similar to: selecting predictors for model from dataframe

Note that I sent this to r-devel, yesterday. However, it didn't appear on the mailing list. So, I'm resending it. Today, I plotted the following: > filled.contour (,,z, color.palette=terrain.colors) It looked OK, in R. However, when I created a PDF document, the plot (and other similar plots) had grid (and other) lines in it, that shouldn't be there. Note that this problem is more

Dear R user, I am a biochemist/bioinformatician, at the moment working on protein clusterings by conformation similarity. I only started seriously working with R about a couple of months ago. I have been able so far to read my way through tutorials and set-up my hierarchical clusterings. My problem is that I cannot find a way to obtain information on the rooting of specific nodes, i.e. of

Hello I have following code in my project and it''s a question for me if it causes memory leaks in IE. pagePart = getSearchResult(); // retrieve part of the page w/ search result from server $(''container'').update(pagePart); $$(''#container a'').each(function(a) { Event.observe(a, ''click'', ...); // handlers for link clicks }); As you

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

Hello I am running R 2.0.1 on Windows, I am attempting to use Frank Harrell's 'fastbw' function (from the Design library), but I get an error that the fit was not created with a Design library fitting function; yet when I go to the help for fastbw (and also look in Frank's book Regression Modeling Strategies) it appears that fastbw should work with a model created with lm.....

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

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

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?

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

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 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

DeaR users. <framework> These days i'm working on fitting an extended Cox model with time-dependent covariables and possibly time-varying effects. My data are in counting process format as described in Therneau&Grambsh's `Modeling Survival Data', page 68. I'm trying to follow Harrell's `Regression Modeling Strategies' advices for the choice of my model. This

I am cleaning up the rms package to not export functions not to be called directly by users. rms uses generic functions defined in other packages. For example there is a latex method in the Hmisc package, and rms has a latex method for objects of class "anova.rms" so there are anova.rms and latex.anova.rms functions in rms. I use:

I was unsure of what suitable goodness-of-fit tests existed in R for logistic regression. After searching the R-help archive I found that using the Design models and resid, could be used to calculate this as follows: d <- datadist(mydataframe) options(datadist = 'd') fit <- lrm(response ~ predictor1 + predictor2..., data=mydataframe, x =T, y=T) resid(fit, 'gof'). I set up a

Please forgive me if you already received this. I had an e-mail sending glitch this morning. http://cran.r-project.org/src/contrib/checkSummary.html reported an error in Design.trans.Rd * checking Rd files ... ERROR Rd files with syntax errors: /var/mnt/hda3/R.check/r-devel/PKGS/Design/man/Design.trans.Rd: unterminated section 'alias' The .Rd file is attached. It begins