similar to: MCMC coding problem

Folks, Based on http://www.biostat.wustl.edu/archives/html/s-news/1999-06/msg00125.html I thought I should experiment with using survreg() to estimate tobit models. I start by simulating a data frame with 100 observations from a tobit model > x1 <- runif(100) > x2 <- runif(100)*3 > ystar <- 2 + 3*x1 - 4*x2 + rnorm(100)*2 > y <- ystar > censored <- ystar <= 0

Dear all, I have a sample with 920 observations. I want to create a loop which takes 300 of these observations for the prediction and the rest to estimate the model. My idea was to create something like this: cs.training.dat <- read.table... cs.training.dat_sub1 <- subset(cs.training.dat, Income>10) cs.training.dat_sub2 <- subset(cs.training.dat_sub1, Dept.Ratio<=1)

Dear all I use aggregate with variables of type numeric and dates. For type numeric functions, such as sum() are very fast, but similar simple functions, such as min() are much slower for the variables of type 'dates'. The difference gets bigger the larger the 'id' var is - but see this sample code: dts <- dates(c("02/27/92", "02/27/92",

I am running my code in a loop and it does not work but when I run it outside the loop I get the values I want. n <- 1000; # Sample size fitglm <- function(sigma,tau){ x <- rnorm(n,0,sigma) intercept <- 0 beta <- 0 ystar <- intercept+beta*x z <- rbinom(n,1,plogis(ystar)) xerr <- x + rnorm(n,0,tau) model<-glm(z ~ xerr, family=binomial(logit))

hello all i searched the archives and couldn't get a solution to the following question. i have the following function: F=function(z,v) { if (v==-.5) return(1) else return(2*v/z + 1/Recall(z,v-1)) } and while testing whether it works (ie F(z,v) is approx = besselK(z,1+v)/besselK(z,V). the recursion formula allows one to calculate besselK(z,1+v)/besselK(z,V) for large values of z )

I am new to R and I am trying to do a monte carlo simulation where I generate data and interject error then test various cut points; however, my output was garbage (at x equal zero, I did not get .50) I am basically testing the performance of classifiers. Here is the code: n <- 1000; # Sample size fitglm <- function(sigma,tau){ x <- rnorm(n,0,sigma) intercept <- 0 beta

Dear R-users: The following code produces two cones in two panels. What I would like to have is to have them in one, and to meet in the origin. Does anyone have any good ideas how to do this? Thanks for your help Jaakko library(lattice) A<-matrix(ncol=2, nrow=64) for(i in 0:63) { A[i+1,1]<-sin(i/10) A[i+1,2]<-cos(i/10) }

Hi I'm encountering an error with some of my Windows clients when using CTDB and samba to access a GPFS hosted share. We see the following error all the time. [2011/03/14 12:31:16.549084, 1] modules/vfs_gpfs.c:1099(vfs_gpfs_ntimes) vfs_gpfs_ntimes: set GPFS ntimes failed -1 The copy continues but is slow. All the timestamps seem reasonable for the data copied. The GPFS

Dear all, I'm using zeroinfl() from the pscl-package for zero inflated Poisson regression. I would like to calculate (aproximate) prediction intervals for the fitted values. The package itself does not provide them. Can this be calculated analyticaly? Or do I have to use bootstrap? What I tried until now is to use bootstrap to estimate these intervals. Any comments on the code are welcome.

Dear community, Our team at IITH have been experimenting with loop-distribution pass in LLVM. We see the following results on few benchmarks. clang -O3 -mllvm -enable-loop-distribute -Rpass=loop-distribute file.c clang -O3 -mllvm -enable-loop-distribute -Rpass-analysis=loop-distribute file.c TORCH

I am experiencing strange results using dmvnorm. I define a scaled distance matrix from the coordinates bellow and then calculate a covariance matrix using a spherical correlation function. Then with certain combinations of range and sill parameters dmvnorm is returning values greater than 1. Surely the results of dmvnorm should be in the interval 0:1 (or do I just nead a holiday?). In addition

Dear R community, I was trying to estimate density at point zero of a multivariate distribution (9 dimensions) and for this I was using a multinormal approximation and the function dmvnorm , gtools package. To have a sense of the error I tried to look the mismatch between a unidimensional version of my distribution and estimate density at point zero with function density, dmvnorm and dnorm. At

Summary: + I am writing an R extension that needs to call dmvnorm more than 10,000 times during a model fitting computation. + My extension uses openmp for parallel execution. + As of R 3.0, it is no longer permitted for threads to call the R interpreter because there is a stack overflow check that always trips because the thread's stack is different from what R is expecting. +

I start by doing a simple gaussian tobit by MLE: x1 <- runif(1000) # E() = 0.5 x2 <- runif(1000)*2 # E() = 1 x3 <- runif(1000)*4 # E() = 2 ystar <- -7 + 4*x1 + 5*x2 + rnorm(1000) # is mean 0 y <- ystar censored <- ystar <= 0 y[censored] <- 0 library(AER) m <- tobit(y ~ x1 + x2, left=0, data=D) summary(m) Which gives: Call:

I am running 1.3.1 on a Windows (NT 4.0) machine. I've fit a nonlinear model intended to predict crop yield from nutrient information, and want to use the profile function. If I type say, profile(simparj.fm) I get the following error message: "Error in prof$getProfile(): number of iterations exceeded maximum of 5.25515e-308" I used the profiler function to profile simparj,fm step

I have the following function, a MSE calc based on some Multivariate normals: MV.MSE<-function(n,EP,X,S){ (dmvnorm(X,mean=rep(0,2),I+S+EP)-dmvnorm(X,mean=rep(0,2),I+S))^2 + 1/n*(dmvnorm(X,mean=rep(0,2),1+S+EP/2)*det(4*pi*EP)^-.5- (dmvnorm(X,mean=rep(0,2),I+S+EP ))^2)} I can get the MV.MSE for given values of the function e.g

Hi, i would to know, if someone have ever write the code to estimate the parameter (mixing proportion, mean, a var/cov matrix) of a mixture of two multivariate normal distribution. I wrote it and it works (it could find mean and mixing proportion, if I fix the var/cov matrix), while if I fix anything, it doesn't work. My suspect is that when the algorithm iterates the var/cov matrix, something

Hi, i would to know, if someone have ever write the code to estimate the parameter (mixing proportion, mean, a var/cov matrix) of a mixture of two multivariate normal distribution. I wrote it and it works (it could find mean and mixing proportion, if I fix the var/cov matrix), while if I fix anything, it doesn't work. My suspect is that when the algorithm iterates the var/cov matrix, something

hello all i searched the archives and couldn't get a solution to the following question. i have the following function: F=function(z,v) { if (v==-.5) return(1) else return(2*v/z + 1/Recall(z,v-1)) } and while testing whether it works (ie F(z,v) is approx = besselK(z,1+v)/besselK(z,V). the recursion formula allows one to calculate besselK(z,1+v)/besselK(z,V) for large values of z )

Dear R experts,   I'm running the following stationary bootstrap programming to find the parameters estimate of a linear model:     X<-runif(10,0,10) Y<-2+3*X a<-data.frame(X,Y) coef<-function(fit){   fit <- lm(Y~X,data=a)    return(coef(fit)) }  result<- tsboot(a,statistic=coef(fit),R = 10,n.sim = NROW(a),sim = "geom",orig.t = TRUE)   Unfortunately, I got this