Wald {RSienaTest} | 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)
A |
A |
ans |
An object of class |
... |
One or more integer numbers between 1 and |
test |
One or more integer numbers 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
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
.
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.
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=123) # 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)}