similar to: a plot of stacked boxes

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(...){

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 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, :

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

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, 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-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

Hi, i have a function: zz<- (constrOptim(c(.5,0), fr, grr, ui=rbind(c(-1,0),c(1,-1)), ci=c(-0.9,0.1))) i can get the result by using command (for example): zz$par now if i can use multicore: zz<-parallel(constrOptim(c(.5,0), fr, grr, ui=rbind(c(-1,0),c(1,-1)), ci=c(-0.9,0.1))) result < collect(zz) i cant get my the result: result$par because multicore add process id. for example:

Hello, I have 3 columns : a, b and a*b I would like to find the pair (a,b) so that a*b is the minimum but not from the points I measured but from the fit of the curve (I have more points that the ones given below but I fit only on this part because I know that the minimum a*b is in this interval). I thought doing it this way : - to fit a*b=f(a) abfit<-lm(ab ~ poly(a,8,raw=T)) - to use the

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Brokers Move On ERMX! EntreMetrix Inc. (ERMX) $0.18 Heavy trading today as ERMX announced its launch of digital support tools for its portfolio companies. Brokers are getting ahead of this steady climb as they grab up large blocks of shares for there clients. Look at the numbers and get on ERMX Friday morning! KNEEguru: Are there any contraindications for the procedure? if the laws of physics

Dear R Users, I am having trouble with lmer. I am looking at recombinant versus non recombinant individuals. In the response variable recombinant individuals are coded as 1's and non-recombinant as 0's. I built a model with 2 fixed factors and 1 random effect. Sex (males/females) is the first fixed effect and sexual genotype (XY, YY, WX and WY) the second one. Sexual Genotype is

<http://r.789695.n4.nabble.com/file/n4657370/untitled.jpg> Hi Everyone, I am a new comer to R circle. I am trying to simulate a VAR estimation. The problem is given above in the image. Assume that the errors are iid and normally distributed. Here the number of observations x1=x2=64. I've written the code below. Not sure if it is correct.

I am trying to sample a subset from a matrix using sample. The size of the matrix is 20X 1532. It works fine with this, but when I transpose the matrix and try to sample it, it returns null. pick.set<-sample(tissue.exp.t,5,replace=FALSE,prob=NULL) Is there something that I am missing here ? Thanks ../Murli

Hello, I'm using aov() to analyse changes in brain volume between males and females. For every subject (there are 331 in total) I have 8 volume measurements (4 different brain lobes and 2 different tissues (grey/white matter)). The data looks like this: Subject Sex Lobe Tissue Volume subect1 1 F g 262374 subect1 1 F w 173758 subect1 1 O g 67155 subect1 1 O w 30067 subect1 1 P g 117981

Hi, I am doing an analysis to see if these is tissue specific effects on the gene expression data . Our data were collected from 6 different labs (batch effects). lab 1 has tissue type 1 and tissue type 2, lab 2 has tissue 3, 4,5,6. The other labs has one tissue type each. The 'sample' data is as below:

I'm working with a nested model (mixed). I have four factors: Patients, Tissue, sex, and tissue_stage. Totally I have 10 patients, for each patient, there are 2 tissues (Cancer vs. Normal). I think Tissue and sex are fixed. Patient is nested in sex,Tissue is nested in patient, and tissue_stage is nested in Tissue. I tried aov and lme as the following, > aov(gene ~ tissue + gender +

Hello, I've used this command to analyse changes in brain volume: mod1<-aov(Volume~Sex*Lobe*Tissue+Error(Subject/(Lobe*Tissue)),data.vslt) I'm comparing males/females. For every subject I have 8 volume measurements (4 different brain lobes and 2 different tissues (grey/white matter)). As aov() provides only type I anovas, I would like to use lmer() with type II, however, I have

Dear R users, I have a data frame in the form below, on which I would like to make normality tests on the values in the ExpressionLevel column. > head(df) ID Plant Tissue Gene ExpressionLevel 1 1 p1 t1 g1 366.53 2 2 p1 t1 g2 0.57 3 3 p1 t1 g3 11.81 4 4 p1 t2 g1 498.43 5 5 p1 t2 g2 2.14 6 6 p1 t2 g3 7.85 I

My small brain is having trouble getting to grips with lme() I wonder if anyone can help me correctly set the random = argument to lme() for this kind of setup with (I think) 9 variance/covariance components ... Study.1 Study.2 ... Study.10 Treatment.A: subject: 1 2 3 4 5 6 etc. 28 29 30 Treatment.B: subject: 31