similar to: plot.summary.Design()

Hi, 1. I want 95% not 99% confidence intervals in my summary.Design plot using the Design package. Putting conf.int=.95 as an argument in plot does not work. The default appears to be .99 not .95 as stated in the package Design manual (p. 164). 2. My sweave chuck is below and my output is attached as well as linked here: http://www.sonoma.edu/users/s/stanny/330A/project/ciplot.pdf 3.

Dear R-help How can one get survplot() to place the number at risk just below the survival curve as opposed to the default which is just above the x-axis? I tried the code bellow but the result is not satisfactory as some numbers are repeated several times at different y coordinates and the position of the n.risk numbers corresponds to the x-axis tick marks not the survival curve time of

Hello, The following script allows for Weibull plots using R. Its output is similar to the output of the wblplot function (or weibplot function) in MATLAB. As opposed to the previously mentioned function it does not require proprietary software. Instead, it is based on R. My code also allows for a graphical visualization of weibull fitted data. In particular, data can be represented by a

Hello, I would like to create a plot composed of stacked boxes (squares or rectangles), where the size of the box would represent the frequency of observations based on a categorical variable (group), the color would represent the proportion of success (binary) within that group (outcome) on a predetermined color scale. Ideally the boxes can be stacked from the bottom left to the top right based

Dear R-help, I would like to add text to each of four panels in a plot generated by xyplot in lattice library. A sample code is given below, the plot generated has the first label repeated in all panels! How can I get the labels to be different in each panel? library(lattice) x <- rnorm(400) y <- rnorm(400) a <- gl(4, 100) xyplot(y~x|a, panel=function(...){

dear list: I used the nlme library according to the great Pinheiro/Bates book, on R2.3, WinXp Lac.lme is an lme object with unbalanced data, group is a factor variable with three levels, when I tried to plot the residuals by group I got this error msg: >plot(Lac.lme,resid(.,type='p')~fitted(.)|group) Error in limits.and.aspect(prepanel.default.xyplot, prepanel = prepanel, :

Hi everybody, I am a new user of R, design package. I am trying to plot the estimated hazard ratio's of my cox regression model with the confidence intervals. But I keep getting the message:Error in `contrasts<-`(`*tmp*`, value = "contr.treatment") : contrasts can be applied only to factors with 2 or more levels I have dichotomous, categorical as well as continuous

Dear R-friends I fitted a regression using GLM and plotted fitted values agaist X axis. Now I need plot upper and lower confidence intervals (95%) for this fit. Acoording to ?lm help and can use something like : [ predict.lm(mod, se.fit=T, interval = "confidence", level=.95) ] to estimate confidence intervals. In fact this command run fine, but I can´t plot upper and

Hello, I have a longitudinal data with about 30 subjects. I used xyplot() to plot the longitudinal data. One problem is that xyplot() recycles the color of auto.key so that every 7th subject has the same color (symbol if setps() was used). Is there a way so that every subject will have a unique color or symbol? Thanks Osman -- Osman O. Al-Radi, MD, MSc, FRCSC Fellow, Cardiovascular Surgery

Dear R-help subscribers, I use xyplot to plot longitudinal data as follows: score<-runif(100,-4,5) group<-sample(1:4,100,rep=T) subject<-rep(1:25,4) age<-rep(runif(4,1,40),25) df<-data.frame(score,group,age,subject) xyplot(score~age|group, group=subject, panel=function(...){ panel.loess(...,lwd=4) panel.superpose(...)} ,data=df) this produced a plot with four panels one for each

Hi, I want to plot grouped bars to compare 95% confidence interval estimates from two models. Each bar represents a 95% confidence interval estimate of a coefficient from one of the two models. Each group represents confidence interval estimates of the same coefficient from the two models. I think such a bar plot will nicely present whether 95% confidence interval estimates of the same

Hello, When I try to use TukeyHSD in the following way it shows the confidence interval corresponding to the last factor only. > throughput.aov <- aov(Throughput~No_databases+Partitioning+No_middlewares+Queue_size,data=throughput) plot(TukeyHSD(throughput.aov)) # I expected here to see the confidence intervals for all factors but see only the last. OTOH this one works but then it is

I am using the coxph, survfit and summary.survfit functions to calculate an estimate of predicted survival with confidence interval for future patients based on the survival distribution of an existing cohort of subjects. I am trying to understand the calculation and interpretation of the std.err and confidence intervals printed by the summary.survfit function. Using the default confidence

Hi, for the data below: time<-c(rep(1:10,5)) y<-time+rnorm(50,5,2) subject<-c(rep('a',10),rep('b',10),rep('c',10),rep('d',10),rep('e',10)) group<-c(rep('A',30),rep('B',20)) df<-data.frame(subject,group,time,y) I'd like to produce a plot with a single pannel with two loess curves one for each group. the code below does

Dear R-users, I'm currently using the multcomp package to produce plots of means with 95% confidence intervals i.e. mult<-glht(lm(response~treatment, data=statdata), linfct=mcp(treatment="Means")) plot(confint(mult,calpha = sig)) Unfortunately the y-axis on the plot appears to be fixed and hence if the labels on the y-axis (treatment levels) are too long, then they are not

Dear R-help, Would it be appropriate to do the following to calculate a p-value for the difference between c-ind of x1 and c-inx of x2 using the output from rcorrp.senc() > r<-rcorrp.senc(x1,x1,y) > pValue<-1-pnorm((r[11]-r[12])/(r[2]/r[5])*1.96) Osman O. Al-Radi, MD, MSc, FRCSC Chief Resident, Cardiac Surgery University of Toronto, Canada

Hi everyone, I am fitting a cox proportional hazard model with a continuous variable "x" as the covariate: fit<-coxph(Surv(time, status)~x) Now I wanted to make a plot of survival probability vs. the covariate, and the 95% confidence interval for the survival probability. It's just like a Kaplan-Meier Survival curve, except now the x axis represents the value of covariate, not

Hello! I would like to draw a graph like the following: http://www.optics.rochester.edu/workgroups/cml/opt307/spr04/pavel/plot_small.jpg Aim is to plot confidence intervals of treatments for X(=response1) and Y(=response2) axis simultaneously to visualize aggreement of confidence interval for two responses. Can anyone please provide me some direction to start with? Thanks! -- Kim.

Hi, there! I'm having kind this same problem https://stat.ethz.ch/pipermail/r-help/2008-October/178221.html but I want to display another plot of my data, which is a point with two arrows indicating confidence interval, in the same graph that I've just plotted another, but the "add=T" is not functioning, I'm getting the same error Warning messages: 1: In plot.window(...)

Hello, I'm ashamed to admit that I'm an R and meta-analysis neophyte just when my colleagues and I are attempting to run R for that latter purpose. We would like to construct a graph of the 95% confidence intervals of odds ratios (inverse odds ratios, actually). We are using the metaplot() function in the "rmeta" package. While we can construct a basic plot, we cannot configure