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Hi, I am trying to convert the following NLMIXED code to NLME, but am running into problems concerning 'Singularity in backsolve'. As I am new to R/S-Plus, I thought I may be missing something in the NLME code. NLMIXED *********** proc nlmixed data=kidney.kidney; parms delta=0.03 gamma=1.1 b1=-0.003 b2=-1.2 b3=0.09 b4=0.35 b5=-1.43 varu=0.5; eta=b1*age+b2*sex+b3*gn+b4*an+b5*pkn+u;

Dear Sir/Madam, I converted my Stata Rgenmetvl.dta file with read.dta succesfully in R's Rgenmetvl.Rdata. However, factors give problem in certain calculations, e.g.: > mean(Rgenmetvl$sex) [1] NA Warning message: In mean.default(Rgenmetvl$sex) : argument is not numeric or logical: returning NA What can I do? In addition to means, summary(Rgenmetvl$sex) works perfectly. Best regards,

I'm trying to perform ANCOVAs in R 1.14, on a Mac OS X, but I can't figure out what I am doing wrong. Essentially, I'm testing whether a number of quantitative dental measurements (the response variables in each ANCOVA) show sexual dimorphism (the sexes are the groups) independently of the animal's size (the concomitant variable). I have attached a 13-column matrix as a data

Dear List, I have tried a stratified Cox Regression, it is working fine, except for the "Anova"-Tests: Here the commands (should work out of the box): library(survival) d = colon[colon$etype==2, ] m = coxph(Surv(time, status) ~ strata(sex) + rx, data=d) summary(m) # Printout ok anova(m, test='Chisq') This is the output of the anova command: > Analysis of Deviance Table

Dear R users, I noticed a problem in the anova command when applied on a single coxph object if there are missing observations in the data: This example code was run on R-2.6.1: > library(survival) > data(colon) > colondeath = colon[colon$etype==2, ] > m = coxph(Surv(time, status) ~ rx + sex + age + perfor, data=colondeath) > m Call: coxph(formula = Surv(time, status) ~ rx +

Full_Name: Soren Feodor Nielsen Version: 2.5.0 OS: linux-gnu Submission from: (NULL) (130.225.103.21) The print-out of xtable in the following example is wrong; instead of yielding the correct ci's for the second model it repeats the ci's from the first model. require(xtable) require(MASS) data(cats) b1<-lm(Hwt~Sex,cats) b2<-lm(Hwt~Sex+Bwt,cats)

Hi Folks, I have repeated measures for data on association time (under 2 acoustic condtions) in male and female frogs as they grow to adulthood (6 timepoints). Thus, two within-subject variables (Acoustic Condition: 2 levels, Timepoint: 6 levels) and one between-subject variable (Sex:male or female). I am pretty sure my distributions depart from normality but I would first like to simply run a

Dear all: "predict.glm" provides an example to perform logistic regression when the response variable is a tow-columned matrix. I find some paradox about the degree of freedom . > summary(budworm.lg) Call: glm(formula = SF ~ sex * ldose, family = binomial) Deviance Residuals: Min 1Q Median 3Q Max -1.39849 -0.32094 -0.07592 0.38220 1.10375

Hi, I am trying to run an analysis of variance using R. in my data table "x" is a continuous variable lengthof 200 and "p" is a categorical variable also of length 200 and p is anyone of three categories 1,2 or ,3. if I run summary(aov(x~p,data=test)) I get Response: x Df Sum Sq Mean Sq F value Pr(>F) p 1 3174.7 3174.7 42.749 5.175e-10 ***

Hi, I'm reading van Belle et al "Biostatistics" and trying to run a cox test using a dataset from: http://faculty.washington.edu/~heagerty/Books/Biostatistics/chapter16.html (Primary Biliary Cirrhosis data link at top of the page), I'm using the following code: --------------- start of code library(survival) liver <-

Dear All, I get the following error when using either SASxport or Hmisc to read an .xpt file: > w <- read.xport("D:/consult/Trophos/dnp/base/TRO_ds_20081006.xpt") Erreur dans factor(x, f$value, f$label) : invalid labels; length 15 should be 1 or 14 > z<- sasxport.get("D:/consult/Trophos/dnp/base/TRO_ds_20081006.xpt") Erreur dans factor(x, f$value, f$label) :

I just had a manuscript returned with the biggest problem being the analysis. Instead of using principal components in a regression I've been asked to analyze a few variables separately. So that's what I'm doing. I pulled a feather from young birds and we quantified certain aspects of the color of those feathers. Since I often have more than one sample from a nest, I thought I

Dear all, How to obtain the odds ratio (OR) and 95% confidence interval (CI) with 1 standard deviation (SD) change of a continuous variable in logistic regression? for example, to investigate the risk of obesity for stroke. I choose the happening of stroke (positive) as the dependent variable, and waist circumference as an independent variable. Then I wanna to obtain the OR and 95% CI with

Hello, I'm trying to integrate example data in the shape of a tab delimited ASCII file into my package and therefore dropped it into the data subdirectory. The build works out just fine, but when I attempt to install I get: ** building package indices ... Error in scan(file, what, nmax, sep, dec, quote, skip, nlines, na.strings, : line 1 did not have 500 elements Calls: <Anonymous>

Hello, I'm trying to do bar plot where 'sex' will be the category axis and 'occupation' will represent the bars and the clusters will represent the mean 'income'. sex occupation income 1 female j 12 2 male b 34 3 male j 22 4 female j 54 5 male b 33 6

I don't think, this has been answered: > I'm trying to run a 3-way within-subject anova in lme with 3 > fixed factors (Trust, Sex, and Freq), but get stuck with handling > the random effects. As I want to include all the possible random > effects in the model, it would be something more or less > equivalent to using aov > > > fit.aov <- aov(Beta ~ >

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 group, I have this type of data x(predictor), y(response), factor (grouping x into many groups, with 6-20 obs/group) I want to fit a linear regression with one common intercept. 'factor' should only modify the slopes, not the intercept. The intercept is expected to be >0. If I use y~ x + factor, I get a different intercept for each factor level, but one slope only if I use y~ x *

When I do t.test on two distributions (see example below), it outputs numerous data about the t.test. What I'd like to do is individually capture some of this data and assign it to other variables. However, I am unable to find anything in the help section. In the example below, the t value is -4.0441 and the p-value is 0.006771 How can I assign these values to two variables, let's

I vote to 'fortunize' Doug Bates on Hierarchical data sets: which software to use? "The widespread use of spreadsheets or SPSS data sets or SAS data sets which encourage the "single table with a gargantuan number of columns, most of which are missing data in most cases" approach to organization of longitudinal data is regrettable."