similar to: fastbw question

Hello again I am trying to fit an ordinal logistic model using the polr function from MASS. When I run model.loan.ordinal <- polr(loancat~age + sex + racgp + yrseduc + needlchg + gallery + sniffball + smokeball + sniffher + smokeher + nicocaine + inject + poly(year.of.int,3) + druginj + inj.years) I get an error Error in optim(start, fmin, gmin, method = "BFGS", hessian =

There is both a technical and a theoretical element to my question... Should I be able to use the outputs which arise from the fastbw function as inputs to nomogram(). I seem to be failing at this, -- I obtain a subscript out of range error. That I can't do this may speak to technical failings, but I suspect it is because Prof Harrell thinks/knows it injudicious. However, I can't

Dear All, For logistic regression models: is it possible to use validate (rms package) to compute bias-corrected AUC, but have variable selection with AIC use step (or stepAIC, from MASS), instead of fastbw? More details: I've been using the validate function (in the rms package, by Frank Harrell) to obtain, among other things, bootstrap bias-corrected estimates of the AUC, when variable

Dear R users, For the purpose of validating a prediction model using validate() from the rms package, I am running into some trouble with using the fastbw() function breaking up natural groups of variables. Is there any way I can specify to keep certain variable together? In particular, if interactions are included I would also like to keep the main effects in the model. Another example is a

Hi, it seems that the fastbw() in the Design package only works with variable of class "factor" according to the help page if I understand correctly. Is there any R function/package that do stepwise variable selection for a Cox model with continuous independent variables? Thank you John ____________________________________________________________________________________ Looking

Hello! I am using relatively simple linear model. By applying fastbw() on ols() results from rms package I would like to get subtable "Approximate Estimates after Deleting Factors". However, it seems this is not possible. Am I right? I can only get coefficients for variables kept in the model (for example: x$coefficients), but not S.E., Wald's Z and P? Is there any easy way to

I want to employ a parsimonious model to draw nomograms, as the full model is too complex to draw nomograms readily (several interactions of continuous variables). However, one interesting variable stays or leaves based on whether I choose "p value" or "AIC" options to fastbw(). My question boils down to this: Is there a theoretical reason to prefer one over another?

I am using the validate option in the Design package with the Cox survival model. I am using the bw=T option which, like the fastbw function, performs a backward elimination variable selection The output includes a series of columns (below) giving information on eliminated variables. My question is that I am unsure of the difference between the 2 p-values given (the one after Chi-Sq and the one

[root at icehouse1 ~(keystone_admin)]# yum install glusterfs glusterfs-server glusterfs-fuseLoaded plugins: fastestmirror, langpacks, prioritiesLoading mirror speeds from cached hostfile * base: centos-mirror.rbc.ru * epel: mirror.logol.ru * extras: centos-mirror.rbc.ru * updates: centos-mirror.rbc.ru16 packages excluded due to repository priority protectionsPackage

Hi all, I'm trying to work out the following problem, but I can't imagine how. I have the following (much reduced & oversimplified) dataset My.df <- cbind.data.frame(PPM=c(15.78, 15.81, 15.87, 15.83, 15.81, 15.84, 15.91, 15.90, 15.83, 15.81, 15.93, 15.83, 15.70, 15.92, 15.76, 15.81, 15.91, 15.75, 15.84, 15.86, 15.82, 15.79,

I would like to know if it is possible to perform negative binomial regression with rate data (incidence density) using the glm.nb (in MASS) function. I used the poisson regression glm call to assess the count of injuries across census tracts. The glm request was adjusted to handle the data as rates using the offset parameter since the population of census tracts can vary by a factor of

Hello I'm using logistic regression from the Design library (lrm), then fastbw to undertake a backward selection and create a reduced model, before trying to make predictions against an independent set of data using predict.lrm with the reduced model. I wouldn't normally use this method, but I'm contrasting the results with an AIC/MMI approach. The script contains: # Determine full

Dear prof. Harrell, I'm not able to use the force option with fastbw, here an example of the error I've got (dataset stagec rpart package): > fitstc <- cph(Surv(stagec$pgtime,stagec$pgstat) ~ age + eet + g2 + grade + gleason + ploidy, data=stagec) > fbwstc <- fastbw(fitstc,rule="aic",type="individual") > fbwstc Deleted Chi-Sq d.f. P Residual d.f.

-----BEGIN PGP SIGNED MESSAGE----- Hash: SHA1 On 06/16/2011 01:47 PM, Rob James wrote: > Ben, > > Thanks for this. Very helpful and clearly others have tripped over the > same problem > I would have supposed that the solution was to ask lrm (or glm) to use > LR rather than Wald, but I don't see syntax to achieve this. Typically drop1 or dropterm (MASS package) will drop

A new version of rms is now available on CRAN for Linux and Windows (Mac will probably be available very soon). Largest changes include latex methods for validate.* and adding the capability to force a subset of variables to be included in all backwards stepdown models (single model or validation by resampling). Recent updates: * In survplot.rms, fixed bug (curves were undefined if

A new version of rms is now available on CRAN for Linux and Windows (Mac will probably be available very soon). Largest changes include latex methods for validate.* and adding the capability to force a subset of variables to be included in all backwards stepdown models (single model or validation by resampling). Recent updates: * In survplot.rms, fixed bug (curves were undefined if

Dear all, I think I have a rather strange question, but I'd like to give it a try: I want to perform a simulation numerous times, thats why I can't do it by hand. I sample a small dataset from a very large one, and use backward selection to select significant predictors for some arbitrary outcome variable Y. These predictors are to be placed in a model, and regression coefficients

I am creating habitat selection models for caribou and other species with data collected from GPS collars. In my current situation the radio-collars recorded the locations of 30 caribou every 6 hours. I am then comparing resources used at caribou locations to random locations using logistic regression (standard habitat analysis). The data is therefore highly autocorrelated and this causes Type

Hello all I am a very new R user I am used to using STATA My problem: I want to build a Cox model and validate this. I have a large number of clinical relevant factors and feel the need to reduce these. Meanwhile I have some clinical variables I deem sufficiently important to force into the model regardless of AIC or p value. This is my present log over commands

Dear R users, I have two data frames that were read from text files as follows: x_data <- read.table("x.txt", header = TRUE, sep = "|", quote = "\"'", dec = ".",as.is = TRUE,na.strings = "NA",colClasses = NA, nrows = 3864284, skip = 0, check.names = TRUE,fill=TRUE, strip.white = TRUE,