RSiena (version 1.1-212)

summary.iwlsm: Summary method for Iterative Weighted Least Squares Models

Description

summary method for objects of class "iwlsm"

Usage

## S3 method for class 'iwlsm':
summary(object, method = c("XtX", "XtWX"),
 correlation = FALSE, ...)

Value

If printing takes place, only a null value is returned. Otherwise, a list is returned with the following components. Printing always takes place if this function is invoked automatically as a method for the summary function.correlationThe computed correlation coefficient matrix for the coefficients in the model.cov.unscaledThe unscaled covariance matrix; i.e, a matrix such that multiplying it by an estimate of the error variance produces an estimated covariance matrix for the coefficients.sigmaThe scale estimate.stddevA scale estimate used for the standard errors.dfThe number of degrees of freedom for the model and for residuals.coefficientsA matrix with three columns, containing the coefficients, their standard errors and the corresponding t statistic.termsThe terms object used in fitting this model.

Details

This function is a method for the generic function summary() for class "iwlsm". It can be invoked by calling summary(x) for an object x of the appropriate class, or directly by calling summary.iwlsm(x) regardless of the class of the object.

References

Venables, W. N. and Ripley, B. D. (2002) Modern Applied Statistics with S. Fourth edition. Springer. See also http://www.stats.ox.ac.uk/~snijders/siena/

See Also

summary

Examples

##not enough data here for a sensible example, but shows the idea.
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)
metadf <- split(meta$thetadf, meta$thetadf$effects)[[1]]
metalm <- iwlsm(theta ~ tconv, metadf, ses=se^2)
summary(metalm)