117 123 124 125 127 135 136 138 [3,] 97 100 101 102 106 107 112 113 117 119 120 121 [4,] 103 105 107 108 110 112 114 120 121 124 125 126 [5,] 114 117 118 120 121 123 124 125 128 129 130 131 [6,] 106 108 110 111 114 115 116 118 119 121 122 123 [,63] [,64] [,65] [,66] [,67] [,68] [,69] [,70] [,71] [,72] [,73] [,74] [1,] 118 119 120 122 127 128 131 136 137 138 141 145 [2,] 139 141 142 143 144 145 147 148 149 150 151 153 [3,] 122 124 127 128 130 132 133 134 137 138 140 141 [4,] 129 131 132 133 134 135 136 137 138 140 141 142 [5,] 133 134 135 137 139 144 145 147 148 149 151 152 [6,] 125 127 128 131 137 138 139 142 144 148 149 150 [,75] [,76] [,77] [,78] [,79] [,80] [,81] [,82] [,83] [,84] [,85] [,86] [1,] 146 148 150 151 152 153 154 155 156 159 160 161 [2,] 154 156 157 159 160 161 164 165 168 171 173 175 [3,] 143 144 146 150 153 156 157 158 159 162 163 165 [4,] 153 154 156 157 158 162 163 164 165 166 168 170 [5,] 155 157 158 159 161 164 167 168 169 171 172 173 [6,] 151 153 156 157 158 159 163 165 166 167 168 169 [,87] [,88] [,89] [,90] [,91] [,92] [,93] [,94] [,95] [,96] [,97] [,98] [1,] 163 164 165 167 168 170 171 177 180 182 184 186 [2,] 176 182 183 184 185 189 191 192 194 195 197 198 [3,] 167 169 171 173 175 179 180 183 184 185 186 187 [4,] 175 178 179 180 181 182 183 184 185 187 188 190 [5,] 179 182 183 187 189 190 192 194 199 203 204 205 [6,] 170 174 176 177 179 185 188 190 194 196 197 199 [,99] [,100] [,101] [,102] [,103] [,104] [,105] [,106] [,107] [,108] [1,] 187 188 189 191 192 196 197 198 200 201 [2,] 200 202 203 205 206 209 211 214 217 218 [3,] 188 189 190 194 195 198 199 200 201 202 [4,] 192 193 194 195 199 200 201 203 204 208 [5,] 206 207 209 211 212 214 217 219 220 221 [6,] 200 201 204 208 209 216 217 218 222 223 [,109] [,110] [,111] [,112] [,113] [,114] [,115] [,116] [,117] [,118] [1,] 202 203 204 206 209 212 213 215 216 219 [2,] 219 220 222 223 224 225 226 227 231 232 [3,] 203 204 205 209 211 212 213 215 216 220 [4,] 211 212 217 220 221 224 226 230 232 233 [5,] 222 223 224 225 227 228 234 235 236 238 [6,] 224 225 227 228 229 233 236 237 238 239 [,119] [,120] [,121] [,122] [,123] [,124] [,125] [,126] [,127] [,128] [1,] 220 227 228 230 232 236 237 239 242 244 [2,] 233 234 235 236 238 239 240 241 242 243 [3,] 221 224 225 227 228 229 231 232 235 239 [4,] 235 241 242 246 247 248 249 251 253 254 [5,] 240 241 243 247 248 249 251 252 254 255 [6,] 241 242 246 247 252 253 254 260 261 262 [,129] [,130] [,131] [,132] [,133] [,134] [,135] [,136] [,137] [,138] [1,] 246 247 250 251 252 253 254 255 258 260 [2,] 244 245 247 249 250 254 255 256 262 264 [3,] 241 243 245 246 247 251 252 255 256 258 [4,] 256 258 259 260 266 269 270 272 273 274 [5,] 259 261 263 264 266 267 268 269 270 271 [6,] 266 268 269 271 273 276 281 283 284 285 [,139] [,140] [,141] [,142] [,143] [,144] [,145] [,146] [,147] [,148] [1,] 261 262 264 265 269 272 276 279 280 281 [2,] 265 266 267 269 271 272 275 279 281 282 [3,] 261 263 265 266 267 270 271 272 273 274 [4,] 277 281 283 286 287 289 293 294 295 296 [5,] 273 274 280 281 282 285 289 291 294 295 [6,] 287 288 289 290 291 293 294 295 298 300 [,149] [,150] [,151] [,152] [,153] [,154] [,155] [,156] [,157] [,158] [1,] 283 286 289 290 292 293 298 299 301 302 [2,] 285 287 292 294 297 299 300 301 302 304 [3,] 275 277 279 280 281 282 283 284 286 287 [4,] 298 300 301 302 304 305 306 307 308 309 [5,] 296 297 298 300 301 303 305 306 307 308 [6,] 302 307 311 312 314 315 318 319 320 323 [,159] [,160] [,161] [,162] [,163] [,164] [,165] [,166] [,167] [,168] [1,] 304 305 306 308 311 313 315 316 319 322 [2,] 308 316 318 320 326 327 329 330 331 334 [3,] 288 289 290 291 292 294 295 301 302 303 [4,] 313 318 320 321 322 325 327 328 329 330 [5,] 309 312 313 317 319 320 321 322 323 324 [6,] 326 328 332 335 336 338 342 343 344 346 [,169] [,170] [,171] [,172] [,173] [,174] [,175] [,176] [,177] [,178] [1,] 324 325 327 329 331 332 335 336 343 345 [2,] 335 337 340 341 343 344 345 347 348 349 [3,] 306 307 308 310 312 316 317 320 324 327 [4,] 331 332 336 337 341 342 344 345 346 348 [5,] 325 326 327 329 331 333 334 335 337 338 [6,] 347 348 349 351 352 353 354 355 358 361 [,179] [,180] [,181] [,182] [,183] [,184] [,185] [,186] [,187] [,188] [1,] 347 349 350 352 354 356 357 358 359 360 [2,] 350 351 352 353 354 355 356 359 360 362 [3,] 330 331 332 334 338 339 340 341 344 345 [4,] 351 352 356 360 361 365 366 367 368 372 [5,] 339 341 343 344 346 348 349 353 354 356 [6,] 362 364 368 371 372 374 375 376 378 381 [,189] [,190] [,191] [,192] [,193] [,194] [,195] [,196] [,197] [,198] [1,] 362 365 367 369 372 374 375 380 383 385 [2,] 364 367 372 373 379 380 381 383 385 386 [3,] 346 348 353 354 355 357 359 360 362 364 [4,] 375 376 377 378 379 380 383 389 390 391 [5,] 359 360 361 362 365 371 373 374 375 376 [6,] 382 384 385 387 388 390 391 392 393 394 [,199] [,200] [,201] [,202] [,203] [,204] [,205] [,206] [,207] [,208] [1,] 389 391 397 399 400 401 404 405 418 420 [2,] 389 391 396 397 398 399 402 403 405 407 [3,] 366 367 369 371 372 373 376 381 385 386 [4,] 395 397 399 400 402 403 405 406 407 408 [5,] 379 380 381 382 383 384 385 388 389 390 [6,] 395 396 397 398 400 402 403 404 405 407 [,209] [,210] [,211] [,212] [,213] [,214] [,215] [,216] [,217] [,218] [1,] 421 423 427 431 432 435 436 437 438 439 [2,] 409 411 414 421 422 424 425 426 429 430 [3,] 388 390 391 395 399 400 401 403 404 405 [4,] 412 415 416 417 422 423 424 425 429 430 [5,] 397 399 400 401 402 405 406 407 408 409 [6,] 408 409 412 414 415 416 419 420 421 425 [,219] [,220] [,221] [,222] [,223] [,224] [,225] [,226] [,227] [,228] [1,] 440 442 444 448 449 450 451 455 460 462 [2,] 432 436 438 439 440 445 449 452 454 455 [3,] 406 407 408 411 412 413 414 416 418 419 [4,] 442 443 444 448 449 450 451 454 455 457 [5,] 411 412 416 418 420 421 422 426 429 430 [6,] 426 428 430 431 432 435 438 439 443 446 [,229] [,230] [,231] [,232] [,233] [,234] [,235] [,236] [,237] [,238] [1,] 469 472 477 478 479 481 486 491 494 495 [2,] 457 458 462 463 464 466 468 469 470 471 [3,] 422 424 425 429 430 431 433 435 440 441 [4,] 458 461 463 468 469 475 476 477 482 487 [5,] 433 435 439 441 443 446 449 450 451 462 [6,] 447 448 451 453 455 457 458 460 463 464 [,239] [,240] [,241] [,242] [,243] [,244] [,245] [,246] [,247] [,248] [1,] 496 497 499 500 501 503 504 505 506 508 [2,] 472 473 474 475 478 481 485 488 489 490 [3,] 443 445 447 448 451 453 455 457 458 459 [4,] 489 490 491 492 494 497 498 501 502 503 [5,] 463 468 469 472 473 474 477 479 480 483 [6,] 466 468 470 473 475 479 481 484 487 489 [,249] [,250] [,251] [,252] [,253] [,254] [,255] [,256] [,257] [,258] [1,] 509 510 511 512 515 518 522 523 524 526 [2,] 491 492 493 494 496 497 500 503 505 508 [3,] 461 463 468 469 470 471 472 474 479 480 [4,] 509 512 513 517 520 521 523 524 526 527 [5,] 484 486 487 488 489 490 491 492 494 496 [6,] 491 492 494 495 497 498 500 504 505 508 [,259] [,260] [,261] [,262] [,263] [,264] [,265] [,266] [,267] [,268] [1,] 529 531 534 535 537 539 542 543 545 546 [2,] 510 511 516 517 519 520 521 522 523 524 [3,] 486 488 491 492 494 496 497 499 500 501 [4,] 528 529 530 531 536 537 539 540 542 544 [5,] 497 500 501 502 505 507 508 513 514 517 [6,] 510 511 512 513 515 519 520 521 523 524 [,269] [,270] [,271] [,272] [,273] [,274] [,275] [,276] [,277] [,278] [1,] 551 553 554 555 556 560 562 564 568 569 [2,] 526 528 531 533 534 535 540 541 543 544 [3,] 502 503 506 509 511 512 514 515 516 518 [4,] 545 546 549 550 556 557 559 560 563 565 [5,] 518 520 522 529 530 531 532 536 537 540 [6,] 526 529 530 531 533 534 536 539 540 542 [,279] [,280] [,281] [,282] [,283] [,284] [,285] [,286] [,287] [,288] [1,] 570 571 574 576 578 581 582 583 585 588 [2,] 545 549 550 552 558 559 561 562 563 564 [3,] 520 521 523 525 530 532 533 535 541 542 [4,] 567 569 571 573 578 579 580 582 583 586 [5,] 542 545 547 550 557 558 559 560 561 563 [6,] 543 545 546 547 548 549 550 552 553 554 [,289] [,290] [,291] [,292] [,293] [,294] [,295] [,296] [,297] [,298] [1,] 589 590 596 599 600 602 604 607 608 609 [2,] 565 569 570 573 574 575 578 579 580 583 [3,] 543 546 548 550 554 555 557 558 559 560 [4,] 587 588 589 595 596 597 599 600 601 603 [5,] 566 568 570 571 573 575 576 579 584 586 [6,] 556 560 561 562 566 567 571 572 573 574 [,299] [,300] [,301] [,302] [,303] [,304] [,305] [,306] [,307] [,308] [1,] 615 616 618 621 623 624 627 629 631 633 [2,] 584 588 593 594 595 597 600 601 602 605 [3,] 561 562 563 564 565 567 568 577 578 579 [4,] 605 606 610 611 612 613 615 616 617 618 [5,] 588 589 592 594 598 599 602 603 604 605 [6,] 575 576 577 581 583 589 593 594 595 596 [,309] [,310] [,311] [,312] [,313] [,314] [,315] [,316] [,317] [,318] [1,] 634 635 636 637 639 640 642 645 646 647 [2,] 610 611 613 618 619 621 624 629 631 633 [3,] 581 582 583 585 587 588 589 591 594 596 [4,] 619 620 623 624 625 626 629 634 637 640 [5,] 608 611 613 616 617 620 624 625 627 632 [6,] 598 600 601 602 604 609 610 611 614 615 [,319] [,320] [,321] [,322] [,323] [,324] [,325] [,326] [,327] [,328] [1,] 648 649 652 653 655 656 657 659 663 666 [2,] 634 636 637 638 640 643 644 645 650 651 [3,] 597 599 601 602 603 604 605 606 608 611 [4,] 642 649 654 655 656 657 658 660 661 664 [5,] 634 639 642 646 647 650 653 662 663 665 [6,] 616 618 619 620 622 624 628 630 631 634 [,329] [,330] [,331] [,332] [,333] [,334] [,335] [,336] [,337] [,338] [1,] 668 669 672 674 675 677 678 679 685 689 [2,] 653 656 660 664 666 667 668 670 671 673 [3,] 613 614 622 623 624 628 629 630 631 632 [4,] 665 666 668 669 670 673 674 675 676 677 [5,] 666 667 669 672 673 674 675 678 679 681 [6,] 636 638 641 644 646 647 648 649 650 652 [,339] [,340] [,341] [,342] [,343] [,344] [,345] [,346] [,347] [,348] [1,] 691 694 697 698 701 705 707 708 709 710 [2,] 674 675 678 679 680 681 685 686 687 688 [3,] 633 636 637 641 644 646 651 652 653 654 [4,] 678 679 680 682 685 686 688 689 690 691 [5,] 682 687 690 691 694 698 701 705 708 711 [6,] 653 654 656 658 659 661 662 668 669 671 [,349] [,350] [,351] [,352] [,353] [,354] [,355] [,356] [,357] [,358] [1,] 711 714 715 717 719 721 726 728 730 731 [2,] 693 694 697 699 700 704 706 708 709 712 [3,] 655 657 658 659 660 661 663 668 670 671 [4,] 693 695 696 699 700 701 702 703 704 706 [5,] 712 713 714 715 716 717 718 719 720 721 [6,] 672 674 675 678 680 681 683 684 689 691 [,359] [,360] [,361] [,362] [,363] [,364] [,365] [,366] [,367] [,368] [1,] 732 733 734 735 738 739 740 745 749 750 [2,] 714 715 716 719 721 722 726 727 730 731 [3,] 673 678 679 680 681 682 684 685 686 689 [4,] 707 710 711 715 717 718 720 721 722 723 [5,] 722 725 726 730 731 732 733 734 735 738 [6,] 693 697 698 701 703 705 706 710 712 713 [,369] [,370] [,371] [,372] [,373] [,374] [,375] [,376] [,377] [,378] [1,] 751 752 753 757 758 760 762 765 768 769 [2,] 733 734 735 737 738 739 740 742 743 744 [3,] 692 693 695 701 706 710 711 715 716 718 [4,] 724 725 726 727 728 729 731 733 734 735 [5,] 739 740 741 742 744 746 749 750 751 753 [6,] 717 720 721 722 724 725 726 729 730 734 [,379] [,380] [,381] [,382] [,383] [,384] [,385] [,386] [,387] [,388] [1,] 771 774 775 778 779 780 781 782 784 785 [2,] 745 747 748 749 750 751 752 755 756 757 [3,] 719 721 724 725 728 730 732 734 739 741 [4,] 739 744 745 747 749 751 753 754 759 760 [5,] 757 760 763 765 766 769 771 772 776 779 [6,] 735 736 738 739 740 741 744 745 747 748 [,389] [,390] [,391] [,392] [,393] [,394] [,395] [,396] [,397] [,398] [1,] 786 787 788 792 794 795 796 797 803 804 [2,] 758 759 760 762 763 764 766 767 769 770 [3,] 742 744 745 748 750 753 758 759 763 766 [4,] 762 767 769 770 772 774 775 776 777 780 [5,] 780 781 783 786 787 788 789 792 794 795 [6,] 751 752 755 756 758 759 760 761 763 765 [,399] [,400] [,401] [,402] [,403] [,404] [,405] [,406] [,407] [,408] [1,] 806 808 812 814 815 819 820 823 826 830 [2,] 772 773 778 781 782 783 784 785 788 789 [3,] 768 769 771 772 773 776 777 778 780 783 [4,] 786 787 792 793 794 796 798 799 800 803 [5,] 798 801 802 804 805 806 807 808 810 812 [6,] 766 767 769 774 775 779 782 783 784 786 [,409] [,410] [,411] [,412] [,413] [,414] [,415] [,416] [,417] [,418] [1,] 831 833 835 836 838 843 845 847 848 849 [2,] 791 792 793 794 795 797 799 801 803 804 [3,] 784 785 786 788 789 792 793 794 795 799 [4,] 808 809 810 811 814 816 818 819 820 821 [5,] 814 816 822 824 825 826 828 830 836 838 [6,] 787 789 792 793 794 795 798 803 804 806 [,419] [,420] [,421] [,422] [,423] [,424] [,425] [,426] [,427] [,428] [1,] 851 852 854 855 857 858 859 860 861 863 [2,] 805 807 809 810 811 814 815 817 819 820 [3,] 802 804 806 807 808 810 811 815 816 818 [4,] 824 825 826 827 830 831 832 835 836 837 [5,] 839 840 841 842 843 848 850 851 852 857 [6,] 807 808 809 812 813 814 815 816 817 821 [,429] [,430] [,431] [,432] [,433] [,434] [,435] [,436] [,437] [,438] [1,] 865 866 867 868 870 874 876 878 879 883 [2,] 821 822 825 826 827 828 829 830 832 833 [3,] 819 823 824 827 828 831 833 837 839 840 [4,] 840 842 843 844 845 846 849 850 851 852 [5,] 859 863 864 866 867 868 871 873 878 879 [6,] 824 827 833 834 835 838 840 841 843 844 [,439] [,440] [,441] [,442] [,443] [,444] [,445] [,446] [,447] [,448] [1,] 885 886 888 889 890 892 893 894 896 897 [2,] 834 837 839 840 842 843 845 848 849 850 [3,] 844 845 846 847 849 853 855 858 859 860 [4,] 854 863 864 868 872 874 877 879 881 885 [5,] 880 881 882 886 887 889 890 891 892 893 [6,] 845 849 850 851 855 857 863 864 866 869 [,449] [,450] [,451] [,452] [,453] [,454] [,455] [,456] [,457] [,458] [1,] 898 899 901 902 908 909 912 916 918 920 [2,] 851 852 855 856 857 858 859 864 865 866 [3,] 861 862 864 866 871 872 876 877 879 880 [4,] 888 890 891 899 900 901 903 904 906 907 [5,] 895 896 898 904 905 907 909 914 916 918 [6,] 870 871 873 874 877 878 879 881 882 885 [,459] [,460] [,461] [,462] [,463] [,464] [,465] [,466] [,467] [,468] [1,] 921 922 923 930 931 933 934 935 938 939 [2,] 868 875 876 878 879 881 882 883 884 885 [3,] 882 886 887 888 889 890 891 897 898 900 [4,] 911 912 913 915 916 918 920 921 922 924 [5,] 920 921 922 923 924 926 928 932 933 934 [6,] 887 888 890 892 899 900 901 903 905 909 [,469] [,470] [,471] [,472] [,473] [,474] [,475] [,476] [,477] [,478] [1,] 945 946 949 952 953 954 955 956 961 962 [2,] 886 887 888 892 893 895 898 900 901 902 [3,] 902 903 906 907 909 911 912 913 917 918 [4,] 926 927 929 931 933 936 937 939 946 947 [5,] 936 937 938 939 941 944 946 947 951 953 [6,] 910 911 912 914 916 921 922 923 926 930 [,479] [,480] [,481] [,482] [,483] [,484] [,485] [,486] [,487] [,488] [1,] 963 965 969 972 974 975 976 977 979 981 [2,] 903 905 908 910 911 912 920 921 922 924 [3,] 922 924 929 930 932 933 934 935 937 939 [4,] 950 951 952 953 954 956 957 959 960 964 [5,] 954 961 962 963 965 970 974 976 979 980 [6,] 931 932 935 937 940 941 942 943 944 947 [,489] [,490] [,491] [,492] [,493] [,494] [,495] [,496] [,497] [,498] [1,] 986 989 990 992 994 997 1000 NA NA NA [2,] 925 926 929 931 932 934 936 937 938 939 [3,] 940 941 945 946 947 951 954 955 956 957 [4,] 965 966 969 971 973 978 983 984 985 986 [5,] 981 982 985 986 990 991 992 995 997 998 [6,] 948 951 953 954 956 958 961 962 963 964 [,499] [,500] [,501] [,502] [,503] [,504] [,505] [,506] [,507] [,508] [1,] NA NA NA NA NA NA NA NA NA NA [2,] 940 943 945 948 950 953 956 957 960 961 [3,] 958 960 962 963 966 967 968 970 972 973 [4,] 987 991 992 993 997 999 NA NA NA NA [5,] 999 1000 NA NA NA NA NA NA NA NA [6,] 965 967 968 970 973 974 977 978 981 982 [,509] [,510] [,511] [,512] [,513] [,514] [,515] [,516] [,517] [,518] [1,] NA NA NA NA NA NA NA NA NA NA [2,] 963 965 968 969 970 971 972 973 974 977 [3,] 974 975 978 980 983 984 985 986 990 991 [4,] NA NA NA NA NA NA NA NA NA NA [5,] NA NA NA NA NA NA NA NA NA NA [6,] 983 986 987 988 992 993 994 995 998 999 [,519] [,520] [,521] [,522] [,523] [,524] [,525] [,526] [,527] [,528] [1,] NA NA NA NA NA NA NA NA NA NA [2,] 978 982 983 984 985 986 987 988 989 991 [3,] 992 993 994 995 998 1000 NA NA NA NA [4,] NA NA NA NA NA NA NA NA NA NA [5,] NA NA NA NA NA NA NA NA NA NA [6,] 1000 NA NA NA NA NA NA NA NA NA [,529] [,530] [,531] [,532] [,533] [,534] [,535] [,536] [,537] [,538] [1,] NA NA NA NA NA NA NA NA NA NA [2,] 992 994 1000 NA NA NA NA NA NA NA [3,] NA NA NA NA NA NA NA NA NA NA [4,] NA NA NA NA NA NA NA NA NA NA [5,] NA NA NA NA NA NA NA NA NA NA [6,] NA NA NA NA NA NA NA NA NA NA [,539] [,540] [,541] [,542] [,543] [,544] [,545] [,546] [,547] [,548] [1,] NA NA NA NA NA NA NA NA NA NA [2,] NA NA NA NA NA NA NA NA NA NA [3,] NA NA NA NA NA NA NA NA NA NA [4,] NA NA NA NA NA NA NA NA NA NA [5,] NA NA NA NA NA NA NA NA NA NA [6,] NA NA NA NA NA NA NA NA NA NA [,549] [,550] [,551] [1,] NA NA NA [2,] NA NA NA [3,] NA NA NA [4,] NA NA NA [5,] NA NA NA [6,] NA NA NA done successfully.
Executing test function test.networkgen2 ... [1] "K in genNET: 6" [1] "K in genNET: 6" done successfully.
Executing test function test.networkgen_time ... Error in W2[[0]] : variable W2 doesn't exist In addition: Warning messages: 1: In RNGkind(kind = testSuite$rngKind, normal.kind = testSuite$rngNormalKind) : RNGkind: Marsaglia-Multicarry has poor statistical properties 2: In RNGkind(kind = testSuite$rngKind, normal.kind = testSuite$rngNormalKind) : RNGkind: severe deviations from normality for Kinderman-Ramage + Marsaglia-Multicarry 3: In RNGkind(kind = testSuite$rngKind, normal.kind = testSuite$rngNormalKind) : RNGkind: Marsaglia-Multicarry has poor statistical properties 4: In RNGkind(kind = testSuite$rngKind, normal.kind = testSuite$rngNormalKind) : RNGkind: severe deviations from normality for Kinderman-Ramage + Marsaglia-Multicarry 5: In (function (n, probs) : This function is deprecated, please use rcat.b1() instead. 6: In (function (n, probs) : This function is deprecated, please use rcat.b1() instead. Error : error while evaluating node F.W2_0 formula: W2[[0]]. Check syntax specification. Error in set.DAG(D) : ...attempt to simulate data from DAG failed... Error in F.W1[0:1][[0]] : ...network indexing variable evaluated to more than one column ... Error : error while evaluating node F.W1_2 formula: F.W1[0:1][[0]]. Check syntax specification. Error in set.DAG(D) : ...attempt to simulate data from DAG failed... Error : Undefined variable: F.A Error in set.DAG(D) : ...attempt to simulate data from DAG failed... done successfully.
Executing test function test.tKmaxnet ... Error in eval.nodeform.full(expr_call = expr_call, expr_str = expr_str, : reference A[...] at node B is not allowed; node A was defined as time-invariant Error in set.DAG(D.t) : ...attempt to simulate data from DAG failed... Error : Undefined variable: xyz Error in set.DAG(D.t) : ...attempt to simulate data from DAG failed... Error in eval.nodeform.full(expr_call = expr_call, expr_str = expr_str, : reference A[...] at node B is not allowed; node A was defined as time-invariant In addition: Warning message: In FUN(X[[i]], ...) : the argument inside [...] cannot be parsed: A[rnorm] Error in set.DAG(D.t) : ...attempt to simulate data from DAG failed... done successfully.
Executing test function test.MV ... Loading required package: mvtnorm ...automatically assigning order attribute to some nodes... node X1.X2.X3, order:1 node Y1.Y2, order:2 node A, order:3 Loading required package: copula
Attaching package: 'copula'
The following object is masked from 'package:simcausal':
ADon't know the expression result type S4FALSE Don't know the expression result type S4FALSE Don't know the expression result type S4FALSE Don't know the expression result type S4FALSE Don't know the expression result type S4FALSE Don't know the expression result type S4FALSE Don't know the expression result type S4FALSE Don't know the expression result type S4FALSE Don't know the expression result type S4FALSE Don't know the expression result type S4FALSE [1] "current list of user-defined vectorized functions: qbinom" Loading required package: bindata done successfully.
Executing test function test.NSEbug ... [1] "s" [1] 1 [1] "t" [1] 0 [1] "variable" [1] "(Intercept)" [1] "s" [1] 1 [1] "t" [1] 0 [1] "variable" [1] "mse" [1] "s" [1] 2 [1] "t" [1] 0 [1] "variable" [1] "(Intercept)" [1] "s" [1] 2 [1] "t" [1] 0 [1] "variable" [1] "mse" existing node L1_0 was modified [1] "s" [1] 3 [1] "t" [1] 0 [1] "variable" [1] "(Intercept)" [1] "s" [1] 3 [1] "t" [1] 0 [1] "variable" [1] "mse" existing node L1_0 was modified [1] "s" [1] 1 [1] "t" [1] 1 [1] "variable" [1] "(Intercept)" [1] "s" [1] 1 [1] "t" [1] 1 [1] "variable" [1] "mse" [1] "s" [1] 2 [1] "t" [1] 1 [1] "variable" [1] "(Intercept)" [1] "s" [1] 2 [1] "t" [1] 1 [1] "variable" [1] "mse" [1] "s" [1] 3 [1] "t" [1] 1 [1] "variable" [1] "(Intercept)" [1] "s" [1] 3 [1] "t" [1] 1 [1] "variable" [1] "mse" [1] "s" [1] 1 [1] "t" [1] 2 [1] "variable" [1] "(Intercept)" [1] "s" [1] 1 [1] "t" [1] 2 [1] "variable" [1] "mse" existing node L1_2 was modified [1] "s" [1] 2 [1] "t" [1] 2 [1] "variable" [1] "(Intercept)" [1] "s" [1] 2 [1] "t" [1] 2 [1] "variable" [1] "mse" [1] "s" [1] 3 [1] "t" [1] 2 [1] "variable" [1] "(Intercept)" [1] "s" [1] 3 [1] "t" [1] 2 [1] "variable" [1] "mse" done successfully.
------------------- UNIT TEST SUMMARY ---------------------
RUNIT TEST PROTOCOL -- Thu Jul 18 09:11:53 2024
Number of test functions: 28 Number of errors: 1 Number of failures: 0
1 Test Suite : simcausal unit testing - 28 test functions, 1 error, 0 failures ERROR in test.latent: Error :
from()was deprecated in igraph 2.0.4 and is now defunct. ℹ Please use.from()instead. Error:unit testing failed (#test failures: 0, #R errors: 1)
In addition: Warning messages: 1: In RNGkind(kind = testSuite$rngKind, normal.kind = testSuite$rngNormalKind) : RNGkind: Marsaglia-Multicarry has poor statistical properties 2: In RNGkind(kind = testSuite$rngKind, normal.kind = testSuite$rngNormalKind) : RNGkind: severe deviations from normality for Kinderman-Ramage + Marsaglia-Multicarry 3: In RNGkind(kind = testSuite$rngKind, normal.kind = testSuite$rngNormalKind) : RNGkind: Marsaglia-Multicarry has poor statistical properties 4: In RNGkind(kind = testSuite$rngKind, normal.kind = testSuite$rngNormalKind) : RNGkind: severe deviations from normality for Kinderman-Ramage + Marsaglia-Multicarry Execution halted ```
-
In both
-
checking re-building of vignette outputs ... WARNING
`` Error(s) in re-building vignettes: ... --- re-building ‘simcausal_vignette.Rnw’ using knitr Error: processing vignette 'simcausal_vignette.Rnw' failed with diagnostics: Running 'texi2dvi' on 'simcausal_vignette.tex' failed. LaTeX errors: ! LaTeX Error: Filecolortbl.sty' not found.Type X to quit or to proceed, or enter new name. (Default extension: sty)
! Emergency stop.
l.55 \RequirePackage [T1]{fontenc}^^M ! ==> Fatal error occurred, no output PDF file produced! --- failed re-building ‘simcausal_vignette.Rnw’
SUMMARY: processing the following file failed: ‘simcausal_vignette.Rnw’
Error: Vignette re-building failed. Execution halted ```
skynet
- Version: 1.4.3
- GitHub: https://github.com/ropensci/skynet
- Source code: https://github.com/cran/skynet
- Date/Publication: 2022-06-17 13:00:02 UTC
- Number of recursive dependencies: 98
Run revdepcheck::cloud_details(, "skynet") for more info
Newly broken
-
checking tests ... ERROR ``` Running ‘testthat.R’ Running the tests in ‘tests/testthat.R’ failed. Complete output: > library(testthat) > library(skynet) > > test_check("skynet") [ FAIL 1 | WARN 24 | SKIP 4 | PASS 69 ]
══ Skipped tests (4) ═══════════════════════════════════════════════════════════ • On CRAN (4): 'test_download_db1b.R:6:3', 'test_download_ontime.R:6:3', 'test_download_t100.R:6:3', 'test_download_t100.R:13:3'
══ Failed tests ════════════════════════════════════════════════════════════════ ── Error ('test_smallerfunctions.R:16:3'): From To function ──────────────────── Error:
from()was deprecated in igraph 2.0.4 and is now defunct. ℹ Please use.from()instead. Backtrace: ▆ 1. ├─testthat::expect_output(...) at test_smallerfunctions.R:16:3 2. │ └─testthat:::quasi_capture(...) 3. │ ├─testthat (local) .capture(...) 4. │ │ └─testthat::capture_output_lines(code, print, width = width) 5. │ │ └─testthat:::eval_with_output(code, print = print, width = width) 6. │ │ ├─withr::with_output_sink(path, withVisible(code)) 7. │ │ │ └─base::force(code) 8. │ │ └─base::withVisible(code) 9. │ └─rlang::eval_bare(quo_get_expr(.quo), quo_get_env(.quo)) 10. ├─base::print(from_to_stats(test$gDir, "JFK", orig = "from")) 11. └─skynet::from_to_stats(test$gDir, "JFK", orig = "from") 12. ├─E(x)[from(V(x)[y])] 13. └─igraph:::[.igraph.es(E(x), from(V(x)[y])) 14. └─igraph:::lazy_eval(...) 15. └─base::lapply(x, lazy_eval, data = data) 16. └─igraph (local) FUN(X[[i]], ...) 17. └─base::eval(x$expr, data, x$env) 18. └─base::eval(x$expr, data, x$env) 19. └─igraph (local) from(V(x)[y]) 20. └─lifecycle::deprecate_stop("2.0.4", "from()", ".from()") 21. └─lifecycle:::deprecate_stop0(msg) 22. └─rlang::cnd_signal(...)[ FAIL 1 | WARN 24 | SKIP 4 | PASS 69 ] Error: Test failures Execution halted ```
In both
- checking dependencies in R code ... NOTE
Namespace in Imports field not imported from: ‘maps’ All declared Imports should be used.
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