similar to: question on jitter in plot.Predict in rms

Hi All, I have a quick question about using plot.Predict now that the rms package uses lattice. I'd like to add tick marks along the regression line, which is given by data=llist(variablename) in the plot call. The ticks show up fine, but I'd like to alter the color. I know the ticks are produced by scat1d, but after spending a fair bit of time going through documentation, it still

Dear All, I wonder if someone can point me in the right direction here. I'm working with the rms library, R 2.9.2 under Windows XP. I'm trying to arrange two plots side by side for a colleague. mfrow or mfcol do not seem to work, however, so I am obviously missing something important. I know that there have been changes in the graphics from Design to rms, but am just not sure where to

Dear all, When fitting an "ols.model", the confidence interval at 95% doesn't cover the plotted data points because it is very narrow. Does this mean that the model is 'overfitted' or is there a specific amount of serial correlation in the residuals? Which R functions can be used to evaluate (diagnostics) major model assumptions (residuals, independence, variance) when

Dear All, I am creating 4 plots (files) but I could see only 3. Here is a simple code d=data.frame(age=rnorm(100,40,8),ht=rnorm(100,170,15)) tiff(file=paste("test","%03d",".tif",sep="")) hist.data.frame(d) datadensity(d) par(mfrow=c(1,2),mar=c(3,3,3,3)) boxplot(d$age) hist(age) dev.off() PDF works fine but png and tiff seems to miss the second

Hi All, I'm using the Hmisc summarize function and used list instead of llist to provide the by variables. It generated an error message. Is this a bug, or do I misunderstand how Hmisc works with lists? The program below demonstrates the error message. Thanks, Bob x<-1:8 group <- c(1,1,1,1,2,2,2,2) gender<- c(1,2,1,2,1,2,1,2) mydata<-data.frame(x,group,gender)

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

Dear R Users, I have searched through the archives but I am still struggling to find a way to process the below dataset. I have a dataset that has stratum and plot identifier. Within each plot there is variable (Top) stating the number of measurments that should be used to to calculate the mean to the largest "top" elements within one of the vectors (X). I would like to process

Following up on this attempt of implementing the tail-recursion optimisation ? now that I?ve finally had the chance to look at it again ? I find that non-local return implemented with callCC doesn?t actually incur much overhead once I do it more sensibly. I haven?t found a good way to handle parallel assignments that isn?t vastly slower than simply introducing extra variables, so I am going with

Interestingly, the <<- operator is also a lot faster than using a namespace explicitly, and only slightly slower than using <- with local variables, see below. But, surely, both must at some point insert values in a given environment ? either the local one, for <-, or an enclosing one, for <<- ? so I guess I am asking if there is a more low-level assignment operation I can get my

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 <-

No clue, but see ?assign perhaps if you have not done so already. -- Bert Bert Gunter "The trouble with having an open mind is that people keep coming along and sticking things into it." -- Opus (aka Berkeley Breathed in his "Bloom County" comic strip ) On Tue, Feb 27, 2018 at 6:51 AM, Thomas Mailund <thomas.mailund at gmail.com> wrote: > Interestingly, the

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 <-

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'

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

Dear friends, Ecdf used to work nicely before. e.g. ecdf(df1v[,c(1:6)],group=df1v$trt,q=.5,col=1:2,label.curves=list(keys="l ines"), datadensity="rug") but now it does not work at all, even for a simpler code??? By the way, I used the same libraries. Thanks for your help. Ahmed [[alternative HTML version deleted]]

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"),

I would like to incorporate multiple summary plots from the rms package into a single device and to control the titles, and also to open a new device when I reach a specified number of plots. Currently I am only getting a single "plot(summary(" graph in the upper left- hand corner of each successive device. However, in the rms documention I see instances of a loop being used with

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

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) +

Hi Jason, Overall patches look good. Just one doubt I have is below: > > We use spinlock to synchronize the work list now which may cause > unnecessary contentions. So this patch switch to use llist to remove > this contention. Pktgen tests shows about 5% improvement: > > Before: > ~1300000 pps > After: > ~1370000 pps > > Signed-off-by: Jason Wang <jasowang