XploRe Help : glmstat

Keywords - Function groups - @ A B C D E F G H I J K L M N O P Q R S T U V W X Y Z

Library:	glm
See also:	glmopt glmcore glmest glmlrtest glmll glmlink glmdiagh

Quantlet:		glmstat
Description:		glmstat provides some statistics for a fitted GLM.

Link:

Tutorial: GLM in XploRe

Usage:	`stat = glmstat(code,x,y,b,bv{,opt})`
Input:
	`code`	text string, the short code for the model (e.g. "bilo" for logit or "noid" for ordinary PLM).
	`x`	n x p matrix, the predictor variables.
	`y`	n x 1 vector, the response variables. (In the case of replicated data, the number of replications should be given in opt.wx and y should contain the sums of all responses for a replication.)
	`b`	p x 1 vector, estimated coefficients.
	`bv`	p x p matrix, inverse Hessian of optimization procedure. This is the estimated covariance of b, as it comes out of "glmcore", i.e. not yet corrected for dispersion!
	`opt`	optional, a list with optional input. "glmopt" can be used to set up this parameter. The order of the list elements is not important.
	`opt.weights`	string, type of weights. Can be "frequency" for replication counts, or "prior" (default) for prior weights in weighted regression.
	`opt.wx`	scalar or n x 1 vector, frequency or prior weights. If not given, set to 1.
	`opt.off`	scalar or n x 1 vector, offset in linear predictor. If not given, set to 0.
	`opt.pow`	scalar, power for power link. If not given, set to 0 (logarithm).
	`opt.nbk`	scalar, extra parameter k for negative binomial distribution. If not given, set to 1 (geometric distribution).
Output:
	`stat`	list with the following statistics:
	`stat.serror`	standard errors of parameter estimates.
	`stat.tvalue`	t-values for parameter estimates.
	`stat.pvalue`	p-values for significance of parameter estimates.
	`stat.df`	degrees of freedom.
	`stat.deviance`	deviance.
	`stat.pearson`	generalized pearson's chi^2.
	`stat.loglik`	log-likelihood.
	`stat.dispersion`	dispersion parameter estimate =pearson/df.
	`stat.r2`	(pseudo) R^2.
	`stat.adr2`	adjusted (pseudo) R^2.
	`stat.aic`	AIC criterion.
	`stat.bic`	BIC criterion.

Example:

library("glm") ;========================== ; simulate data ;========================== n=100 b=1|2 p=rows(b) bv=0.*matrix(p,p) x=2.*uniform(n,p)-1 y=( 1./(1+exp(-x*b)).>uniform(n) ) ;========================== ; compute statistics ;========================== glmstat("bilo",x,y,b,bv)

Result:

The statistics of this perfect fit.

Author: M. Mueller, 20010228