Wald: Wald and score tests for RSiena results
In RSiena: Siena - Simulation Investigation for Empirical Network Analysis
Description
Usage
Arguments
Details
Value
Author(s)
References
See Also
Examples
Description
These functions compute Wald-type and score-type tests for results
estimated by siena07.
Usage
1
2
3
4
5
Wald.RSiena(A, ans)
Multipar.RSiena(ans, ...)
score.Test(ans, test=ans$test)
Arguments
A
A k * p matrix, where p = ans$pp,
the number of parameters in ans excluding the basic rate parameters
used for conditional estimation.
ans
An object of class sienaFit,
resulting from a call to siena07.
...
One or more integer numbers between 1 and p, specifying
the tested effects
(numbered as in print(ans); if conditional estimation was used, numbered
as the 'Other parameters').
test
One or more integer numbers between 1 and p,
or a logical vector of length p; these should specify the tested
effects (numbered as described for the ...).
Details
The hypothesis tested by Wald.RSiena
is Aθ = 0, where θ is
the parameter estimated in the process leading to ans.
The hypothesis tested by Multipar.RSiena is that all
parameters given in … are 0. This is a special case of
the preceding.
The tested effects for score.Test should have been specified
in includeEffects or setEffect with
fix=TRUE, test=TRUE, i.e., they should not have been estimated.
The hypothesis tested by score.Test is that the tested parameters have
the value indicated in the effects object used for obtaining ans.
These tests should be carried out only when convergence is adequate
(overall maximum convergence ratio less than 0.25
and all t-ratios for convergence less than 0.1 in absolute value).
These functions have their own print method, see print.sienaTest.
Value
An object of class sienaTest, which is a list with elements:
chisquare: The test statistic, assumed to have a chi-squared null
distribution.
df: The degrees of freedom.
pvalue: The associated p-value.
onesided: For df=1, the onesided test statistic.
efnames: For Multipar.RSiena and score.Test, the names
of the tested effects.
Author(s)
Tom Snijders
References
See the manual and http://www.stats.ox.ac.uk/~snijders/siena/
See Also
Examples
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mynet <- sienaDependent(array(c(s501, s502), dim=c(50, 50, 2)))
mydata <- sienaDataCreate(mynet)
myeff <- getEffects(mydata)
myalgorithm <- sienaAlgorithmCreate(nsub=1, n3=40, seed=1777, projname=NULL)
# nsub=1 and n3=40 is used here for having a brief computation,
# not for practice.
myeff <- includeEffects(myeff, transTrip, transTies)
myeff <- includeEffects(myeff, outAct, outPop, fix=TRUE, test=TRUE)
(ans <- siena07(myalgorithm, data=mydata, effects=myeff, batch=TRUE))
A <- matrix(0, 2, 6)
A[1, 3] <- 1
A[2, 4] <- 1
wa <- Wald.RSiena(A, ans)
wa
# A shortcut for the above is:
Multipar.RSiena(ans, 3, 4)
# The following two are equivalent:
sct <- score.Test(ans, c(FALSE, FALSE, FALSE, FALSE, FALSE, TRUE))
sct <- score.Test(ans,6)
print(sct)
# Getting all 1-df score tests separately:
# (More identifying information for the effects may be added as necessary)
for (i in which(ans$test)){
sct <- score.Test(ans,i)
cat(ans$requestedEffects$effectName[i], '\n')
print(sct)}
# Testing that endowment and creation effects are identical:
myeff1 <- getEffects(mydata)
myeff1 <- includeEffects(myeff1, recip, include=FALSE)
myeff1 <- includeEffects(myeff1, recip, type='creation')
(myeff1 <- includeEffects(myeff1, recip, type='endow'))
(ans1 <- siena07(myalgorithm, data=mydata, effects=myeff1, batch=TRUE))
A <- matrix(c(0,1,-1), 1, 3)
(Wald.RSiena(A, ans1))
Example output
Warning message:
no DISPLAY variable so Tk is not available
siena07 will create an output file /work/tmp/tmp/RtmpAh9EaR/Siena168b4894d5d53.txt .
This is a temporary file for this R session.
effectName include fix test initialValue parm
1 transitive triplets TRUE FALSE FALSE 0 0
2 transitive ties TRUE FALSE FALSE 0 0
effectName include fix test initialValue parm
1 outdegree - popularity TRUE TRUE TRUE 0 0
2 outdegree - activity TRUE TRUE TRUE 0 0
Start phase 0
theta: -1.49 0.00 0.00 0.00 0.00 0.00
Start phase 1
Phase 1 Iteration 1 Progress: 0%
Phase 1 Iteration 2 Progress: 0%
Phase 1 Iteration 3 Progress: 0%
Phase 1 Iteration 4 Progress: 1%
Phase 1 Iteration 5 Progress: 1%
Phase 1 Iteration 10 Progress: 2%
Phase 1 Iteration 15 Progress: 2%
Phase 1 Iteration 20 Progress: 3%
Phase 1 Iteration 25 Progress: 4%
Phase 1 Iteration 30 Progress: 5%
Phase 1 Iteration 35 Progress: 6%
Phase 1 Iteration 40 Progress: 6%
Phase 1 Iteration 45 Progress: 7%
Phase 1 Iteration 50 Progress: 8%
theta: -1.750 0.437 0.650 -0.477 0.000 0.000
Start phase 2.1
Phase 2 Subphase 1 Iteration 1 Progress: 56%
Phase 2 Subphase 1 Iteration 2 Progress: 57%
theta -1.874 0.671 0.830 -0.362 0.000 0.000
ac 0.00378 3.79486 -1.06273 -3.09687 -0.05719 0.12148
Phase 2 Subphase 1 Iteration 3 Progress: 57%
Phase 2 Subphase 1 Iteration 4 Progress: 57%
theta -2.1545 1.2009 0.6376 0.0483 0.0000 0.0000
ac 0.235 2.246 -1.056 -2.625 0.220 0.408
Phase 2 Subphase 1 Iteration 5 Progress: 57%
Phase 2 Subphase 1 Iteration 6 Progress: 57%
theta -2.454 1.711 0.230 0.637 0.000 0.000
ac 0.225 1.826 -1.110 -2.672 0.246 0.472
Phase 2 Subphase 1 Iteration 7 Progress: 57%
Phase 2 Subphase 1 Iteration 8 Progress: 58%
theta -2.621 1.956 0.115 0.669 0.000 0.000
ac -0.2214 0.0523 -1.0209 -1.1194 -0.0999 0.2100
Phase 2 Subphase 1 Iteration 9 Progress: 58%
Phase 2 Subphase 1 Iteration 10 Progress: 58%
theta -2.6549 2.1076 0.0503 0.6892 0.0000 0.0000
ac -0.298 -0.225 -1.081 -1.250 -0.230 0.104
theta -2.620 2.096 0.307 0.540 0.000 0.000
ac -0.4355 -0.5384 -0.7333 -0.9225 -0.1641 -0.0898
theta: -2.620 2.096 0.307 0.540 0.000 0.000
Start phase 3
Estimates, standard errors and convergence t-ratios
Estimate Standard Convergence
Error t-ratio
Rate parameters:
0 Rate parameter 6.6977 ( 1.0182 )
Other parameters:
1. eval outdegree (density) -2.6200 ( 0.1514 ) -0.4627
2. eval reciprocity 2.0958 ( 0.2976 ) -0.5244
3. eval transitive triplets 0.3066 ( 0.2922 ) -0.8100
4. eval transitive ties 0.5397 ( 0.4471 ) -0.6856
5. eval outdegree - popularity 0.0000 ( NA ) 0.1821
6. eval outdegree - activity 0.0000 ( NA ) 0.1142
Overall maximum convergence ratio: 0.9363
Score test for 2 parameters:
chi-squared = 6.33, p = 0.0422.
Total of 122 iteration steps.
chi-squared = 25.49, d.f. = 2; p < 0.001.
Tested effects:
mynet: transitive triplets
mynet: transitive ties
chi-squared = 25.49, d.f. = 2; p < 0.001.
Tested effects:
mynet: outdegree - activity
chi-squared = 3.56, d.f. = 1; one-sided Z = -1.89; p = 0.059.
outdegree - popularity
Tested effects:
mynet: outdegree - popularity
chi-squared = 6.10, d.f. = 1; one-sided Z = -2.47; p = 0.014.
outdegree - activity
Tested effects:
mynet: outdegree - activity
chi-squared = 3.56, d.f. = 1; one-sided Z = -1.89; p = 0.059.
[1] effectName include fix test initialValue
[6] parm
<0 rows> (or 0-length row.names)
effectName include fix test initialValue parm type
1 reciprocity TRUE FALSE FALSE 0 0 creation
effectName include fix test initialValue parm type
1 reciprocity TRUE FALSE FALSE 0 0 endow
effectName include fix test initialValue parm type
1 basic rate parameter mynet TRUE FALSE FALSE 4.69604 0 rate
2 outdegree (density) TRUE FALSE FALSE -1.48852 0 eval
3 reciprocity TRUE FALSE FALSE 0.00000 0 endow
4 reciprocity TRUE FALSE FALSE 0.00000 0 creation
Start phase 0
theta: -1.49 0.00 0.00
Start phase 1
Phase 1 Iteration 1 Progress: 0%
Phase 1 Iteration 2 Progress: 0%
Phase 1 Iteration 3 Progress: 1%
Phase 1 Iteration 4 Progress: 1%
Phase 1 Iteration 5 Progress: 1%
Phase 1 Iteration 10 Progress: 2%
Phase 1 Iteration 15 Progress: 3%
Phase 1 Iteration 20 Progress: 4%
Phase 1 Iteration 25 Progress: 5%
Phase 1 Iteration 30 Progress: 6%
Phase 1 Iteration 35 Progress: 8%
Phase 1 Iteration 40 Progress: 9%
Phase 1 Iteration 45 Progress: 10%
Phase 1 Iteration 50 Progress: 11%
theta: -1.5212 -0.0961 1.0000
Start phase 2.1
Phase 2 Subphase 1 Iteration 1 Progress: 43%
Phase 2 Subphase 1 Iteration 2 Progress: 43%
theta -1.568 -0.177 2.396
ac 0.579 6.448 2.755
Phase 2 Subphase 1 Iteration 3 Progress: 44%
Phase 2 Subphase 1 Iteration 4 Progress: 44%
theta -1.767 -0.109 4.356
ac 0.836 0.913 1.358
Phase 2 Subphase 1 Iteration 5 Progress: 44%
Phase 2 Subphase 1 Iteration 6 Progress: 44%
theta -1.977 0.129 4.282
ac 0.895 -0.346 1.139
Phase 2 Subphase 1 Iteration 7 Progress: 45%
Phase 2 Subphase 1 Iteration 8 Progress: 45%
theta -2.080 0.569 2.915
ac 0.770 0.148 0.443
Phase 2 Subphase 1 Iteration 9 Progress: 45%
Phase 2 Subphase 1 Iteration 10 Progress: 45%
theta -2.102 0.581 4.549
ac 0.8067 0.1411 0.0306
theta -2.066 0.971 3.478
ac -0.192 -0.012 -0.608
theta: -2.066 0.971 3.478
Start phase 3
Estimates, standard errors and convergence t-ratios
Estimate Standard Convergence
Error t-ratio
Rate parameters:
0 Rate parameter 4.3298 ( 0.4866 )
Other parameters:
1. eval outdegree (density) -2.0655 ( 0.2415 ) 0.1263
2. endow reciprocity 0.9706 ( 1.0712 ) 0.0339
3. creat reciprocity 3.4778 ( 0.4926 ) 0.0876
Overall maximum convergence ratio: 0.1794
Total of 218 iteration steps.
chi-squared = 4.24, d.f. = 1; one-sided Z = -2.06; p = 0.039.
RSiena documentation built on Sept. 24, 2020, 3 p.m.
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Description Usage Arguments Details Value Author(s) References See Also Examples
Description
These functions compute Wald-type and score-type tests for results estimated by siena07.
Usage
1 2 3 4 5 | Wald.RSiena(A, ans)
Multipar.RSiena(ans, ...)
score.Test(ans, test=ans$test)
|
Arguments
A |
A |
ans |
An object of class |
... |
One or more integer numbers between 1 and |
test |
One or more integer numbers between 1 and |
Details
The hypothesis tested by Wald.RSiena
is Aθ = 0, where θ is
the parameter estimated in the process leading to ans.
The hypothesis tested by Multipar.RSiena is that all
parameters given in … are 0. This is a special case of
the preceding.
The tested effects for score.Test should have been specified
in includeEffects or setEffect with
fix=TRUE, test=TRUE, i.e., they should not have been estimated.
The hypothesis tested by score.Test is that the tested parameters have
the value indicated in the effects object used for obtaining ans.
These tests should be carried out only when convergence is adequate (overall maximum convergence ratio less than 0.25 and all t-ratios for convergence less than 0.1 in absolute value).
These functions have their own print method, see print.sienaTest.
Value
An object of class sienaTest, which is a list with elements:
chisquare: The test statistic, assumed to have a chi-squared null distribution.
df: The degrees of freedom.
pvalue: The associated p-value.
onesided: For
df=1, the onesided test statistic.efnames: For
Multipar.RSienaandscore.Test, the names of the tested effects.
Author(s)
Tom Snijders
References
See the manual and http://www.stats.ox.ac.uk/~snijders/siena/
See Also
Examples
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 | mynet <- sienaDependent(array(c(s501, s502), dim=c(50, 50, 2)))
mydata <- sienaDataCreate(mynet)
myeff <- getEffects(mydata)
myalgorithm <- sienaAlgorithmCreate(nsub=1, n3=40, seed=1777, projname=NULL)
# nsub=1 and n3=40 is used here for having a brief computation,
# not for practice.
myeff <- includeEffects(myeff, transTrip, transTies)
myeff <- includeEffects(myeff, outAct, outPop, fix=TRUE, test=TRUE)
(ans <- siena07(myalgorithm, data=mydata, effects=myeff, batch=TRUE))
A <- matrix(0, 2, 6)
A[1, 3] <- 1
A[2, 4] <- 1
wa <- Wald.RSiena(A, ans)
wa
# A shortcut for the above is:
Multipar.RSiena(ans, 3, 4)
# The following two are equivalent:
sct <- score.Test(ans, c(FALSE, FALSE, FALSE, FALSE, FALSE, TRUE))
sct <- score.Test(ans,6)
print(sct)
# Getting all 1-df score tests separately:
# (More identifying information for the effects may be added as necessary)
for (i in which(ans$test)){
sct <- score.Test(ans,i)
cat(ans$requestedEffects$effectName[i], '\n')
print(sct)}
# Testing that endowment and creation effects are identical:
myeff1 <- getEffects(mydata)
myeff1 <- includeEffects(myeff1, recip, include=FALSE)
myeff1 <- includeEffects(myeff1, recip, type='creation')
(myeff1 <- includeEffects(myeff1, recip, type='endow'))
(ans1 <- siena07(myalgorithm, data=mydata, effects=myeff1, batch=TRUE))
A <- matrix(c(0,1,-1), 1, 3)
(Wald.RSiena(A, ans1))
|
Example output
Warning message:
no DISPLAY variable so Tk is not available
siena07 will create an output file /work/tmp/tmp/RtmpAh9EaR/Siena168b4894d5d53.txt .
This is a temporary file for this R session.
effectName include fix test initialValue parm
1 transitive triplets TRUE FALSE FALSE 0 0
2 transitive ties TRUE FALSE FALSE 0 0
effectName include fix test initialValue parm
1 outdegree - popularity TRUE TRUE TRUE 0 0
2 outdegree - activity TRUE TRUE TRUE 0 0
Start phase 0
theta: -1.49 0.00 0.00 0.00 0.00 0.00
Start phase 1
Phase 1 Iteration 1 Progress: 0%
Phase 1 Iteration 2 Progress: 0%
Phase 1 Iteration 3 Progress: 0%
Phase 1 Iteration 4 Progress: 1%
Phase 1 Iteration 5 Progress: 1%
Phase 1 Iteration 10 Progress: 2%
Phase 1 Iteration 15 Progress: 2%
Phase 1 Iteration 20 Progress: 3%
Phase 1 Iteration 25 Progress: 4%
Phase 1 Iteration 30 Progress: 5%
Phase 1 Iteration 35 Progress: 6%
Phase 1 Iteration 40 Progress: 6%
Phase 1 Iteration 45 Progress: 7%
Phase 1 Iteration 50 Progress: 8%
theta: -1.750 0.437 0.650 -0.477 0.000 0.000
Start phase 2.1
Phase 2 Subphase 1 Iteration 1 Progress: 56%
Phase 2 Subphase 1 Iteration 2 Progress: 57%
theta -1.874 0.671 0.830 -0.362 0.000 0.000
ac 0.00378 3.79486 -1.06273 -3.09687 -0.05719 0.12148
Phase 2 Subphase 1 Iteration 3 Progress: 57%
Phase 2 Subphase 1 Iteration 4 Progress: 57%
theta -2.1545 1.2009 0.6376 0.0483 0.0000 0.0000
ac 0.235 2.246 -1.056 -2.625 0.220 0.408
Phase 2 Subphase 1 Iteration 5 Progress: 57%
Phase 2 Subphase 1 Iteration 6 Progress: 57%
theta -2.454 1.711 0.230 0.637 0.000 0.000
ac 0.225 1.826 -1.110 -2.672 0.246 0.472
Phase 2 Subphase 1 Iteration 7 Progress: 57%
Phase 2 Subphase 1 Iteration 8 Progress: 58%
theta -2.621 1.956 0.115 0.669 0.000 0.000
ac -0.2214 0.0523 -1.0209 -1.1194 -0.0999 0.2100
Phase 2 Subphase 1 Iteration 9 Progress: 58%
Phase 2 Subphase 1 Iteration 10 Progress: 58%
theta -2.6549 2.1076 0.0503 0.6892 0.0000 0.0000
ac -0.298 -0.225 -1.081 -1.250 -0.230 0.104
theta -2.620 2.096 0.307 0.540 0.000 0.000
ac -0.4355 -0.5384 -0.7333 -0.9225 -0.1641 -0.0898
theta: -2.620 2.096 0.307 0.540 0.000 0.000
Start phase 3
Estimates, standard errors and convergence t-ratios
Estimate Standard Convergence
Error t-ratio
Rate parameters:
0 Rate parameter 6.6977 ( 1.0182 )
Other parameters:
1. eval outdegree (density) -2.6200 ( 0.1514 ) -0.4627
2. eval reciprocity 2.0958 ( 0.2976 ) -0.5244
3. eval transitive triplets 0.3066 ( 0.2922 ) -0.8100
4. eval transitive ties 0.5397 ( 0.4471 ) -0.6856
5. eval outdegree - popularity 0.0000 ( NA ) 0.1821
6. eval outdegree - activity 0.0000 ( NA ) 0.1142
Overall maximum convergence ratio: 0.9363
Score test for 2 parameters:
chi-squared = 6.33, p = 0.0422.
Total of 122 iteration steps.
chi-squared = 25.49, d.f. = 2; p < 0.001.
Tested effects:
mynet: transitive triplets
mynet: transitive ties
chi-squared = 25.49, d.f. = 2; p < 0.001.
Tested effects:
mynet: outdegree - activity
chi-squared = 3.56, d.f. = 1; one-sided Z = -1.89; p = 0.059.
outdegree - popularity
Tested effects:
mynet: outdegree - popularity
chi-squared = 6.10, d.f. = 1; one-sided Z = -2.47; p = 0.014.
outdegree - activity
Tested effects:
mynet: outdegree - activity
chi-squared = 3.56, d.f. = 1; one-sided Z = -1.89; p = 0.059.
[1] effectName include fix test initialValue
[6] parm
<0 rows> (or 0-length row.names)
effectName include fix test initialValue parm type
1 reciprocity TRUE FALSE FALSE 0 0 creation
effectName include fix test initialValue parm type
1 reciprocity TRUE FALSE FALSE 0 0 endow
effectName include fix test initialValue parm type
1 basic rate parameter mynet TRUE FALSE FALSE 4.69604 0 rate
2 outdegree (density) TRUE FALSE FALSE -1.48852 0 eval
3 reciprocity TRUE FALSE FALSE 0.00000 0 endow
4 reciprocity TRUE FALSE FALSE 0.00000 0 creation
Start phase 0
theta: -1.49 0.00 0.00
Start phase 1
Phase 1 Iteration 1 Progress: 0%
Phase 1 Iteration 2 Progress: 0%
Phase 1 Iteration 3 Progress: 1%
Phase 1 Iteration 4 Progress: 1%
Phase 1 Iteration 5 Progress: 1%
Phase 1 Iteration 10 Progress: 2%
Phase 1 Iteration 15 Progress: 3%
Phase 1 Iteration 20 Progress: 4%
Phase 1 Iteration 25 Progress: 5%
Phase 1 Iteration 30 Progress: 6%
Phase 1 Iteration 35 Progress: 8%
Phase 1 Iteration 40 Progress: 9%
Phase 1 Iteration 45 Progress: 10%
Phase 1 Iteration 50 Progress: 11%
theta: -1.5212 -0.0961 1.0000
Start phase 2.1
Phase 2 Subphase 1 Iteration 1 Progress: 43%
Phase 2 Subphase 1 Iteration 2 Progress: 43%
theta -1.568 -0.177 2.396
ac 0.579 6.448 2.755
Phase 2 Subphase 1 Iteration 3 Progress: 44%
Phase 2 Subphase 1 Iteration 4 Progress: 44%
theta -1.767 -0.109 4.356
ac 0.836 0.913 1.358
Phase 2 Subphase 1 Iteration 5 Progress: 44%
Phase 2 Subphase 1 Iteration 6 Progress: 44%
theta -1.977 0.129 4.282
ac 0.895 -0.346 1.139
Phase 2 Subphase 1 Iteration 7 Progress: 45%
Phase 2 Subphase 1 Iteration 8 Progress: 45%
theta -2.080 0.569 2.915
ac 0.770 0.148 0.443
Phase 2 Subphase 1 Iteration 9 Progress: 45%
Phase 2 Subphase 1 Iteration 10 Progress: 45%
theta -2.102 0.581 4.549
ac 0.8067 0.1411 0.0306
theta -2.066 0.971 3.478
ac -0.192 -0.012 -0.608
theta: -2.066 0.971 3.478
Start phase 3
Estimates, standard errors and convergence t-ratios
Estimate Standard Convergence
Error t-ratio
Rate parameters:
0 Rate parameter 4.3298 ( 0.4866 )
Other parameters:
1. eval outdegree (density) -2.0655 ( 0.2415 ) 0.1263
2. endow reciprocity 0.9706 ( 1.0712 ) 0.0339
3. creat reciprocity 3.4778 ( 0.4926 ) 0.0876
Overall maximum convergence ratio: 0.1794
Total of 218 iteration steps.
chi-squared = 4.24, d.f. = 1; one-sided Z = -2.06; p = 0.039.
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