similar to: Summary/Bootstrap for Design library's lrm function

Hey, I have a set of data and want to use Bootstrap method to estimate some pararameters. So does anybody know if there are such functions in R to achieve this? Thanks for your points. Fred -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read http://www.ci.tuwien.ac.at/~hornik/R/R-FAQ.html Send "info", "help", or

Hi there, Could anyone please help me to understand what should be done in order not to get this error message: Error: evaluation nested too deeply: infinite recursion / options(expressions=)? Here is my code: determinant<- function(x){det(matrix(c(1.0,0.2,0.5,0.8,0.2,1.0,0.5,0.6,0.5,0.5,0.5,1.0,x,0.8,0.6,x,1.0),ncol=4,byrow=T))} matrix<-

Dear all, I know there have been various questions posted over the years about loops but I'm afraid that I'm still stuck. I am using Windows XP and R 2.9.2. I am generating some data using the multivariate normal distribution (within the 'mnormt' package). [The numerical values of sanad and covmat are not important.] > datamat <-

Hi, I'm running into an error message for the "anova"-function I never got before with the Design (Version 2.2-0) package. There seems to be a missing function "Varcov", please check my function calls (it's in german but I think you get the error): > library(Design) ## attaching Design package and dependent packages Lade n?tiges Paket: Hmisc ## loading

What is 1. par.ests <- optimfit$par 2. fisher <- hessb(negloglik, par.ests, maxvalue=maxima); 3. varcov <- solve(fisher); 4. par.ses <- sqrt(diag(varcov)); Thanks a lot, fit.GEV <- function(maxima) { sigma0 <- sqrt((6. * var(maxima))/pi) mu0 <- mean(maxima) - 0.57722 * sigma0 xi0 <- 0.1 theta <- c(xi0, mu0, sigma0) #10/5/2007: removed assign() for maxima.nl

After upgrading R to 2.9.2, I can't use the anova() fuction. It says "could not find function "Varcov" ". What's wrong with my computer? Help needed, thanks! Yao Zhu Department of Urology Fudan University Shanghai Cancer Center No. 270 Dongan Road, Shanghai, China [[alternative HTML version deleted]]

I have always avoided missing data by keeping my distance from the real world. But I have a student who is doing a study of real patients. We're trying to test regression models using multiple imputation. We did the following (roughly): f <- aregImpute(~ [list of 32 variables, separated by + signs], n.impute=20, defaultLinear=T, data=t1) # I read that 20 is better than the default of

Dear R-list member, I'm using the function pmnorm() (-->library(mnormt)) within a for-loop. Certain parameter values leads to an error message: "(In sqrt(diag(S)) : NaNs produced, In sqrt(1/diag(V)) : NaNs produced, In cov2cor(S) : diag(.) had 0 or NA entries; non-finite result is doubtful)" obviously because "NaNs" were produced. Is it possible to tell R that it

Dear List: I am having some trouble reproducing some GLS estimates using matrix operations that I am not having with other R procedures. Here are some sample data to see what I am doing along with all code: mu<-c(100,150,200,250) Sigma<-matrix(c(400,80,16,3.2,80,400,80,16,16,80,400,80,3.2,16,80,400),n c=4) sample.size<-100 temp <-

Dear List: I am estimating a gls model and am having to make some rather unconventional modifications to handle a particular problem I have identified. My aim is to fit a GLS with an AR1 structure, obtain the variance-covariance matrix (V), modify it as needed given my research problem, and then reestimate the GLS by brute force using matrix operations. All seems to be working almost perfectly,

Hello, has anybody written a function to plot density ellipses (95%, 99% or anything) in a scatterplot? I found nothing in any package, nor in the list archives. There does seem to be a contributed package "ellipse" for S-Plus (on S-Archive), but it does a lot more than what I would need. Still, if anybody ported it to R, I'd be grateful for a link. I'm a bit afraid to try the

This was working a few weeks ago, but perhaps the package has been updated since then. model.1 <- lrm(response ~ p_value, data=c_abl_oncogene_1_RTK) When I run the following command . . . . prediction.1 <- predict(model.1, type=c("fitted")) I get the following error message. . . . Error in predictDesign(object, ..., type = "lp", se.fit = FALSE) : could not find

Hi All. I'd like to generate a sample of n observations from a k dimensional multivariate normal distribution with a random correlation matrix. My solution: The lower (or upper) triangle of the correlation matrix has n.tri=(d/2)(d+1)-d entries. Take a uniform sample of n.tri possible correlations (runi(n.tr,-.99,.99) Populate a triangle of the matrix with the sampled correlations Mirror the

Hi If you read the description of "varcov.spatial" you'll see that it is used to *predict* a covariance matrix, based on the parameters of the covariance function. So you don't need the oebserved data, you need parameters for the covariance function. Regards EJ On Thu, 2003-04-17 at 05:43, Yan Yu wrote: > THanks for the suggestion. > I have a Q when trying to use it..

Hi all! Is there a function that provides the VarCov matrix for a nlme objects? How I can extract the matrix for a nlme model fitted? I would appreciate any guidance Regards Lic. Gabriela Escati Pe?aloza Biolog?a y Manejo de Recursos Acu?ticos Centro Nacional Patag?nico(CENPAT). CONICET Bvd. Brown s/n?. (U9120ACV)Pto. Madryn Chubut Argentina Tel: 54-2965/451301/451024/451375/45401 (Int:277)

Hi I'm using geoR package to perform linear spatial interpolation(OK). The function likfit() fails to compute REML. The error meassage is : Error in solve.default(v$varcov, xmat); How I can find out that likfit() is failed to process and retrieving the error message ? Thank you so much for your help.

I do have some trouble with matrices. I want to build up a covariance matrix with a hierarchical structure). For instance, in dimension n=10, I have two subgroups (called REGION). NR=2; n=10 CORRELATION=matrix(c(0.4,-0.25, -0.25,0.3),NR,NR) REGION=sample(1:NR,size=n,replace=TRUE) R1=REGION%*%t(rep(1,n)) R2=rep(1,n)%*%t(REGION) SIGMA=matrix(NA,n,n) for(i in 1:NR){ for(j in

I found a strange inconsistency: If m is a matrix and d is a data frame then - mean(m), median(m), max(m) and max(d) all return a single value but - mean(d) returns the column means - median(d) fails - both var(m) and var(d) return the variance covariance matrix You pretty much have to experiment to figure this out since much of this behavior is not readily obvious from the help files.

Warranty on Accuracy, Precision, Legality, ... of R in Research (These questions may well have been raised.) What is the implied warranty of using R for research & publications, consulting, etc.? Alternately, how does one obtain such a warranty? Your answers will be much appreciated. Perhaps you can point me to some websites which discussed this subject in the past. Thanks & regards

Dear all, I am doing a simulation for my model that works when I use only the rq() command. However, since I need to use the varcov matrix for my Wald test, I need to compute summary(rq(), cov=TRUE). But the simulation does not work because of the error: tau + h > 1: error in summary.rq I tried to use: if (tau + h > 1) stop("tau + h > 1: error in summary.rq") But the