similar to: how to add 3d-points to bplot {rms} figure?

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 get bootstrap resampled estimates of covariates in a Cox model using cph (Design library). Using the following I get the error: > ddist2.abr <- datadist(data2.abr) > options(datadist='ddist2.abr') > cph1.abr <- cph(Surv(strt3.abr,loc3.abr)~cov.a.abr+cov.b.abr, data=data2.abr, x=T, y=T) > boot.cph1 <- bootcov(cph1.abr, B=100, coef.reps=TRUE,

Hi, I'm trying to customize a window with 2 graphs. I'm able to do the first one with something like this general example par(mfrow=c(1,2),cex.axis=0.85,cex.lab=0.80,mai=c(1.3,1,0.5,0),las=3) bplot<-barplot(bar.values,names.arg=cf.names,width=0.5,ylab="% Area held") abline(h=0.3,lty=3,col="red") abline(h=0.1,lty=3,col="blue")

I have a data set that I display using barplot. I don't know what you call it, but when I look at it, it looks like this: > lsu (0,0.1] (0.1,0.2] (0.2,0.3] (0.3,0.4] (0.4,0.5] (0.5,0.6] A 0.052631579 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 B 0.000000000 0.000000000 0.001007049 0.003021148 0.000000000 0.000000000 E 0.200000000 0.000000000

Dear community, I had been looking for an easy way to produce latex tables from R output. xtable() and the package apsrtable produce good outputs but they are not exactly what I was looking for. I wrote this code that generates regression tables from multiple R linear models. I want to share it because it might be useful for someone else, and because I would appreciate comments on how to

Hello, I'm quite new to R but tried to learn as much as possible in the last few months. My problem is that I would like to estimate the model of Leon et al. (2005). I have shortly summarised the most important equations in the following pdf file: http://hannes.fedorapeople.org/leon2005.pdf My main question is now how could I introduce these two additional terms into the Likelihood

Dear All, The following code save my graphs as pdf: pdf("j:/mix.pdf", width = 18, height = 16) grid.newpage() pushViewport(viewport(layout = grid.layout(3,1))) vplayout <- function(x, y) viewport(layout.pos.row = x, layout.pos.col = y) print(Aplot, vp = vplayout(1, 1)) print(Bplot, vp = vplayout(2, 1)) print(Cplot, vp = vplayout(3, 1)) dev.off() How can I save it in

Hi I have implented boxplots in my script to create box plots BoxplotsCheck <- readline(prompt = "Would you like to create boxplots for any Feature? (y/n):")   if (BoxplotsCheck  == "y"){     BoxplotsFeature <- readline(prompt = "Which Feature would you like to create a Boxplot for?:")     BoxplotsFeature <- as.numeric(BoxplotsFeature)     BoxplotsData

Dear All, I would like to plot a scalar (e.g. a temperature) on a non-rectangular domain (or even better: I would simply like to be able to draw a contour plot on an arbitrary 2D domain). I wonder if there is any tool to achieve that with R. I did some online search in particular on the list archives, found several queries similar to this one but was not able to find any conclusive answer. I

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:

This is to announce a new package rms on CRAN. rms goes along with my book Regression Modeling Strategies. The home page for rms is http://biostat.mc.vanderbilt.edu/rms, or go directly to http://biostat.mc.vanderbilt.edu/Rrms for information just about the software. rms is a re-write of the Design package that has improved graphics and that duplicates very little code in the survival

This is to announce a new package rms on CRAN. rms goes along with my book Regression Modeling Strategies. The home page for rms is http://biostat.mc.vanderbilt.edu/rms, or go directly to http://biostat.mc.vanderbilt.edu/Rrms for information just about the software. rms is a re-write of the Design package that has improved graphics and that duplicates very little code in the survival

The problem is badly scaled -- parameters from very small to very big. nlmrt package manages a bit better, but scaling would likely make it and nls both happier. nlxb insists on having its data in a data frame. Here's my code rm(list=ls()) Laptop_sale <- c(1405, 1863,2027,2669,2938,5275,6595,6943,8621,10905,12420,22400,32380,31600,34900,43163,47838,47592) CuSale <- Laptop_sale time

Wolfgang Viechtbauer wrote: > Hello R-Helpers, > > plot(x, y, type="b", pch="1") > > plots x vs. y with both a line and the symbol "1" but how do I change > the "spacing" of the symbol being plotted. In other words, I don't want > to plot the "1" at every data point, but only at every kth point (things > get too

Hello, I'm making barplots out of binary data, typically 0=no 1=yes, but in some instances I have 2=Don't Know. Anyway, when I create a juxtaposed barplot, what appears in the legend is 0,1,and 2. I want it to read No, Yes, Don't Know. I'm not sure how to do that. In addition to this, the other variable that this juxtaposed barplot is grouped by is grade level, which is a

The rms package, a replacement for the Design package, has been updated on CRAN. The most major change is the addition of smooth calibration curves for externally (val.surv function) or internally (calibrate.cph, calibrate.psm) validating a survival model with right-censored data. The polspline package is used to estimate the survival probability at a fixed time point as a function of the

The rms package, a replacement for the Design package, has been updated on CRAN. The most major change is the addition of smooth calibration curves for externally (val.surv function) or internally (calibrate.cph, calibrate.psm) validating a survival model with right-censored data. The polspline package is used to estimate the survival probability at a fixed time point as a function of the

Version 2.2-0 of the rms package is now available. This is a somewhat major update. One major change is not downward compatible: Instead of specifying predictor=. or predictor=NA to Predict, summary, nomogram, survplot, gendata, you just specify the name of the predictor. For example, to get predictions for the default range of x1 and for just 2 values of x2 you might specify Predict(fit,

Version 2.2-0 of the rms package is now available. This is a somewhat major update. One major change is not downward compatible: Instead of specifying predictor=. or predictor=NA to Predict, summary, nomogram, survplot, gendata, you just specify the name of the predictor. For example, to get predictions for the default range of x1 and for just 2 values of x2 you might specify Predict(fit,

Hi R-Users, I have a time series of bivariate observations (x,y,t). I've developed a few panel routines to explore certain aspects of the data, and now would like to break the time series up into smaller chunks to explore the temporal trends. I would like to know if anyone has any experience breaking up time series with lattice. Base graphics offers coplot, utilizing the