similar to: Problems getting slope and intercept to change when do multiple reps.

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

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))

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

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-users E-mail: r-help@r-project.org Hi! R-users. http://finzi.psych.upenn.edu/R/library/mvpart/html/xpred.rpart.html says: data(car.test.frame) fit <- rpart(Mileage ~ Weight, car.test.frame) xmat <- xpred.rpart(fit) xerr <- (xmat - car.test.frame$Mileage)^2 apply(xerr, 2, sum) # cross-validated error estimate # approx same result as rel. error from printcp(fit) apply(xerr, 2,

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

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

------------------- Version 2 of my problem improving the definition of what the optimal solution would be. Dear all, I'm working on the following problem: Assume two datasets: Y, Y that represent the same physical quantity Q. Dataset X contains values of Q after an event A while dataset Y contains values of Q after an event B. In R X, Y are vectors of the same length, containing

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) }

Updating gnulib has caused -Wformat-signedness to be enabled. This has revealed many problems in C format strings. The fixes here fall into the following main categories: - Using %d with an unsigned parameter. - %x and %o expect an unsigned argument. - uid_t and gid_t are unsigned on Linux. The safe way to print these is to cast them to uintmax_t and then print then using the %ju

Updating gnulib has caused -Wformat-signedness to be enabled. This has revealed many problems in C format strings. The fixes here fall into the following main categories: - Using %d with an unsigned parameter. - %x and %o expect an unsigned argument. - uid_t and gid_t are unsigned on Linux. The safe way to print these is to cast them to uintmax_t and then print them using the %ju

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+ +

0down votefavorite <https://stats.stackexchange.com/questions/296569/how-to-obtain-random-slope-random-intercept-plots-for-categorical-response-varia#> I'm trying to generate a random slope random intercept plot after ordinal regression using the clmmfunction from the ordinal package in R. I have aggression levels which are categorical with six levels. Earlier, I made random intercept

Greetings, I am trying to run a logistic regression model for binary data with a random intercept and slope in R 2.6.1. When I use the code: lmer1<-lmer(infect ~ time+gender + (1+time|id), family=binomial, data=ichs, method="Laplace") Then from: summary(lmer1) I get the message: Error in if (any(sd < 0)) return("'sd' slot has negative entries") : missing

I am wondering if it possible to normalize the slope of a linear regression to its intercept to allow for valid between-group comparisons. Here is the scenario: I need to compare the slopes of biomass increase among NAFO divisions of Northwest Atlantic cod. However, the initial division biomass is a confounding factor that may influence the slope of the regression model. How can I normalize the

Windows Vista R 2.10.1 Does the following use of groupedData and lme produce an analysis with both random intercept and slope, or only random slope? zz<-groupedData(y~time | Subject,data=data.frame(data), labels = list( x = "Time", y = "y" ), units = list( x = "(yr)", y = "(mm)") ) plot(zz)

Dear all, I'm trying to fit a model on ecological data in which I have measured a few biotic and abiotic factors over the course of a few days in several individuals. Specifically, I'm interested in modelling y ~ x1, with x2, x3, and 'factor' as independent variables. Because data suggests both slope and intercept (for y ~x1) might differ between individuals, I'd want to

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 list members, I am working with a multilevel growth curve, that in its simplest form goes like follows: Yit = Ai + Bi t + eit (the error term is assumed to follow an AR(1) autorregressive process) One major topic in my research is the convergence in the values of Y over time. Thus, I am interested in the relationship between the random effects for the intercept and the slope, and I