similar to: HMisc/rms package questions

Hi, I'm using the rms package to do regression analysis using the lrm function. Retrieving odds ratios is possible using summary.rms. However, I could not find any information on how exactly the odds ratios for continuous variables are calculated. It doesn't appear to be the odds ratio at 1 unit increase, because the output of summary.rms did not match the coefficient's value. E.g.

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

I have fitted a proportional odds model, but would like to compare it to a continuation ratio model. However, I am unable to fit the CR model _including_ imputated data. I guess my troubles start with settuping the data for the CR model. Any hint is appreciated! Christian library(Hmisc) library(rms) library(mice) ## simulating data (taken from rms::residuals.lrm) set.seed(1) n <- 400 age

The code library(Design) f <- lrm(y~x1+x2+x1*x2, data=data) plot(f) produces a plot of log odds vs x2 with 0.95 confidence intervals. How do I get a plot of odds ratios vs x2 instead? Thanks -- View this message in context: http://r.789695.n4.nabble.com/Odds-ratios-from-lrm-plot-tp4033340p4033340.html Sent from the R help mailing list archive at Nabble.com.

In running R CMD check I get an error I can't debug. Would someone please let me know if they spot a syntax error in the code below or if there is a workaround for the parse error? Thanks -Frank Error in parse(file, n, text, prompt) : parse error Error in codoc(package = "Hmisc") : cannot source usages in documentation object 'plsmo' Execution halted * checking for

Dear All, using the example from the help of summary.rms library(rms) n <- 1000 # define sample size set.seed(17) # so can reproduce the results age <- rnorm(n, 50, 10) blood.pressure <- rnorm(n, 120, 15) cholesterol <- rnorm(n, 200, 25) sex <- factor(sample(c('female','male'), n,TRUE)) label(age) <- 'Age'

Dear list, I'm currently trying to use the rms package to get predicted ordinal responses from a conditional ratio model. As you will see below, my model seems to fit well to the data, however, I'm having trouble getting predicted mean (or fitted) ordinal response values using the predict function. I have a feeling I'm missing something simple, however I haven't been able to

Hi! Does anyone know how to do the test for goodness of fit of a logistic model (in rms package) after running fit.mult.impute? I am using the rms and Hmisc packages to do a multiple imputation followed by a logistic regression model using lrm. Everything works fine until I try to run the test for goodness of fit: residuals(type=c("gof")) One needs to specify y=T and x=T in the fit. But

Dear R-help and Prof. Harrell: My question concerns the baseline state for continuous variable in lrm() within the RMS package. I have a model which can be reduced to: lrm(FT ~ rcs(V1, c(0, 1,5)) The model makes perfect sense if the baseline state is where V1>=5 but the model makes no sense if the baseline category is 0 (which I had expected). Can someone point me to a reference, or

Hi, I am doing a retrospective analysis on a cohort from a designed trial, and I am fitting the model fit<-glmD(survived ~ Covariate*Therapy + confounder,myDat,X=TRUE, Y=TRUE, family=binomial()) My covariate has three levels ("A","B" and "C") and therapy has two (treated and control), confounder is a continuous variable. Also patients were randomized to

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

I have a plot produced by function bplot (package = rms) that is really a lattice plot (class="trellis"). It is similar to this plot produced by a very minor modification of the first example on the bplot help page: requiere(rms) n <- 1000 # define sample size set.seed(17) # so can reproduce the results age <- rnorm(n, 50, 10) blood.pressure <- rnorm(n, 120,

Hi all! I am trying to develop a plot a figure in which I would like to show the odds ratios obtained from a logistic model. I have tried with the dotplot option but no success. Could you help me? Is there any option when modelling the logistic model in R? Thank you in advance

Hello list, Is there any mean to fit models for ordinal response other than multinomial polytomous ("multinom" from nnet ) and cumulative logit ("polr" from MASS)? I am particularly interested in continuation-ratio model and adjacent-category logit model. It is for the sake of epidemiology in wild-living populations! Many thanks, Emmanuelle Fromont

Hi all, I have another question about the lrm function (from the rms library) that I cannot find the answer to. I get an error message when I try to fit a model, and I don't know what to make of it. Please forgive me for not having a toy example, but it appears the size and complexity of my data is somehow causing the error. The best I can do is show you what I type and what errors I get.

I am encountering an error I do not know how to debug. The error arises when I try to add an interaction term involving two continuous variables (defined using rcs functions) to an existing (and working) model. The new model reads: model5 <- lrm( B_fainting ~ gender+ rcs(exactage, 7) + rcs(DW_nadler_bv, 7) + rcs(drawtimefrom8am, 7)+ DW_firsttime+ DW_race_eth +

A significant update to the Hmisc package is now available on CRAN for all platforms. Hmisc source is now on github at https://github.com/harrelfe/Hmisc and the full change log may be found at https://github.com/harrelfe/Hmisc/commits/master The most important updates are additions of new graphics functions for summarizing and displaying data with an aim of replacing tables. There is also

Greetings, useRs. Does anybody have replication of the examples from the RMS book by Harrell coded in R? I find that most the code does not work and it takes too much time to debug. For example from p.276 > age.t <- w[,"age"] > f.full <- lrm(cvd~scored(rx)+rcs(dtime,5)+age.t+wt.t+pf.t+hx+sbp+ekg.t+sz.t+sg.t+ap.t+bm+hg.t,x=T,y=T) Error in model.frame.default(formula = cvd ~

Dear list, I am relatively new to ordinal models and have been working through the example given by Frank Harrell in the predict.lrm {Design} help All of this makes sense to me, except for the responses, i,e how do i interpret them? i would be extremely grateful if someone could explain the results? First i establish the date and model - > y <- factor(sample(1:3, 400, TRUE), 1:3,

Hello. One of my teaching assistants was experimenting and encountered unexpected behaviour with the lrtest function in the rms package. It appears that when you have a pair of non-nested models that employ an RCS, the error checking for non-nested models appears not to work. Here is a reproducible example. > library(rms) Loading required package: Hmisc Loading required package: lattice