Hello... just some additional thoughts: Maybe I can try it in a simple way with a repeated lm-regression, like:
Y=c(15,14,23,18,19,9,19,13) X1=c(0.2,0.6,0.45,0.27,0.6,0.14,0.1,0.52) X2a=c(17,22,21,18,19,25,8,19) X2b=c(22,22,29,34,19,26,17,22) X2 <- function()runif(length(X2a), X2a, X2b) for loop --> repeat lm(Y~X1+X2()) for let's say 1000 times and write the regression coefficients into a vector. Afterwards just get e.g the mean and standard deviation for Intercept, and beta. How is the for loop done in this case? I tried for(i in 1:1000) model(i) <-lm(Y~X1+X2()) but that is not working.... /johannes -------- Original-Nachricht -------- Datum: Mon, 15 Aug 2011 10:20:53 +0200 Von: "Johannes Radinger" <[email protected]> An: [email protected] Betreff: MCMC regress, using runif() Hello, just to follow up a question from last week. Here what I've done so far (here an example): library(MCMCpack) Y=c(15,14,23,18,19,9,19,13) X1=c(0.2,0.6,0.45,0.27,0.6,0.14,0.1,0.52) X2a=c(17,22,21,18,19,25,8,19) X2b=c(22,22,29,34,19,26,17,22) X2 <- function()runif(length(X2a), X2a, X2b) model1 <- MCMCregress(Y~X1+X2()) summary(model1) but I am not sure if my X2-function is working in the MCMCpack? Is a random number drawn each iteration step? I don't think so as the results are varying greatly if I run the script several times. Is there any other way to do several thousand runs of a linear regression,always drawing a random number for X2 and then compute average values for the regressions? /Johannes -- -- ______________________________________________ [email protected] 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.
