similar to: mult.fig() utility [was "margin text mtext"]

Hi, I successfully created 3 screens with the following: fig.mat<-c(0,.5,.5,.5,1,1,0,0,.5,1,.5,1) fig.mat<-matrix(fig.mat,nrow=3) fig.mat split.screen(fig.mat) I can plot three different plots on those 3 screens, but when I try the following: (Trying to create three graphs with a common x-axis but different y-axis on screen 1) screen(1) par(oma=c(3,3,0,0)) par(fig=c(0,1,0,0.33))

Hello, I would like to use the aregImpute and fit.mult.impute to impute missing values for my dataset and then conduct logistic regression analyses on the data, taking into account that we imputed values. I have no problems imputing the values using aregImpute, but I am getting an error at the fit.mult.impute stage. Here is some sample code (I actually have more observations and variables to

The enclosed zip has some test files for both LLVM and Clang, to go along with the last mult-alt patch I submitted to the list. You'll note that some of the code is commented out for various problems not directly related to the mult-alt stuff. Though I worked on some additional versions for platforms not included here in the LLVM tests, they have various problems with the lowering, but which

I followed Bill Venables's suggestion and tried to make a multiplot figure with fig (using R .62). > x<-rnorm(100) > y<-rnorm(100) > x11() > par(fig=c(0,2/3,0,1)) > plot(x,y) > par(fig=c(2/3,1,0,1)) > qqnorm(x) > postscript(file="twoplot.ps") > par(fig=c(0,2/3,0,1)) > plot(x,y) > par(fig=c(2/3,1,0,1)) > qqnorm(x) However

I have always avoided missing data by keeping my distance from the real world. But I have a student who is doing a study of real patients. We're trying to test regression models using multiple imputation. We did the following (roughly): f <- aregImpute(~ [list of 32 variables, separated by + signs], n.impute=20, defaultLinear=T, data=t1) # I read that 20 is better than the default of

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

Hi all (and especially Frank), I'm trying to use x=T, y=T in order to run a validated stepwise cox regression in rsm, having multiply imputed using mice. I'm coding model.max<-fit.mult.impute(baseform,cph,miced2,dated.sexrisk2,x=T,y=T) baseform is baseform<-Surv(si.age,si=="Yes")~ peer.press + copy.press + excited + worried + intimate.friend + am.pill.times +

I have a largish dataset (1025) with around .15 of the data missing at random overall, but more like .25 in the dependent variable. I am interested in modelling the data using quantile regression, but do not know how to do this with multiply imputed data (which is what the dataset seems to need). The original plan was to use qr (or whatever) from the quantreg package as the 'fitter'

Hi all, I have read through the archives, but can't find a solution to this problem. I need the text direction on "dependent B", plotted in margin 4, to go top to bottom (opposite what it is now). Here's some sample code: #plot with mtext example par(mgp = c(2,1,0), mfrow=c(2,2), las=1, mar=c(2,2,2,2), omi= c(0.5,0.2,0,0.2)) a<-1:10 b<-7:16 c<-21:30 plot(a~b,

many thanks to P. Dalgaard, J. Fox, J. Lemon, JE. Paradis and J. Polzehl for their quick replies. The original posting is at the end of this summary. I've not well explained myself but I don't wanted to use par(mfrow) or par(mfcol) because I wanted to plot very different graphics and this solution doesn't match my needs. E. Paradis and P. Dalgaard made me discover a new (for me!)

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

hello all, I want to draw a figure with multiple plot on the same page using the par(fig=value) parameter but > par(fig = c(0, 50, 60, 95)/100, adj = 5/10) > eboulis(iris.acp) > par(fig = c(45, 100, 60, 95)/100, mgp = c(3, 1/2, 0)) > boites(iris.acp) draw the graphics on 2 different pages. what am I doing wrong ? thanks for your help. Mathieu [using R 0.65 under Linux Redhat

Any idea why I can no longer plot two graphs on the same graphics device using the par(fig=) parameter? A simpler par(mfrow=c(1,2)) does work, showing the two plots side-by-side, but I would like the first to be larger. This simple example fails: x<-c(1,1,NA,2,2,NA,3,3) y<-c(2,4,NA,3,5,NA,1,4) par(fig=c(0,2/3,0,1)) plot(x,y) par(fig=c(2/3,1,0,1)) qqnorm(x) When plotted, the last

Hi guys, # I would like to plot a figure with the following layout: # # ---------------------------- # | | | # | | | # | | | # | |--------| # | | | # | | | # | | | # ---------------------------- x <- rnorm(100) y <- rnorm(100)

Hello all R users and developers! In the "Notes on R" I have found the following information: (page 60, 13-th footnote) "... a better solution is to use the fig() driver (available from statlib) and use a conversion program, such as fig2dev, to convert the resultant fig code to Encapsulated Podtscript." I was very glad to read it, because I'm often forced to use

Hi all, I am new in R tool. I would like to plot two dataset in in fig. Here is what I did for both a and b data sets jpeg(file="a.jpeg") dat<-read.table('a', header=F, sep=',') dim(dat) y<-dat[,1] y<-y[!is.na(y)] plot(y);lines(lowess(y, f=0.05), col = ("red"), lwd=5) dev.off Two questions: 1. How I can save this lowess smooth data? 2. Once I

hello R-users, I'd like to plot four graphics on the same page but with different sizes. I've tried to use : par(fig=c(0,0.5,0,0.6)) plot(fig1) par(fig=c(0.5,1,0,0.6)) plot(fig2) etc... but when a figure is plotted, it erase the previous. I've tried to pass 'new=T' to plot function but it's not possible. What can I do ? is it a bug ? I've already reported this a 2 or

Can we add a note to the documentation that setting par(fig) resets par(mfrow) and par(mfcol) to c(1,1)? Or are mfrow and mfcol now deprecated in favor of all the split screen stuff? (I was spending the morning trying to write some code that plotted multiple subplots within whatever plot region was active at the moment; I was able to set and reset fig successfully, but got very confused as to

I tried multiple imputation with aregImpute() and fit.mult.impute() in Hmisc 3.8-3 (June 2010) and R-2.12.1. The warning message below suggests that summary(f) of fit.mult.impute() would only use the last imputed data set. Thus, the whole imputation process is ignored. "Not using a Design fitting function; summary(fit) will use standard errors, t, P from last imputation only. Use

Dear all, I used fit.mult.impute in Dr. Harrell's Design package to fit a cox ph regression model on five imputed datasets, where all missing predictors were filled by multiple imputation using R package Mice. Are there any functions able to do bootstrapping or cross-validation for the aggregated model? I tried function 'validate' and 'calibrate' in Design package, but