similar to: Logistic regression/Cut point? predict ??

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))

library(ROCR) n <- 1000 fitglm <- function(iteration,intercept,sigma,tau,beta){ x <- rnorm(n,0,sigma) ystar <- intercept+beta*x z <- rbinom(n,1,plogis(ystar)) xerr <- x + rnorm(n,0,tau) model<-glm(z ~ xerr, family=binomial(logit)) *int*<-coef(model)[1] *slope*<-coef(model)[2] # when add error you are suppose to get slightly bias slope. However when I change

Forgive my resending this post. To data I have received only one response (thank you Bert Gunter), and I still do not have an answer to my question. Respectfully, John Windows XP R 2.12.1 contrast package. I am trying to understand how to create contrasts for a model that contatains an interaction. I can get contrasts to work for a model without interaction, but not after adding the

R 2.5 windows XP I am getting an error from glm() that I don't understand. Any help or suggestions would be appreciated. N.B. 1<=AAMTCAREJ<=327900 > summary(data$AAMTCAREJ) Min. 1st Qu. Median Mean 3rd Qu. Max. 1.0 404.3 1430.0 6567.0 5457.0 327900.0 > fitglm<-glm(AAMTCAREJ~sexcat+H_AGE+SmokeCat+InsuranceCat+MedicadeCat+ +

Hi, I'd like to make a legend with a mix of mathematical symbol (tau), numeric variable and character variables.I have tried : types<-c("Type 1","Type 2","Type 2") tau<-c(1,3,2) legend(x="topright",legend=paste(types,"tau=",expression(tau))) but it doesn't work: the 'tau' symbol is not written in its 'symbol

Dear r-community, Today I have completed my PhD.  I would like to take this opportunity to thank the r-community for helping me with the r-coding.  I use r to do data manipulation during my PhD and I benefit a lot through the discussion in the r-forum. I will continue using R and help the others too. Thank you so much. Regards, Roslina UniSA. [[alternative HTML version deleted]]

Hi all, myint=function(mu,sigma){ integrate(function(x) dnorm(x,mu,sigma)/(1+exp(-x)),-Inf,Inf)$value } mymu=seq(-3,3,length(1000)) mysigma=seq(0,1,length(500))[-1] k=1 v=c() for (j in 1:length(mymu)) { for (i in 1:length(mysigma)) { v[k]=myint(mymu[j],mysigma[i]) k=k+1 } } Basically, I want to investigate for what values of mu and sigma, the integral is divergent. Is there another way

I'm trying to annotate a density plot and I'm using bquote to paste the sigma symbol next to the numeric text of the standard deviation calculation that I am performing. I have been able to successfully turn the sigma symbol and numeric output the color blue, but when I try to change the font of the text to bold, R doesn't seem to recognize the "font=" command in the same way

Paul, You solution worked out really well when I ran my code in R. However, when I try to turn the plot into a pdf, the unicode string no longer seems to function and instead of the sigma symbol there are just two periods (See example code below). The following is the code working in the R environment just like I want it to look: set.seed(1) Data=rnorm(100,sd=10000) plot(density(Data))

Dear All, I am trying to convert some S-plus code that I have to run MCMC into R-code. The program works in S-plus, but runs slowly. I have managed to source the program into R. R recognizes that the program is there; for example, it will display the code when I type the function name at the prompt. However, the program will not run. When I try to run the program, I get the following error

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 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) }

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'm trying to annotate a density plot and I would like to have R calculate the standard deviation and place it in the plot next to the standard deviation symbol "sigma". I can successfully use the text command to paste "StDev =",round(sd(Data),digits=3)) on the plot. However, I have trouble when I want to replace "StDev" with the Greek symbol sigma (See code

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

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 listers, I have a piece of code which performs an ANOVA type of analysis on 2D GC data. The code is shown below: # ANOVA 2D GC analysis # maxc <- number of samples # nreps <- number of samples maxc <- 2 nreps <- 4 sscl <- NULL cmean <- NULL # # Initial stat. variable # dftot <- nrow(mat)-1 dfcl <- maxc - 1 dferr <- dftot - dfcl totmean

I hope this question is adequate for this list I use the nnet code from V&R p. 348: The very nice and general function CVnn2() to choose the number of hidden units and the amount of weight decay by an inner cross-validation- with a slight modification to use it for classification (see below). My data has 2 classes with unequal size: 45 observations for classI and 116 obs. for classII With

Dear R-users, dear Prof. Ripley as package maintainer I tried to investigate the odd error, when I call nnet together with a 'weights' parameter, using the 'fgl' example in V&R p 348 The error I get is: Error in eval(expr, envir, enclos) : Object "w" not found I think it is a kind of scoping problem, but I really cannot see, what the problem exactly is. and