Wald and score tests for RSiena results
These functions compute Wald-type and score-type tests for results estimated by siena07.
Wald.RSiena(A, ans) Multipar.RSiena(ans, ...) score.Test(ans, test=ans$test) testSame.RSiena(ans, e1, e2)
A |
A |
ans |
An object of class |
... |
One or more integer numbers between 1 and |
test |
One or more integer numbers between 1 and |
e1,e2 |
Each an integer number between 1 and |
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
Wald.RSiena.
The hypothesis tested by testSame.RSiena is that all
parameters given in e1 are equal to those in e2.
This also is a special case of Wald.RSiena.
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.
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.
Tom Snijders
See the manual and http://www.stats.ox.ac.uk/~snijders/siena/
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:
for (i in which(ans$test)){
sct <- score.Test(ans,i)
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))
testSame.RSiena(ans1, 2, 3)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.