similar to: Unexpected behaviour in rms::lrtest

After some struggling with the data format, non-standard in BioConductor, I have gotten cor.balance in package CORREP to work. My desire was to obtain maximum-likelihood p-values from the same data object using cor.LRtest, but it appears that this function wants something different, which I can't figure out from the documentation. Briefly, my dataset consists of 36 samples from 12

Dear All, using the example from the help of summary.rms library(rms) n <- 1000 # define sample size set.seed(17) # so can reproduce the results age <- rnorm(n, 50, 10) blood.pressure <- rnorm(n, 120, 15) cholesterol <- rnorm(n, 200, 25) sex <- factor(sample(c('female','male'), n,TRUE)) label(age) <- 'Age'

I have a plot produced by function bplot (package = rms) that is really a lattice plot (class="trellis"). It is similar to this plot produced by a very minor modification of the first example on the bplot help page: requiere(rms) n <- 1000 # define sample size set.seed(17) # so can reproduce the results age <- rnorm(n, 50, 10) blood.pressure <- rnorm(n, 120,

In my own package, I want to use the default S3 method of the generic function lrtest() from the lmtest package. Since I need only one function from lmtest, I tried to use importFrom in my NAMESPACE: importFrom(lmtest, lrtest) However, this fails R CMD check in the examples: Error in UseMethod("lrtest") : no applicable method for 'lrtest' applied to an object of class

Hi all I am trying to generate a normal unbalanced data to estimate the coefficients of LM, LMM, GLM, and GLMM and their standard errors. Also, I am trying to estimate the variance components and their standard errors. Further, I am trying to use the likelihood ratio test to test H0: sigma^2_b = 0 (random effects variance component), and the t-test to test H0:mu=0 (intercept of the model Yij = mu

Hello all, I am unsure of how to interpret the output from a Generalized Estimating Equation analysis of an ordinal response. I hope someone can enlighten me. The analysis was done using package 'repolr'. The data consists of a Score on a 3-point scale from 56 Subjects after repeatedly washing their hands with soap. Two soap Products were tested, each panelist washed 10 times = 10

Hi, I have a technical question about rpart: according to Breiman et al. 1984, different costs for misclassification in CART can be modelled either by means of modifying the loss matrix or by means of using different prior probabilities for the classes, which again should have the same effect as using different weights for the response classes. What I tried was this: library(rpart)

Dear Rlist, How can the Legend of the plot.Design() function can be visualized? Following the documentation in R, I did the following (see below), only the 'Legend' function doesn't visualize the legend of the plot (method='image') of the lrmfit. I tried to change par( margin setting) but this didn’t solve it. How can this be solved? Thanks a lot, Jan

Hi, When running z <- plot(fit, age=NA, cholesterol=NA, perim=boundaries, method='image') Legend(z, fun=plogis, at=qlogis(c(.01,.05,.1,.2,.3,.4,.5)), zlab='Probability') And after pointing the cursor to the plot() screen in R, I obtain the following message: Using function "locator(2)" to place opposite corners of image.legend Error in

Hi, just making some experiments with design library i get an error if i want plot(fit) - show below from onlineHelp !? ..perhaps is here another mask problem?, but label from xtable which was my first problem is now off ! Thanks for advance & regards, Christian $ n <- 1000 # define sample size $ set.seed(17) # so can reproduce the results $ age <- rnorm(n, 50, 10)

Hi, I'm trying to extract a histogram over the results from a bootstrap. However I keep receiving the error message "Error in hist.default(boot.lrtest$ll, breaks = "scott") : 'x' must be numeric". The bootstrap I'm running looks like: > boot.test <- function(data, indeces, maxit=20) { + y1 <- fit1+e1[indeces] + mod1 <- glm(y1 ~ X1-1, maxit=maxit) +

Hello r-help, I am using the excellent np package to conduct a nonparametric kernel regression and am having some trouble plotting the results. I have 2 covariates, x1 and x2, and a continuous outcome variable y. I am conducting a nonparametric regression of y on x1 and x2. The one somewhat unusual feature of these data is that, to be included in the dataset, x1 must be at least as large as x2.

Hi, I am working with R version 2.1.0, and I seem to have run into what looks like a bug. I get the same error message when I run R on Windows as well as when I run it on Linux. When I call anova to do a LR test from inside a function, I get an error. The same call works outside of a function. It appears to not find the right environment when called from inside a function. I have provided

Dear R-helpers, I'm having a problem in using plot.design in Design Library. Tho following example code produce the error: > n <- 1000 # define sample size > set.seed(17) # so can reproduce the results > age <- rnorm(n, 50, 10) > blood.pressure <- rnorm(n, 120, 15) > cholesterol <- rnorm(n, 200, 25) > sex <-

dear£º in the example£¨nomogram£©£¬I don't understand the meanings of the program which have been marked by red line.And how to compile the program(L <- .4*(sex=='male') + .045*(age-50) + (log(cholesterol - 10)-5.2)*(-2*(sex=='female') + 2*(sex=='male'))). n <- 1000 # define sample size set.seed(17) # so can reproduce the results age <- rnorm(n, 50, 10)

Dear R-users, I have noticed small discrepencies in the reported estimate of the variance of the frailty by the print method for survreg() and the 'theta' component included in the object fit: # Examples in R-2.6.2 for Windows library(survival) # version 2.34-1 (2008-03-31) # discrepancy fit1 <- survreg(Surv(time, status) ~ rx + frailty(litter), rats) fit1 fit1$history[[1]]$theta

Dear all, I was trying to understand how "multcomp" package works by running the examples given in the documentation. However I still don't understand when it comes to multiple comparison set by user (please refer to "Ksub" in the code). Therefore I run 2 other cases along with the original example (case 1), with the expectation I'll get the point from the output. The

Hi, I've written a script that checks all bivariate correlations for variables in a matrix. I'm now trying to run a logistic regression on each pair (x,y) where y is a factor with 2 levels. I don't know how (or whether I want) to try to fathom what's up with glm. What I wrote is attached. Here's what I get. *****************************************************

Hi. Thanks for your help with my previous question (comparing two lm() models with a maximum likelihood ratio test) I had a look at lrtest from the package lmtest as it has been suggested to me, but I am not 100% sure if that is the right thing to do ... lrtest uses the same log likelihoods as you can extract by hand from lm() with logLik - are this the maximum log likelihoods? How does R

Hi Group, I have a question about obtaining the bias-corrected c-index using validate from the Design library. As an example, consider the example from help page: library(Design) ?validate.lrm n <- 1000 age <- rnorm(n, 50, 10) blood.pressure <- rnorm(n, 120, 15) cholesterol <- rnorm(n, 200, 25) sex <- factor(sample(c('female','male'),