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tests/testthat/_snaps/TreeMineR.md
In TreeMineR: Tree-Based Scan Statistics
Check sequential test run
Code
head(TreeMineR(data = diagnoses, tree = icd_10_se, p = 1 / 11, n_exposed = 1000,
n_unexposed = 10000, n_monte_carlo_sim = 10, random_seed = 1234), 10)
Output
cut n1 n0 risk1 risk0 RR llr p
1 12 122 669 0.122 0.0669 1.823617 16.187145 0.09090909
2 11 132 782 0.132 0.0782 1.687980 13.657855 0.09090909
3 V01-X59 241 1687 0.241 0.1687 1.428571 12.268163 0.09090909
4 V01-V99 210 1438 0.210 0.1438 1.460362 11.957318 0.09090909
5 15 133 822 0.133 0.0822 1.618005 11.797748 0.09090909
6 19 306 2281 0.306 0.2281 1.341517 10.806142 0.09090909
7 02 207 1452 0.207 0.1452 1.425620 10.423109 0.09090909
8 18 114 712 0.114 0.0712 1.601124 9.711722 0.09090909
9 01 207 1483 0.207 0.1483 1.395819 9.287006 0.09090909
10 16 109 690 0.109 0.0690 1.579710 8.803335 0.09090909
Check parallel test run
Code
head(TreeMineR(data = diagnoses, tree = icd_10_se, p = 1 / 11, n_exposed = 1000,
n_unexposed = 10000, n_monte_carlo_sim = 20, random_seed = 124, future_control = list(
"multisession", workers = 2)), 10)
Output
cut n1 n0 risk1 risk0 RR llr p
1 12 122 669 0.122 0.0669 1.823617 16.187145 0.04761905
2 11 132 782 0.132 0.0782 1.687980 13.657855 0.04761905
3 V01-X59 241 1687 0.241 0.1687 1.428571 12.268163 0.04761905
4 V01-V99 210 1438 0.210 0.1438 1.460362 11.957318 0.09523810
5 15 133 822 0.133 0.0822 1.618005 11.797748 0.09523810
6 19 306 2281 0.306 0.2281 1.341517 10.806142 0.09523810
7 02 207 1452 0.207 0.1452 1.425620 10.423109 0.09523810
8 18 114 712 0.114 0.0712 1.601124 9.711722 0.09523810
9 01 207 1483 0.207 0.1483 1.395819 9.287006 0.09523810
10 16 109 690 0.109 0.0690 1.579710 8.803335 0.09523810
Test the use of titles
Code
head(TreeMineR(data = diagnoses, tree = icd_10_se, p = 1 / 11, n_exposed = 1000,
n_unexposed = 10000, dictionary = icd_10_se_dict, n_monte_carlo_sim = 20,
random_seed = 124), 10)
Output
title
1 Hudens och underhudens sjukdomar (L00-L99)
2 Matsmaltningsorganens sjukdomar (K00-K93)
3 Olycksfall
4 Transportolyckor
5 Graviditet, forlossning och barnsangstid (O00-O99)
6 Skador, forgiftningar och vissa andra foljder av yttre orsaker (S00-T98)
7 Tumorer (C00-D48)
8 Symtom, sjukdomstecken och onormala kliniska fynd och laboratoriefynd som ej klassificeras pa annan plats (R00-R99)
9 Vissa infektionssjukdomar och parasitsjukdomar (A00-B99)
10 Vissa perinatala tillstand (P00-P96)
cut n1 n0 risk1 risk0 RR llr p
1 12 122 669 0.122 0.0669 1.823617 16.187145 0.04761905
2 11 132 782 0.132 0.0782 1.687980 13.657855 0.04761905
3 V01-X59 241 1687 0.241 0.1687 1.428571 12.268163 0.04761905
4 V01-V99 210 1438 0.210 0.1438 1.460362 11.957318 0.09523810
5 15 133 822 0.133 0.0822 1.618005 11.797748 0.09523810
6 19 306 2281 0.306 0.2281 1.341517 10.806142 0.09523810
7 02 207 1452 0.207 0.1452 1.425620 10.423109 0.09523810
8 18 114 712 0.114 0.0712 1.601124 9.711722 0.09523810
9 01 207 1483 0.207 0.1483 1.395819 9.287006 0.09523810
10 16 109 690 0.109 0.0690 1.579710 8.803335 0.09523810
Test out put if no number of individuals is specified
Code
head(TreeMineR(data = diagnoses, tree = icd_10_se, p = 1 / 11, dictionary = icd_10_se_dict,
n_monte_carlo_sim = 20, random_seed = 124), 10)
Output
title
1 Hudens och underhudens sjukdomar (L00-L99)
2 Matsmaltningsorganens sjukdomar (K00-K93)
3 Olycksfall
4 Transportolyckor
5 Graviditet, forlossning och barnsangstid (O00-O99)
6 Skador, forgiftningar och vissa andra foljder av yttre orsaker (S00-T98)
7 Tumorer (C00-D48)
8 Symtom, sjukdomstecken och onormala kliniska fynd och laboratoriefynd som ej klassificeras pa annan plats (R00-R99)
9 Vissa infektionssjukdomar och parasitsjukdomar (A00-B99)
10 Vissa perinatala tillstand (P00-P96)
cut n1 n0 llr p
1 12 122 669 16.187145 0.04761905
2 11 132 782 13.657855 0.04761905
3 V01-X59 241 1687 12.268163 0.04761905
4 V01-V99 210 1438 11.957318 0.09523810
5 15 133 822 11.797748 0.09523810
6 19 306 2281 10.806142 0.09523810
7 02 207 1452 10.423109 0.09523810
8 18 114 712 9.711722 0.09523810
9 01 207 1483 9.287006 0.09523810
10 16 109 690 8.803335 0.09523810
Test return of test distribution
Code
temp <- TreeMineR(data = diagnoses, tree = icd_10_se, p = 1 / 11, dictionary = icd_10_se_dict,
n_monte_carlo_sim = 20, random_seed = 124, return_test_dist = TRUE)
head(temp$test_dist, 10)
Output
iteration max_llr
1 2 12.208531
2 3 5.039655
3 4 6.866651
4 5 5.097155
5 6 7.184879
6 7 5.149988
7 8 5.039655
8 9 5.963116
9 10 5.534555
10 11 5.777970
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TreeMineR documentation built on April 12, 2025, 1:30 a.m.
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Check sequential test run
Code
head(TreeMineR(data = diagnoses, tree = icd_10_se, p = 1 / 11, n_exposed = 1000,
n_unexposed = 10000, n_monte_carlo_sim = 10, random_seed = 1234), 10)
Output
cut n1 n0 risk1 risk0 RR llr p
1 12 122 669 0.122 0.0669 1.823617 16.187145 0.09090909
2 11 132 782 0.132 0.0782 1.687980 13.657855 0.09090909
3 V01-X59 241 1687 0.241 0.1687 1.428571 12.268163 0.09090909
4 V01-V99 210 1438 0.210 0.1438 1.460362 11.957318 0.09090909
5 15 133 822 0.133 0.0822 1.618005 11.797748 0.09090909
6 19 306 2281 0.306 0.2281 1.341517 10.806142 0.09090909
7 02 207 1452 0.207 0.1452 1.425620 10.423109 0.09090909
8 18 114 712 0.114 0.0712 1.601124 9.711722 0.09090909
9 01 207 1483 0.207 0.1483 1.395819 9.287006 0.09090909
10 16 109 690 0.109 0.0690 1.579710 8.803335 0.09090909
Check parallel test run
Code
head(TreeMineR(data = diagnoses, tree = icd_10_se, p = 1 / 11, n_exposed = 1000,
n_unexposed = 10000, n_monte_carlo_sim = 20, random_seed = 124, future_control = list(
"multisession", workers = 2)), 10)
Output
cut n1 n0 risk1 risk0 RR llr p
1 12 122 669 0.122 0.0669 1.823617 16.187145 0.04761905
2 11 132 782 0.132 0.0782 1.687980 13.657855 0.04761905
3 V01-X59 241 1687 0.241 0.1687 1.428571 12.268163 0.04761905
4 V01-V99 210 1438 0.210 0.1438 1.460362 11.957318 0.09523810
5 15 133 822 0.133 0.0822 1.618005 11.797748 0.09523810
6 19 306 2281 0.306 0.2281 1.341517 10.806142 0.09523810
7 02 207 1452 0.207 0.1452 1.425620 10.423109 0.09523810
8 18 114 712 0.114 0.0712 1.601124 9.711722 0.09523810
9 01 207 1483 0.207 0.1483 1.395819 9.287006 0.09523810
10 16 109 690 0.109 0.0690 1.579710 8.803335 0.09523810
Test the use of titles
Code
head(TreeMineR(data = diagnoses, tree = icd_10_se, p = 1 / 11, n_exposed = 1000,
n_unexposed = 10000, dictionary = icd_10_se_dict, n_monte_carlo_sim = 20,
random_seed = 124), 10)
Output
title
1 Hudens och underhudens sjukdomar (L00-L99)
2 Matsmaltningsorganens sjukdomar (K00-K93)
3 Olycksfall
4 Transportolyckor
5 Graviditet, forlossning och barnsangstid (O00-O99)
6 Skador, forgiftningar och vissa andra foljder av yttre orsaker (S00-T98)
7 Tumorer (C00-D48)
8 Symtom, sjukdomstecken och onormala kliniska fynd och laboratoriefynd som ej klassificeras pa annan plats (R00-R99)
9 Vissa infektionssjukdomar och parasitsjukdomar (A00-B99)
10 Vissa perinatala tillstand (P00-P96)
cut n1 n0 risk1 risk0 RR llr p
1 12 122 669 0.122 0.0669 1.823617 16.187145 0.04761905
2 11 132 782 0.132 0.0782 1.687980 13.657855 0.04761905
3 V01-X59 241 1687 0.241 0.1687 1.428571 12.268163 0.04761905
4 V01-V99 210 1438 0.210 0.1438 1.460362 11.957318 0.09523810
5 15 133 822 0.133 0.0822 1.618005 11.797748 0.09523810
6 19 306 2281 0.306 0.2281 1.341517 10.806142 0.09523810
7 02 207 1452 0.207 0.1452 1.425620 10.423109 0.09523810
8 18 114 712 0.114 0.0712 1.601124 9.711722 0.09523810
9 01 207 1483 0.207 0.1483 1.395819 9.287006 0.09523810
10 16 109 690 0.109 0.0690 1.579710 8.803335 0.09523810
Test out put if no number of individuals is specified
Code
head(TreeMineR(data = diagnoses, tree = icd_10_se, p = 1 / 11, dictionary = icd_10_se_dict,
n_monte_carlo_sim = 20, random_seed = 124), 10)
Output
title
1 Hudens och underhudens sjukdomar (L00-L99)
2 Matsmaltningsorganens sjukdomar (K00-K93)
3 Olycksfall
4 Transportolyckor
5 Graviditet, forlossning och barnsangstid (O00-O99)
6 Skador, forgiftningar och vissa andra foljder av yttre orsaker (S00-T98)
7 Tumorer (C00-D48)
8 Symtom, sjukdomstecken och onormala kliniska fynd och laboratoriefynd som ej klassificeras pa annan plats (R00-R99)
9 Vissa infektionssjukdomar och parasitsjukdomar (A00-B99)
10 Vissa perinatala tillstand (P00-P96)
cut n1 n0 llr p
1 12 122 669 16.187145 0.04761905
2 11 132 782 13.657855 0.04761905
3 V01-X59 241 1687 12.268163 0.04761905
4 V01-V99 210 1438 11.957318 0.09523810
5 15 133 822 11.797748 0.09523810
6 19 306 2281 10.806142 0.09523810
7 02 207 1452 10.423109 0.09523810
8 18 114 712 9.711722 0.09523810
9 01 207 1483 9.287006 0.09523810
10 16 109 690 8.803335 0.09523810
Test return of test distribution
Code
temp <- TreeMineR(data = diagnoses, tree = icd_10_se, p = 1 / 11, dictionary = icd_10_se_dict,
n_monte_carlo_sim = 20, random_seed = 124, return_test_dist = TRUE)
head(temp$test_dist, 10)
Output
iteration max_llr
1 2 12.208531
2 3 5.039655
3 4 6.866651
4 5 5.097155
5 6 7.184879
6 7 5.149988
7 8 5.039655
8 9 5.963116
9 10 5.534555
10 11 5.777970
Try the TreeMineR package in your browser
Any scripts or data that you put into this service are public.
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.
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