XploRe Help : gplmstat

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:	gplm
See also:	gplmopt gplminit gplmcore gplmest glmstat

Quantlet:		gplmstat
Description:		gplmstat provides some statistics for a fitted GPLM.

Link:

Tutorial: GPLM in XploRe

Usage:	`stat = gplmstat(code,x,t,y,b,m{,wx{,off}})`
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 discrete predictor variables.
	`t`	n x q matrix, the continuous predictor variables.
	`y`	n x 1 vector, the response variables.
	`h`	q x 1 vector, the bandwith.
	`b`	p x 1 vector, estimated coefficients.
	`m`	n x 1 vector, estimated nonparametric part
	`opt`	optional, a list with optional input. "gplmopt" can be used to set up this parameter. The order of the list elements is not important.
	`opt.weights`	string, type of observation 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("gplm") ;========================== ; simulate data ;========================== n=100 b=6|-6 p=rows(b) bv=0.001*unit(p) df=n x=2.*uniform(n,p)-1 t=sort(2.*uniform(n)-1,1) m=cos(pi.*t) y=( 1./(1+exp(-x*b-m)).>uniform(n) ) ;========================== ; compute statistics ;========================== stat=gplmstat("bilo",x,t,y,b,bv,m,df)

Result:

should be quit good :-)

Author: M. Mueller, 20010228