search for: fastbw

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 selection is carried out (using AIC as criterion). validate calls predab.resample, which in turn calls fastb...

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 invent a reason why nomograms shoul...

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 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 group of comorbidity variables that I would like to be ass...

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 ____________________...

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 after df) This may be a gap in my...

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? Consider: fastbw(model94c, aic=1e10) Deleted Chi-Sq d.f. P Residual d.f. P AIC ToD 0.11 3 0.9903 0.11 3 0.9903 -5.89 Experience * ToD 2.56...

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?...

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:...

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-S...

...MASS package) will drop appropriate terms from the model and test the difference via LRT (or F test). stepAIC in the MASS package will do stepwise selection via AIC. This opens the larger can of worms of why you're doing stepwise model selection in the first place ... I was surprised to see fastbw() in the rms package, since Frank Harrell is normally a vociferous opponent of stepwise approaches, but I assume (my copy of his book is not handy) that there is some context within which it makes statistical sense. > > My challenge is further complicated because I had hoped to use fast...

...ays defined * In cph, put survival::: on exact fit call * Quit ignoring zlim argument in bplot; added xlabrot argument * Added caption argument for latex.anova.rms * Changed predab to not print summaries of variables selected if bw=TRUE * Changed predab to pass force argument to fastbw * fastbw: implemented force argument * Added force argument to validate.lrm, validate.bj, calibrate.default, calibrate.cph, calibrate.psm, validate.bj, validate.cph, validate.ols * print.validate: added B argument to limit how many resamples are printed summarizing variables selecte...

...edictors for some arbitrary outcome variable Y. These predictors are to be placed in a model, and regression coefficients estimated in a new small dataset. This is what I got: #First I sample a small dataset from the large one, lrm is logistic regression from the Design package (as is the command fastbw) fsubset=lrm(Ysub~X1sub+X2sub+X3sub+X4sub, data=dsubset1) variables[i]=as.vector(fastbw(fsubset, rule="p", type="individual", sls=0.5)[2]) variables=unlist(variables[i]) #So my "variables" are the significant ones. #Below is the sampling of the testset, in which I wa...

...ays defined * In cph, put survival::: on exact fit call * Quit ignoring zlim argument in bplot; added xlabrot argument * Added caption argument for latex.anova.rms * Changed predab to not print summaries of variables selected if bw=TRUE * Changed predab to pass force argument to fastbw * fastbw: implemented force argument * Added force argument to validate.lrm, validate.bj, calibrate.default, calibrate.cph, calibrate.psm, validate.bj, validate.cph, validate.ols * print.validate: added B argument to limit how many resamples are printed summarizing variables selecte...

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

...ollows: d <- datadist(mydataframe) options(datadist = 'd') fit <- lrm(response ~ predictor1 + predictor2..., data=mydataframe, x =T, y=T) resid(fit, 'gof'). I set up a script to first use glm to create models use stepAIC to determine the optimal model. I used this instead of fastbw because I found the AIC values to be completely different and the final models didn't always match. Then my script takes the reduced model formula and recreates it using lrm as above. Now the problem is that for some models I run into an error to which I can find no reference whatsoever on the...

...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: export(asis,bj,bjplot,bootBCa,bootcov,bootplot,bplot,calibrate,cph,catg,combineRelatedPredictors,confplot,contrast,coxphFit,cph,cr.setup,datadist,effective.df,fastbw,formatNP,gendata,gIndex,GiniMd,Glm,Gls,groupkm,Hazard,hazard.ratio.plot,histdensity,"%ia%",ie.setup,interactions.containing,legend.nomabbrev,lm.pfit,lrm,lrtest,lsp,matinv,matrx,Newlabels,Newlevels,nomogram,num.intercepts,ols,ols.influence,oos.loglik,pantext,Penalty.matrix,Penalty.setup,pe...

...om all of the componet variables using ordinary least squares (ols in rms package) as the followings. I would like to know whether I am doing right or not. > library(rms) > plogit <- predict(full.model) > full.ols <- ols(plogit ~ stenosis+x1+x2+ClinicalScore+procedure, sigma=1) > fastbw(full.ols, aics=1e10) Deleted Chi-Sq d.f. P Residual d.f. P AIC R2 stenosis 1.41 1 0.2354 1.41 1 0.2354 -0.59 0.991 x2 16.78 1 0.0000 18.19 2 0.0001 14.19 0.882 procedure 26.12 1 0.0000 44.31 3 0.0000 38.31 0.711 Clinica...

Hi, Does R support any of the 3 stepwise or 8 criteria methods for logistic regression and multiple linear regression that SAS supports? If yes, could you give me some simple demostration code. Thanks, steve __________________________________________________ Do You Yahoo!? Make international calls for as low as $.04/minute with Yahoo! Messenger http://phonecard.yahoo.com/