extractRoot: Extract a Root from a result of a wle function

In wle: Weighted Likelihood Estimation

Description

This function extract the information regarding one solution of the Weighted Likelihood Estimating Equation.

Usage

## S3 method for class 'wle.glm'
extractRoot(object, root=1, ...)

Arguments

object	an object of class "wle.glm", usually, a result of a call to wle.glm.
root	an integer number to specify which root should be extract.
...	further arguments passed to or from other methods.

Value

extract.wle.glm returns an object of class "extract.wle.glm.root", a (variable length) list containing at least the following components:

coefficients	a named vector of coefficients
residuals	the working residuals, that is the residuals in the final iteration of the IWLS fit. Since cases with zero weights are omitted, their working residuals are NA.
fitted.values	the fitted mean values, obtained by transforming the linear predictors by the inverse of the link function.
rank	the numeric rank of the fitted linear model.
family	the family object used.
linear.predictors	the linear fit on link scale.
deviance	up to a constant, minus twice the maximized log-likelihood. Where sensible, the constant is chosen so that a saturated model has deviance zero.
aic	Akaike's An Information Criterion, minus twice the maximized log-likelihood plus twice the number of coefficients (so assuming that the dispersion is known).
null.deviance	The deviance for the null model, comparable with deviance. The null model will include the offset, and an intercept if there is one in the model. Note that this will be incorrect if the link function depends on the data other than through the fitted mean: specify a zero offset to force a correct calculation.
iter	the number of iterations of IWLS used.
weights	the working weights, that is the weights in the final iteration of the IWLS fit.
prior.weights	the weights initially supplied, a vector of 1s if none were.
df.residual	the residual degrees of freedom.
df.null	the residual degrees of freedom for the null model.
y	if requested (the default) the y vector used. (It is a vector even for a binomial model.)
x	if requested, the model matrix.
model	if requested (the default), the model frame.
converged	logical. Was the IWLS algorithm judged to have converged?
boundary	logical. Is the fitted value on the boundary of the attainable values?
wle.weights	final (robust) weights based on the WLE approach.
wle.asymptotic	logicals. If TRUE asymptotic weight based on Anscombe residual is used for the corresponding observation.

In addition, non-empty fits will have components qr, R, qraux, pivot and effects relating to the final weighted linear fit.

family	the family object used.
call	the matched call.
formula	the formula supplied.
terms	the terms object used.
data	the data argument.
offset	the offset vector used.
control	the value of the control argument used.
method	the name of the fitter function used, currently always "wle.glm.fit".
contrasts	(where relevant) the contrasts used.
xlevels	(where relevant) a record of the levels of the factors used in fitting.
tot.sol	the number of solutions found.
not.conv	the number of starting points that does not converge after the max.iter (defined using wle.glm.control) iterations are reached.
na.action	(where relevant) information returned by model.frame on the special handling of NAs.

If a binomial wle.glm model was specified by giving a two-column response, the weights returned by prior.weights are the total numbers of cases (factored by the supplied case weights) and the component y of the result is the proportion of successes.

Author(s)

Claudio Agostinelli and Fatemah Al-quallaf

See Also

anova.wle.glm.root

Examples

## --- Continuing the Example from  '?wle.glm':

anova(extractRoot(wle.glm.D93))

wle documentation built on May 29, 2017, 11:48 a.m.