RSiena (version 1.1-212)

siena08: Function to perform a meta analysis of a collection of Siena fits.

Description

Estimates a meta analysis based on a collection of Siena fits.

Usage

siena08(..., projname = "sienaMeta", bound = 5, alpha = 0.05, maxit=20)

Value

An object of class sienaMeta. There are print, summary and plot methods for this class,

An object of class sienaMeta is a list containing at least the following. (Items cor.est to ns appear once for each effect.)cor.estSpearman rank correlation coefficient between estimates and their standard errors.cor.pvalp-value for aboveregfitPart of the result of the fit of iwlsm.regsummaryThe summary of the fit, which includes the coefficient table.Tsqtest statistic for effect zero in every modelpTsqp-value for abovetratiotest statistics that mean effect is 0ptratiop-value for aboveQstatTest statistic for variance of effects is zeropttildep-value for abovecjplusTest statistic for at least one theta strictly greater than 0cjminusTest statistic for at least one theta strictly less than 0cjpluspp-value for cjpluscjminuspp-value for cjminusmu.mlML estimate of population meanmu.ml.sestandard error of ML estimate of population meansigma.mlML estimate of population standard deviationmu.confintconfidence interval for population mean based on profile likelihoodsigma.confintconfidence interval for population standard deviation based on profile likelihoodn1Number of fits on which the meta analysis is basedscoreplusTest statistic for combination of right one-sided p-values from score testsscoreminusTest statistic for combination of left one-sided p-values from score testsscorepluspp-value for scoreplusscoreminuspp-value for scoreminusnsNumber of fits on which the score test analysis is basedthetadfData frame containing the coefficients, standard errors and score test resultsprojnameName for any output file to be produced by the print methodboundEstimates with standard error above this value were excluded from the calculationsscoresObject of class by indicating, for each effect in the models, whether score test information was present.

Details

A meta analysis is performed as described in the Siena manual, section `Meta-analysis of Siena results'. This consists of three parts: an iterated weighted least squares modification of the method described in the reference below; maximum likelihood estimates and confidence intervals based on profile likelihoods under normality assumptions; and Fisher combinations of left-sided and right-sided p-values.

Note that the corresponding effects must have the same effect name in each model fit. This implies that at least covariates and behavior variables must have the same name in each model fit.

References

T. A. B. Snijders and Chris Baerveldt. Multilevel network study of the effects of delinquent behavior on friendship evolution. Journal of Mathematical Sociology, 27: 123--151, 2003.

See also http://www.stats.ox.ac.uk/~snijders/siena/

See Also

sienaMeta, siena07

Examples

mymodel <- sienaModelCreate(fn=simstats0c, nsub=2, n3=100)
mynet1 <- sienaNet(array(c(s501, s502), dim=c(50, 50, 2)))
mynet2 <- sienaNet(array(c(s502, s503), dim=c(50, 50, 2)))
mydata1 <- sienaDataCreate(mynet1)
mydata2 <- sienaDataCreate(mynet2)
myeff1 <- getEffects(mydata1)
myeff2 <- getEffects(mydata2)
myeff1 <- setEffect(myeff1, transTrip, fix=TRUE, test=TRUE)
myeff2 <- setEffect(myeff2, transTrip, fix=TRUE, test=TRUE)
myeff1 <- setEffect(myeff1, cycle3, fix=TRUE, test=TRUE)
myeff2 <- setEffect(myeff2, cycle3, fix=TRUE, test=TRUE)
ans1 <- siena07(mymodel, data=mydata1, effects=myeff1, batch=TRUE)
ans2 <- siena07(mymodel, data=mydata2, effects=myeff2, batch=TRUE)
meta <- siena08(ans1, ans2)