Summary method for Iterative Weighted Least Squares Models
summary method for objects of class "iwlsm"
## S3 method for class 'iwlsm'
summary(object, method = c("XtX", "XtWX"),
correlation = FALSE, ...)object |
the fitted model.
This is assumed to be the result of some fit that produces
an object inheriting from the class |
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. |
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.
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. |
Adapted by Ruth Ripley
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/
## 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)
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