Wald | R Documentation |
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 A\theta = 0
, where \theta
is
the parameter estimated in the process leading to ans
.
The hypothesis tested by Multipar.RSiena
is that all
parameters given in \ldots
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 |
efnames: |
For |
Tom Snijders
See the manual and https://www.stats.ox.ac.uk/~snijders/siena/
M. Schweinberger (2012). Statistical modeling of network panel data: Goodness-of-fit. British Journal of Statistical and Mathematical Psychology 65, 263–281.
siena07
, print.sienaTest
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)
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.