RSiena (version 1.1-232)

sienaTimeTest: Functions to assess and account for time heterogeneity of parameters

Description

Takes a sienaFit object from a siena07 estimation and tests for time heterogeneity by the addition of interactions with time dummy variables at waves m=2...(M-1). The test used is the score-type test of Schweinberger (2012). Tests for joint significance, parameter-wise significance, period-wise significance, individual significance, and one-step estimates of the unrestricted model parameters are returned in a list.

Usage

sienaTimeTest(sienaFit, effects=NULL, excludedEffects=NULL, condition=FALSE)

Arguments

sienaFit

A sienaFit object returned by siena07.

effects

Optional vector of effect numbers to test. Use the number on the print of the sienaFit object.

excludedEffects

Optional vector of effect numbers for which time heterogeneity is not to be tested. Use the number on the print of the sienaFit object.

condition

Whether to orthogonalize effect-wise score-type tests and individual significance tests against estimated effects and un-estimated dummy terms, or just against estimated effects.

Value

sienaTimeTest Returns a list containing many items, including the following:

JointTest

A chi-squared test for joint significance of the dummies.

EffectTest

A chi-squared test for joint significance across dummies for each separate effect.

GroupTest

A chi-squared test for joint significance across dummies; if sienaFit is a fit for a multi-group object then these refer to each group; else they refer to ecah period.

IndividualTest

A matrix displaying initial estimates, one-step estimates, and \(p\)-values for the individual interactions.

Details

This test follows the score type test of Schweinberger (2012) as elaborated by Lospinoso et al. (2011) by using statistics already calculated at each wave to obtain vectors of partitioned moment functions corresponding to a restricted model (the model in the sienaFit object; used as null hypothesis) and an unrestricted model (which contains dummies for waves m=2...(M-1); used as alternative hypothesis).

condition=TRUE leads to a rough-and-easy approximation to controlling the mentioned tests also for the unestimated effects.

After assessing time heterogeneity, effects objects can be modified by adding numbers of all or some periods to the timeDummy column. This is facilitated by the includeTimeDummy function. For an effects object in which the timeDummy column of some of the included effects includes some or all period numbers, interactions of those effects with time dummies for the indicated periods will also be estimated.

An alternative to the use of includeTimeDummy is to define time-dependent actor covariates (dummy variables or other functions of wave number that are the same for all actors), include these in the data set through sienaAlgorithmCreate, and include interactions of other effects with ego effects of these time-dependent actor covariates by includeInteraction. Using includeTimeDummy is easier; using self-defined interactions with time-dependent variables gives more control.

If you wish to use this function with sienaFit objects that use the finite differences method of derivative estimation, or which use maximum likelihood estimation, you must request the derivatives to be returned by wave using the byWave=TRUE option for siena07.

Effects leading to dummy interactions that are collinear with the model originally fitted, after excluding the effects mentioned, will be automatically excluded from the time heterogeneity testing.

References

See http://www.stats.ox.ac.uk/~snijders/siena/ for general information on RSiena.

J.A. Lospinoso, M. Schweinberger, T.A.B. Snijders, and R.M. Ripley (2011). Assessing and Accounting for Time Heterogeneity in Stochastic Actor Oriented Models. Advances in Data Analysis and Computation, 5:147-176.

M. Schweinberger (2012). Statistical modeling of network panel data: Goodness-of-fit. British Journal of Statistical and Mathematical Psychology, 65:263-281.

See Also

siena07, plot.sienaTimeTest, includeTimeDummy

Examples

# NOT RUN {
## Estimate a restricted model
myalgorithm <- sienaAlgorithmCreate(nsub=2, n3=100)
mynet1 <- sienaDependent(array(c(s501, s502, s503), dim=c(50, 50, 3)))
mydata <- sienaDataCreate(mynet1)
myeff <- getEffects(mydata)
myeff <- includeEffects(myeff, transTrip, balance)
ans <- siena07(myalgorithm, data=mydata, effects=myeff, batch=TRUE)

## Conduct the score-type test to assess whether heterogeneity is present.
tt <- sienaTimeTest(ans)
summary(tt)

## Suppose that we wish to include two time dummies.
## Add them in the following way:
myeff <- includeTimeDummy(myeff, recip, balance, timeDummy="2")
ans2 <- siena07(myalgorithm, data=mydata, effects=myeff, batch=TRUE)

## Re-assess the time heterogeneity
tt2 <- sienaTimeTest(ans2)

## And so on..
# }
# NOT RUN {
## A demonstration of the plotting facilities, on a larger dataset:
## (Of course pasting these identical sets of three waves after each other
##  in a sequence of six is not really meaningful.)
# }
# NOT RUN {
myalgorithm <- sienaAlgorithmCreate(nsub=4, n3=1000)
mynet1 <- sienaDependent(array(c(s501, s502, s503, s501, s503, s502), dim=c(50, 50, 6)))
mydata <- sienaDataCreate(mynet1)
myeff <- getEffects(mydata)
myeff <- includeEffects(myeff, transTrip, balance)
myeff <- includeTimeDummy(myeff, recip, timeDummy="2,3,5")
myeff <- includeTimeDummy(myeff, balance, timeDummy="4")
myeff <- includeTimeDummy(myeff, density, timeDummy="all")
ansp <- siena07(myalgorithm, data=mydata, effects=myeff, batch=TRUE)
ttp <- sienaTimeTest(ansp)

## Pairwise plots show
plot(ttp, pairwise=TRUE)

## Time test plots show
plot(ttp, effects=1:4, dims=c(2,2))
# }
# NOT RUN {
## A demonstration of RateX heterogeneity. Note that rate
## interactions are not implemented in general, just for
## Rate x cCovar.
# }
# NOT RUN {
myalgorithm <- sienaAlgorithmCreate(nsub=4, n3=1000)
mynet1 <- sienaDependent(array(c(s501, s502, s503), dim=c(50, 50, 3)))
myccov <- coCovar(s50a[,1])
mydata <- sienaDataCreate(mynet1, myccov)
myeff <- getEffects(mydata)
myeff <- includeEffects(myeff, transTrip, balance)
myeff <- includeTimeDummy(myeff, RateX, type="rate",
            interaction1="myccov")
ans <- siena07(myalgorithm, data=mydata, effects=myeff, batch=TRUE)
# }