similar to: Multiple rms summary plots in a single device

Dear experts, Please forgive the puzzled title and the length of this message - I thought it would be best to be as complete as possible and to show the avenues I have explored. I'm trying to fit a linear model to data with a binary dependent variable (i.e. Target.ACC: accuracy of response) using lrm, and thought I would start from the most complex model (of which "sample1.lrm1" is

Dear all, I am trying to plot KM curves with confidence bands with function survplot under package rms. However, the following codes do not seem to work. The KM curves are produced, but the confidence bands are not there. Any insights? Thanks in advance. library(rms) ########data generation############ n <- 1000 set.seed(731) age <- 50 + 12*rnorm(n) label(age) <- "Age"

Dear R-users, I am using R 2.13.0 and rms 3.3-0 , but can not reproduce figure 7.8 of the handouts *Regression Modeling Strategies* ( http://biostat.mc.vanderbilt.edu/wiki/pub/Main/RmS/course2.pdf) by the following code. Could any one help me figure out how to solve this? setwd('C:/Rharrell') require(rms) load('data/counties.sav') older <- counties$age6574 + counties$age75

Hi, in Design package, a plot of survival probability vs. a covariate can be generated by survplot() on a cph object using the folliowing code: n <- 1000 set.seed(731) age <- 50 + 12*rnorm(n) label(age) <- "Age" sex <- factor(sample(c('male','female'), n, TRUE)) cens <- 15*runif(n) h <- .02*exp(.04*(age-50)+.8*(sex=='Female')) dt <-

The integration of plot methods for various outputs from rms packages is a great appreciated aspect of the rms package. I particularly like to use: plot(summary(model)) for my own purposes, but... for publication/presentation I need to modify details like variable names, or the number of signficant digits used in the figure annotations. Is there a simple way to modify the plot inputs

Hi everyone, I'm having some difficulty getting "simple effects" for the ols() function in the rms package. The example below illustrates my difficulty -- I'll be grateful for any help. #make up some data exD <- structure(list(Gender = structure(c(1L, 2L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 2L, 2L, 2L, 1L, 2L), .Label = c("F", "M"), class = "factor"),

Dear colleagues, I have a question regarding controlling the jitter when plotting predictions in the rms package. Below I've simulated some data that reflect what I'm working with. The model predicts a continuous variable with an ordinal score, a two-level group, and a continuous covariate. Of primary interest is a plot of the group by score interaction, where the score is the ordinal

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 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'

Hello all, I'm trying to plot the effects of variables estimated by a regression model fit individually, and for categorical predictors, the independent variable shows up on the y-axis, with the dependent variable on the x-axis. Is there a way to prevent this reversal? Sample code with dummy data: # make dummy data set.seed(1) x1 <- runif(200) x2 <- sample(c(1,2),200, TRUE) x3 <-

Hello! I have created a bplot-figure using this code: *file <- "2dcali_red.ttt" ux<-as.matrix(read.table(file, dec = ",")) mode(ux)<-'numeric' vel<-ux[,1] ang<-ux[,2] x<-ux[,3] y<-ux[,4] dat<- data.frame(ang=ang, x=x,y=y) require(rms) ddist2 <- datadist(dat) options(datadist="ddist2") fitn <- lrm(ang ~ rcs(x,4) +

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

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 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, Running the following example from ?Predict() throws an error I have never seen before: set.seed(1) x1 <- runif(300) x2 <- runif(300) ddist <- datadist(x1,x2); options(datadist='ddist') y <- exp(x1+ x2 - 1 + rnorm(300)) f <- ols(log(y) ~ pol(x1,2) + x2) p1 <- Predict(f, x1=., conf.type='mean') Error in paste(nmc[i], "=", if (is.numeric(x))

Hi All, I am attempting to work with some data from loggers. I have read in a .csv exported from MS Access that already has my dates and times (in 24 clock format), (with StringsAsFactors=FALSE). > head(tdata) LogData date time 1 77.16 2008/04/24 02:00 2 61.78 2008/04/24 04:00 3 75.44 2008/04/24 06:00 4 89.43 2008/04/24

Dear, When i multiply the y of a regression by 10, I would expect that the coefficient would be multiply by 10 and the z value to stay constant. Here some reproducible code to support the case. *Ex 1* library(mvtnorm) library(VGAM) set.seed(1) x=rmvnorm(1000,sigma=matrix(c(1,0.75,0.75,1),2,2))

I think this is the fourth attempt to send this blessed message, so let's hope this gets through without any 'unprocessed' or 'ignored' in-lines on auto-reply. I wish to report to you some strange problems I'm experiencing with installing packages directly into my -R- 2.15.1 (there is an indirect solution, which I note below). First, here's some essential information:

Hello R users, I am new to R and am wondering if anyone can help me out with the following issue: I wrote a function to build ts models using different inputs, but when R displays the call for a model, I cannot tell which variables it is using because it shows the arguments instead of the real variables passed to the function. (e.g Call: lm(formula = dyn(dep ~ lag(dep, -1) + indep)) --->

Hello R users, I have been trying to output all my results (text, plots, etc) into the same postscript file as one document, but have been unable to...Can anyone help me improve my code below so that I can accomplish this? Currently I have to output them separately then piece them back together into one document.. Thanks in Advance for any help! options (scipen=999, digits=7)