Perhaps I am missing something but it appears that because X1 and X2 are random normal, that the influence of X2 is much like a second sampling of X1, and thus you would expect just what you observed, especially with a large (1000) sample size. Try making X2 and X1 different.
Charles Annis, P.E. charles.an...@statisticalengineering.com 561-352-9699 http://www.StatisticalEngineering.com -----Original Message----- From: r-help-boun...@r-project.org [mailto:r-help-boun...@r-project.org] On Behalf Of Ana De Barros Sent: Thursday, March 25, 2010 12:19 PM To: r-help@r-project.org Subject: [R] Logit/probit model Deal all, I have a population with the following characteristics: N=1000 X0=rep(1,N) X1=rnorm(N) X2=rnorm(N) I also know that the population distribution is a linear logistic function with parameters alpha0=0 (intercept), alpha1=0.4 and alpha2=1.1. So easily I can get the dependent variable (in my case the response propensities) by doing: alpha=as.vector(c(0, 0.4, 1.1)) X=cbind(X0,X1, X2) X=matrix(X, ncol=3, nrow=N) P=X%*%alpha propensity=1/(1+exp(-(P))) proptrue=mean(propensity) I have to estimate by sampling simulation the response propensity (dependent variable), assuming I don9t know the population distribution and assuming: 1. a linear logistic function adjusting for x1 only 1.1 assuming I know the true parameters (alpha0=0 and alpha1=0.4) 1.2 assuming I don9t know the true parameters 2. a probit function adjusting for x1 only 2.1 assuming I know the true parameters (alpha0=0 and alpha1=0.4) 2.2 assuming I don9t know the true parameters When I assume I don9t know the true parameters I sample by doing for (g in 1:replicas) { labels=sample(N, sample.size, replace=FALSE) x0=X0[labels] x1=X1[labels] x2=X2[labels] propsample=propensity[labels] logitx1=glm(propsample~x1, family=binomial(link="logit")) coefx1= logitx1$coefficients fitx1= logitx1$fitted.values PSprob=mean(fitx1) probx1=glm(propsample~x1, family=binomial(link="probit")) c33= probx1$coefficients cc33= probx1$fitted.values PSprob=mean(cc33) } My problem is that although I omit x2 in the simulations I still get very similar results (similar response propensities) with the population response propensity and it doesn9t make any sense... I must be doing something wrong but I don9t find the error. Can you help me, please? Thanks a lot Ana [[alternative HTML version deleted]] ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
