similar to: error message in cph

Hi, all, I am wondering if it is possible to set parameters of 'rpart' and 'tree' such that they will produce the exact same tree? Thanks. Auston Wei Statistical Analyst Department of Biostatistics and Applied Mathematics The University of Texas MD Anderson Cancer Center Tel: 713-563-4281 Email: wwei@mdanderson.org [[alternative HTML version deleted]]

Hi, there, I am having trouble using 'lrm' function in package 'Design'. Basically, the ' . ' after ' ~ ' wouldn't work. Here are some sample codes: > temp <- data.frame(a=c(rep(0,3),rep(1,3)),b=rnorm(6),c=c('a','b','c','a','b','c')) > lrm(a~.,data=temp) Error in terms.formula(formula, specials =

Hi, List, I used the following codes to generate ps plots but foo.ps contains nothing. Would someone please point out what is wrong with my codes? Thanks a million! postscript('foo.ps') par(mfrow=c(2,1)) par(mfg=c(1,1)) hist(rnorm(100),col='blue') par(mfrow=c(2,2)) par(mfg=c(2,1)) hist(rnorm(50),col='blue') par(mfg=c(2,2)) hist(rnorm(60),col='blue') dev.off()

Hi, list, suppose i have such a data frame: trash <- data.frame(cbind(seq(1:5),c('a','a','b','a','b'),c('b','a','b','b','a'))) names(trash) <- c('age','typeI','typeII') and I want to change all 'a's to be 0 and 'b's to be 1. temp <- as.matrix(trash)

While sm4.6ll<-fit.mult.impute(Surv(agesi, si)~partner+ in.love+ pubty+ FPA+ strat(gender),fitter = cph, xtrans = dated.sexrisk2.i, data = dated.sexrisk2, x=T,y=T,surv=T, time.inc=16) runs perfectly using Hmisc, Design and mice under R11 run via Sciviews-K, with library(Design) library(mice) ds2d<-datadist(dated.sexrisk2) options(datadist="ds2d")

Hi list, Would someone please give me a hint how to extract the between and within subject StdDev from "lme" fit? Below is the output from the fit, but I couldn't find the number 0.3927714 anywhere in the fitted object. Thanks in advance for your help! Random effects: Formula: ~1 | group (Intercept) Residual StdDev: 0.3927714 0.7857085 Best, Auston

Dear R-helpers, I am very new to R and trying to run the conditional logit model using "clogit " command. I have more than 4000 observations in my dataset and try to predict the dependent variable from 14 independent variables. My command is as follows clmtest1 <- clogit(Pin~Income+Bus+Pop+Urbpro+Health+Student+Grad+NE+NW+NCC+SCC+CH+SE+MRD+strata(IDD),data=clmdata) However, it

I have been attempting to use cph models to get better calibration of my models for which I had originally used logistic regression. I tried running with 40 repetitions and got an error. I then tried 500 repetitions (thinking that the NaNs in the output below might be caused by that choice) and then let my computer crunch for several hours and got only the same error message and

R list: I am using a 'for' loop to run a number of different models (stratified by different variables) with coxph. The data becomes sparse when some strata are used causing the model to become unstable. The following error occurs and the analysis is terminated. >Error in fitter(X, Y, strats, offset, init, control, weights = weights, : (converted from warning) Loglik

I'm attempting to reflect the information for use with corresponding fields in GUI (in a different package), to provide default values, argname as key for UI label lookups, etc. So I want something much more like the formals of the implementation: { "object", "method": c("median", "vs", "tukey"),

Dear all, I am reading the cch function source code. But I can not understand the following codes. Please help me. What's the fitter here? fitter <- get(method) out <- fitter(tenter = tenter, texit = texit, cc = cc, id = id, X = X, ntot = nn, robust = robust) [[alternative HTML version deleted]]

What a delicious example! (I'm taking the liberty of sharing it with r-devel, since it raises some good issues.) You have two questions, presumably: 1 - how could the order of the setMethod calls make a difference in the results? 2 - what's causing the infinite loop & how could it be avoided, reliably? Second question first. The danger sign is the "vector" method:

hi, I applied coxph to my matrix of 300 samples and 215 variables and got the following error Error in fitter(X, Y, strats, offset, init, control, weights = weights, : NA/NaN/Inf in foreign function call (arg 6) In addition: Warning message: Ran out of iterations and did not converge in: fitter(X, Y, strats, offset, init, control, weights = weights, 26% of time data is censored and here

On Jan 28, 2015, at 6:37 PM, Michael Lawrence <lawrence.michael at gene.com> wrote: > At this point I would just due: > > formals(body(method)[[2L]]) > > At some point we need to figure out what to do with this .local() confusion. Agreed, definitely. The current hack is to avoid re-matching arguments on method dispatch, so a fix would need to be fairly deep in the

Hi, I have a script that takes a subset of genes on a microarray and tries to fit a coxph model to the expression values for each gene. This seems to work fine but in some cases it produces warnings and/or errors. For example: Error in fitter(X, Y, strats, offset, init, control, weights = weights, : NA/NaN/Inf in foreign function call (arg 6) In addition: Warning message: In fitter(X, Y,

Hi all, This is the first time I'm writing to R-devel, and this time I'm just asking for the purpose for a certain line of code in stats::as.hclust.dendrogram, which comes up as I'm trying to fix dendextend. The line in question is at line 128 of dendrogram.R in R-3.3.3, at stats::as.hclust.dendrogram: stopifnot(length(s) == 2L, all( vapply(s, is.integer, NA) )) Is there any

I just downloaded and installed R 2.3.0 on my Mac G5 running OS X 10.4.6. I also updated with R.app revision 3114 as recommended. Now, when I attemp to use package rgl I get the error > library(rgl) Error: package 'rgl' is not installed for 'arch=ppc' > I have tried reinstalling from CRAN using both binary and source. The source install fails, The binary install yields

I tried multiple imputation with aregImpute() and fit.mult.impute() in Hmisc 3.8-3 (June 2010) and R-2.12.1. The warning message below suggests that summary(f) of fit.mult.impute() would only use the last imputed data set. Thus, the whole imputation process is ignored. "Not using a Design fitting function; summary(fit) will use standard errors, t, P from last imputation only. Use

Hi, I'm trying to use the R Survival analysis on a windows 7 system. The input data format is described at the end of this mail. 1/ I tried to perform a survival analysis including stratified variables using the following formula. cox.xtab_miR=coxph(Surv(time, status) ~ miR + strata(sex,nbligne, age), data=matrix) and obtain the following error message Warning message: In fitter(X, Y,

I have a largish dataset (1025) with around .15 of the data missing at random overall, but more like .25 in the dependent variable. I am interested in modelling the data using quantile regression, but do not know how to do this with multiply imputed data (which is what the dataset seems to need). The original plan was to use qr (or whatever) from the quantreg package as the 'fitter'