similar to: Trouble fitting models with nonNULL datadist in Design Library

Hello, using the Design library, and the following command (from the Harrell's book, example at Ch.20): dd <- datadist(rx, age, wt, pf, pf.coded, heart, map, hg, sz, sg, ap, bm) options(datadist=='dd') I get the following error: Error in datadist == "dd" : comparison (1) possible only for atomic data or list What does it means? thank you, giulia

Hi, Design isn't strictly an R base package, but maybe someone can explain the following. When lrm is called within a function, it can't find the dataset dd: > library(Design) > age <- rnorm(30, 50, 10) > cholesterol <- rnorm(30, 200, 25) > ch <- cut2(cholesterol, g=5, levels.mean=TRUE) > fit <- function(ch, age) + { + d <- data.frame(ch, age) +

Dear all, I have performed a simple logistic regression using the lrm function from the Design library. Now I want to plot the summary, or make a nomogram. I keep getting a datadist error: options(datadist= m.full ) not created with datadist. I have tried to specify datadist beforhand (although I don't know why it should be done): ddist<-datadist(d) ##where d is my dataset

I am running R version 2.4.1 on Windows XP. I have a question regarding the datadist() function from the Design library. I have a data.frame (call it my.data) with 4 columns. When I submit the code datadist(data=my.data) I get the following error message: Error in X[[1]] : subscript out of bounds I suspect there may be something wrong with my data.frame (I'm certain there is nothing

Hi I got an error message using datadist() from Design package: > library(Design,T) > dd <- datadist(beta.final) > options(datadist="dd") > lrm(Disease ~ gsct+apcct+rarct, x=TRUE, y=TRUE) Error in eval(expr, envir, enclos) : object "Disease" not found All variables inclduing response variable "Disease" are in the data frame

Dear R helpers, I would like to ask why polr occasionally generates results that look very odd. I have been trying to compare the power of proportional odds logistic regression with the Wilcoxon test. I generated random samples, applied both tests and extracted and compared the p-values, thus:- library(MASS) c1=rep(NA,100); c2=c1 for (run in 1:100) { dat=c(rbinom(20,12,0.65),rbinom(20,12,0.35))

Hey, I have a few doubts with regard to the usage of the auto.arima function from the forecast package in R. *Background:* I have a set of about 50 time-series for which I would like to estimate the best autroregressive model. (I want to estimate the coefficients and order of p). Each of the series is non-stationary and are also have a non-normal distribution. The data is non-seasonal. My