wle (version 0.9-91)

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

Description

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

Usage

"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.

See Also

anova.wle.glm.root

Examples

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

anova(extractRoot(wle.glm.D93))