similar to: Design: predict.lrm does not recognise lrm.fit object

Dear All, We aim to remove the spatial structure of our data using Moran Eigen Vectors and spdep package . Our data has 3694 samples and 13 variables. The computer stop working after almost 4 days of processing (we found it emitting a sharp sound and with all colors on the screen. No wories, it was restared without problem!). And we are left with nothing: no result file was produced since the

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

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

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.....

Hi, I've come across a strange error when using the lrm.fit function and the subsequent predict function. The model is created very quickly and can be verified by printing it on the console. Everything looks good. (In fact, the performance measures are rather nice.) Then, I want to use the model to predict some values. I get the following error: "fit was not created by a Design

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

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

Hi, I've come across a strange error when using the lrm.fit function and the subsequent predict function. The model is created very quickly and can be verified by printing it on the console. Everything looks good. (In fact, the performance measures are rather nice.) Then, I want to use the model to predict some values. I get the following error: "fit was not created by a Design

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

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

Hi, I am trying to construct a logistic regression model from my data (104 patients and 25 events). I build a full model consisting of five predictors with the use of penalization by rms package (lrm, pentrace etc) because of events per variable issue. Then, I tried to approximate the full model by step-down technique predicting L from all of the componet variables using ordinary least squares

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 would like to perform a bootstrap validation of a backward stepwise logistic regression analysis, but I am a beginner with R and I am not sure of how to do it. Is there anyone that can send me a sample file in tab format (that I can modify in Excel by pasting my data) and the pertinent R algorithm? Many thanks Giuseppe -- ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ Dr. Giuseppe Biondi

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/

Hi All, I know in R there is function named 'step', which does the stepwise regression and choose the model by AIC. However, if I want to choose a model per this logic: 1. Run a full model (linear regression, f = lm(y ~., data = ZZZ), for example) 2. Pick up the variable with biggest p value, delete it from the module and get a new regression model. 3. Repeat step 2

Sorry, I have wrote a wrong subject in the first email! Regards, Linda ---------- Forwarded message ---------- From: linda Porz <linda.porz@gmail.com> Date: 2011/5/25 Subject: combined odds ratio To: r-help@r-project.org Cc: r-help-request@stat.math.ethz.ch Dear all, I am looking for an R function which does stepwise selection cox model in r (delta chisq likelihood ratio test) similar

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

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?