similar to: Error in example Glm rms package

Following p.206 of "Statistical Models in S", I wish to change the code for summary.glm() so that it estimates the dispersion for binomial & poisson models when the parameter dispersion is set to zero. The following changes [insertion of ||dispersion==0 at one point; and !is.null(dispersion) at another] will do the trick: "summary.glm" <- function(object, dispersion =

Hi all I am preparing a document using Sweave; a really useful tool. But I am having a problem. Consider this toy example Sweave file: \documentclass{article} \begin{document} <<echo=TRUE,results=verbatim>>= options(width=40) # Set width to 40 characters hide <- capture.output(example(glm)) # Create an example of the problem, but hide the output summary(glm.D93) #

Hello R users I like to extract z values for x1 and x2. I know how to extract coefficents using model$coef but I don't know how to extract z values for each of independent variable. I looked around using names(model) but I couldn't find how to extract z values. Any help would be appreciated. Thanks TM ######################################################### >summary(model) Call:

Hello list, I'm trying to figure out how exactly the specification of nested random effects works in the lmer function of lme4. To give a concrete example, consider the rat-liver dataset from the R book (rats.txt from: http://www.bio.ic.ac.uk/research/mjcraw/therbook/data/ ). Crawley suggests to analyze this data in the following way: library(lme4) attach(rats) Treatment <-

Dear All, I'm not sure of the interpretation of interactions with contrasts. Can anyone help? I do an ANCOVA, dryweight is covariate, block and treatment are factors, c4 the response variable. model<-aov(log(c4+1)~dryweight+treatment+block+treatment:block) summary(model); Df Sum Sq Mean Sq F value Pr(>F) dryweight 1 3.947 3.947 6.6268 0.01076 *

Dear colleagues, I face a problem doing haplotype analyses with haplostats: when I use the haplo.em function, the programme gives an error message because of 'recursive default argument reference.' I am not able to figure out what this means. Could you perhaps help me? The full output is the following: > library(haplo.stats) >

This is a basics beginner question. I attempted fitting a a Poisson GLM to data that is non-integer ( I believe Poisson is suitable in this case, because it is modelling counts of infections, but the data collected are all non-negative numbers with 2 decimal places). My question is, since R doesn't return an error with this glm fitting, is it important that the data is non-integer. How does

Hi group, I'm trying to do a Tukey test to compare the means of a factor ("treatment") with three levels in an lme model that also contains the factors "site" and "time": model = response ~ treatment * (site + time) When I enter this model in csimtest, it takes all but the main factor "treatment" as covariables, not as factors (see below). Is it

Hi, I have a experiment with block, plots, sub-plots, and sub-sub-plots with repeated measures and 3 factors (factorial design) when we have been observed diameter (mm), high (cm) and leaves number (count). However, we don't have one treatment in one factor, so, my design is unbalanced. On a previous message here, a friend tell me that "It appears to me that your design is a split-split

Ronaldo, Further to my previous posting on your Glycogen nested aov model. Having read Douglas Bates' response and Reflected on his lmer analysis output of your aov nested model example as given.The Glycogen treatment has to be a Fixed Effect.If a 'treatment' isn't a Fixed Effect what is ? If Douglas Bates' lmer model is modified to treat Glycogen Treatment as a purely

Dear All I am trying to do a repeated measures analysis using lmer and have a number of issues. I have non-orthogonal, unbalanced data. Count data was obtained over 10 months for three treatments, which were arranged into 6 blocks. Treatment is not nested in Block but crossed, as I originally designed an orthogonal, balanced experiment but subsequently lost a treatment from 2 blocks. My

Hello, I am performing cor() of some of my data. For example, I'll do 3 corr() (many variables) operations, one for each of the three treatments. I then do the following: i <-lower.tri(treatment1.cor) cor(cbind(one = treatment1.corr[i], two = treatment2.corr[i], three = treatment3.corr[i])) Does this operation above tell me how correlated each of the three treatments is? Because this

There may be a bug in the anova.glm function. deathstar[32] R R : Copyright 2004, The R Foundation for Statistical Computing Version 2.0.1 (2004-11-15), ISBN 3-900051-07-0 R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project

Anyone know how to get p-values for the t-values from the coefficients produced in vglm? Attached is the code and output ? see comment added to output to show where I need p-values + print(paste("********** Using VGAM function gamma2 **********")) + modl2<- vglm(MidPoint~Count,gamma2,data=modl.subset,trace=TRUE,crit="c") + print(coef(modl2,matrix=TRUE))

Hi All, I have been experiencing a strange issue ever since I upgraded my R and the packages. I cannot plot anything to the CairoPNG device.For an example:> Cairo(600, 600, file="plot.png", type="png", bg="white")> plot(1:10)> dev.off()will create an empty plot.png file. I am running R 2.12.1 (32bit) on Windows 7 (64bit). ----------------------------->

Dear all, I noticed in the latest R version (R.2.12.1) that the readPNG gives following warning when running the example code in the help file (or when using any other png for that matter) : 50: In rasterImage(img, 1.2, 1.27, 1.8, 1.73) : Per-pixel alpha not supported on this device No picture is shown, and code I used to be able to run, doesn't run any more. > sessionInfo() R version

I am attempting to run a glm with a binomial model to analyze proportion data. I have been following Crawley's book closely and am wondering if there is an accepted standard for how much is too much overdispersion? (e.g. change in AIC has an accepted standard of 2). In the example, he fits several models, binomial and quasibinomial and then accepts the quasibinomial. The output for residual

Dear Help List, Thanks in advance for reading...I hope my questions are not too ignorant. I have an experiment looking at evolution of wing size [centroid] in fruitflies and the effect of 6 different experimental treatments [treatment]. I have five replicate populations [replic] in each treatment and have reared the flies in two different temperatures [cond] to assay the wing size, making

Dear experts, I am trying to perform an association using snpStats. I have a snp matrix called 'plink' which contains my genotype data (as a list of $genotypes, $map, $fam), and a phenotype data frame which contains the outcomes (outcome1, outcome2,...) I would like to associate with the genotype. My question is, how do I loop through the outcomes? This type of data seems different from

Hi, We released individual birds into a room with 2 trees. We counted the number of visits to each of the 2 tree. One of the trees is always a control tree and the other tree is either treatment 1, treatment 2 or treatment3 or treatment 4. Ind Treat ContrTree ExpTree Total visits 1 1 11 16 27 1 2 6 9 15 1 3 5 13 18 1 4 11 25 36 2 1 2 3 5 4 1 6 7 13 4 3 4 4 8 4 4 2 5 7 6 1 1 1 2 6 4 5 16 21