similar to: Nomograms from rms' fastbw output objects

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?

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 list, I'm facing the following problem : A cox model with my sex variable interacting with several continuous variables : cph(S~sex*(x1+x2+x3)) And I'd like to make a nomogram. I know it's a bit tricky and one mights argue that nomogram is not a good a choice... I could use the parameter interact=list(sex=("male","female"),x1=c(a,b,c))... but with rcs or pol

The rms package includes the nomogram function, which generates a list object that can be passed to plot for graphical production of nomograms. I would like to remove the "linear predictor" line in the graph, which means (I suspect) removing it from the nomogram output object. I've looked at the nomogram output object, but it is not clear to me if or how it might be edited to

Dear volunteer: I am a graduate student of medcine in china.And now,I am devoting myself to constructing a nomograms of bladder cancer.I want to do it with R-project.However, I do not know how to construct a nomograms with R-project.I want to get yours help,thank you! I wish you can tell me the operating procedure of the R-project. And I apologize for my english,it is poor,sorry!

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

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, I am a novice R-user with some experience in using the RMS package for taking nomograms after various survival models. This time, I am trying to plot a nomogram after a Random Effects Cox, implemented by the "coxme" package. My questions are: 1. Is it possible to take a nomogram directly after the coxme survival function? 2. If not is there a way to take the linear

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

Hi R-help, ? I have fit a cox ph model to my data, but have beenreceiving an error when trying to fit a model to the nomogram. Here is the codeand corresponding error: ? ? >nomogramCF = nomogram(cph_age6_40avp4, +????????????????????lp.at= seq(-10,10,by =0.5),lp = TRUE, +?????????????????????? +??????????????????????funlabel="5year survival",

Hi R users. I have a model of cox that already it is estimated (I have only the model estimated, I haven't data), how can I determine a nomogram with R? Is it posible to do nomograms in Design package? I think that's only when the model (Cox Regression in this case) is before estimated in R. Thanks

Hello, I am constructing a nomogram using cph and nomogram commands in Dr. Harrell's Design/RMS package. The HR that I obtain for dichotomous and categorical variables are identical to those that I obtain using STATA stcox. However, the inter-quartile HR I obtain for continuous variables is obviously different, since STATA gives me HR for each unit (year, centimeter, etc) like coxph would

Dear R-users, I have used the nomogram function from the rms package for a logistic regresison model made with lrm(). Everything works perfectly (r version 2.15.1 on a mac). My question is this: if my final model is not the one created by lrm, but I internally validated the model and 'shrunk' the regression coefficients and computed a new intercept, how can I build a nomogram using that

Hello, I am using Dr. Harrell's rms package to make a nomogram. I was able to make a beautiful one. However, I want to change 5-year survival probability to 5-year failure probability. I couldn?t get hazard rate from Hazard(f1) because I used cph for the model. Here is my code: library(rms) f1 <- cph(Surv(retime,dfs) ~ age+her2+t_stage+n_stage+er+grade+cytcyt+Cyt_PCDK2 , data=data11,

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 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:

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 all R users I did a logistic regression with my binary variable Y (0/1) and 2 explanatory variables. Now I try to draw my nomogram with predictive value. I visited the help of R but I have problem to understand well the example. When I use glm fonction, I have a problem, thus I use lrm. My code is: modele<-lrm(Y~L+P,data=donnee) fun<- function(x) plogis(x-modele$coef[1]+modele$coef[2])