summary.iwlsm: Summary method for Iterative Weighted Least Squares Models In RSiena: Siena - Simulation Investigation for Empirical Network Analysis

Description

`summary` method for objects of class `"iwlsm"`

Usage

 ```1 2 3``` ```## S3 method for class 'iwlsm' summary(object, method = c("XtX", "XtWX"), correlation = FALSE, ...) ```

Arguments

 `object` the fitted model. This is assumed to be the result of some fit that produces an object inheriting from the class `iwlsm`, in the sense that the components returned by the `iwlsm` function will be available. `method` Should the weighted (by the IWLS weights) or unweighted cross-products matrix be used? `correlation` logical. Should correlations be computed (and printed)? `...` arguments passed to or from other methods.

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.

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.

 `correlation` The computed correlation coefficient matrix for the coefficients in the model. `cov.unscaled` The 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. `sigma` The scale estimate. `stddev` A scale estimate used for the standard errors. `df` The number of degrees of freedom for the model and for residuals. `coefficients` A matrix with three columns, containing the coefficients, their standard errors and the corresponding t statistic. `terms` The terms object used in fitting this model.

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/

`summary`
 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21``` ```## Not run: ##not enough data here for a sensible example, but shows the idea. myalgorithm <- sienaAlgorithmCreate(nsub=2, n3=100) mynet1 <- sienaDependent(array(c(s501, s502), dim=c(50, 50, 2))) mynet2 <- sienaDependent(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(myalgorithm, data=mydata1, effects=myeff1, batch=TRUE) ans2 <- siena07(myalgorithm, 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) ## End(Not run) ```