XploRe Help : eivplmnor

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:	eiv
See also:	gplmest gplmopt

Quantlet:		eivplmnor
Description:		eivplmnor fits partially linear EIV model where the conditional distribution of y given x and t is normally distributed.

Reference(s):

Severini/Staniswalis, JASA, 1994; Liang, H., Haerdle, W. and Carroll, R. 1997 SFB DP27

Link:

Tutorial: EIV in XploRe

Usage:	`myfit = eivplmnor(x,t,y,sigma,h{,opt})`
Input:
	`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.
	`sigma`	scalar, the variance of the measurement error.
	`h`	q x 1 vector, the bandwidth.
	`opt`	optional, a list with optional input. The macro "gplmopt" can be used to set up this parameter. The order of the list elements is not important.
	`opt.b0`	p x 1 vector, the initial coefficients. If not given, all coefficients are put =0 originally.
	`opt.wx`	scalar or n x 1 vector, prior weights. If not given, set to 1.
	`opt.wt`	n x 1 vector, weights for t (trimming factors). If not given, all set to 1.
	`opt.tg`	ng x 1 vector, a grid for continuous part. If tg is given, the nonparametric function will also be computed on this grid.
	`opt.shf`	integer, if exists and =1, some output is produced which indicates how the iteration is going on.
	`opt.nosort`	integer, if exists and =1, the continuous variables t and the grid tg are assumed to be sorted by the 1st column. Sorting is required by the algorithm! Hence this option should be given only when data are sorted.
	`opt.miter`	integer, maximal number of iterations. The default is 10.
	`opt.cnv`	integer, convergence criterion. The default is 0.0001.
	`opt.wtc`	n x 1 vector, weights for convergence criterion, w.r.t. m(t) only. If not given, opt.wt is used.
	`opt.off`	scalar or n x 1 vector, offset in predictor.
Output:
	`myfit.b`	k x 1 vector, estimated coefficients.
	`myfit.bv`	k x k matrix, estimated covariance matrix for coefficients.
	`myfit.m`	n x 1 vector, estimated nonparametric part.
	`myfit.mg`	ng x 1 vector, estimated nonparametric part on grid if tg was given. This component will not exist, if tg was not given.
	`myfit.stat`	list with the following statistics:
	`myfit.stat.deviance`	deviance,
	`myfit.stat.pearson`	generalized pearson's chi^2,
	`myfit.stat.r2`	pseude R^2,
	`myfit.stat.dispersion`	dispersion parameter estimate,
	`myfit.stat.it`	scalar, number of iterations needed.

Example:

library("eiv") library("xplore") n=100 randomize(n) sigma=0.81 b=1|2 p=rows(b) x=2.*uniform(n,p)-1 t=sort(2.*uniform(n)-1,1) w=x+sqrt(sigma)*uniform(n) m=0.5*cos(pi.*t)+0.5*t y=x*b+m+normal(n)./2 h=0.5 sf=eivplmnor(w,t,y,sigma,h) b~sf.b dds=createdisplay(1,1) show(dds,1,1,t~m,t~sf.m)

Result:

A partially linear fit for E[y|x,t] is computed.
sf.b contains the coefficients for the linear part.
sf.m contains the estimated nonparametric part
evaluated at observations t. The example gives the
true b together with the EIVPLM estimate sf.b. Also, the
estimated function sf.m is displayed.

Author: H. Liang, W. Haerdle, 19970828 license MD*Tech