similar to: plotting pca of samples in different colors

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

Hi, I''m trying to understand the lme output and procedure. I''m using the Crawley''s book. I''m try to analyse the rats example take from Sokal and Rohlf (1995). I make a nested analysis using aov following the book. > summary(rats) Glycogen Treatment Rat Liver Min. :125.0 Min. :1 Min. :1.0 Min. :1 1st Qu.:135.8

Your response nicely clarifies a question that I've had for a long time, but which I've dealt with by giving each subject a unique label. Unless I'm missing something, both techniques should work as the toy example below gives exactly the same output in all 3 cases below (forgetting about the convergence problem). Would there be a reason to prefer labeling the levels one way or

Hi All, I would like to ask a question on fixed and random effecti in lme. I am fiddlying around Mick Crawley dataset "rats" : http://www.bio.ic.ac.uk/research/mjcraw/statcomp/data/ The advantage is that most work is already done in Crawley's book (page 361 onwards) so I can check what I am doing. I am tryg to reproduce the nested analysis on page 368:

Dear all, During my pre-R era I tried (yes, tried) to understand mixed models by working through the 'rat example' in Sokal and Rohlfs Biometry (2000) 3ed p 288-292. The same example was later used by Crawley (2002) in his Statistical Computing p 363-373 and I have seen the same data being used elsewhere in the litterature. Because this example is so thoroughly described, I thought

Hi i would like to use some graphs or tables to explore the data and make some sensible guesses of what to expect to see in a glm model to assess if toxin concentration and sex have a relationship with the kill rate of rats. But i cant seem to work it out as i have two predictor variables~help?Thanks.:) Here's my data. >

Hi, When I call the *switch* function first time, it works. but when I call it at the second time, it does nothing. The version I use is R version 2.9.0 Under development (unstable) (2009-02-21 r47969) here is the output: > organism="human" > species <- switch(organism, human <- "Hs", fly <- "Dm", mouse <-

When I try to run the example from Variogram with an lme object, I get an error (although summary works): R : Copyright 2005, The R Foundation for Statistical Computing Version 2.2.1 (2005-12-20 r36812) ISBN 3-900051-07-0 ... > fm1 <- lme(weight ~ Time * Diet, BodyWeight, ~ Time | Rat) Error: couldn't find function "lme" > Variogram(fm1, form = ~ Time | Rat, nint =

For example, c("dog.is.an.animal", "cat.is.an.animal", "rat.is.an.animal"). How can I identify the common prefix is ".is.an.animal" and delete it to give c("dog", "cat", "rat") ? Thanks _________________________________________________________________ [[alternative HTML version deleted]]

Simple problem but I don't see the answer. I'm trying to clean up some data I have 120 columns in a data.frame. I have one value in a column named "blaw" that I want to change. How do I find the coordinates. I can find the row by doing a subset on the data.frame but how do I find out here "blaw " is in columns without manually counting them or converting names(Df) to a

Dear R-listers, Does anybody know what is the correct source of "rats" dataset in survival package? The help gives the following information: Rat data from survival5 Description: 48 rats were injected with a carcinogen, and then randomized to either drug or placebo. The number of tumors ranges from 0 to 13; all rats were censored at 6 months after randomization.

Runninn R.2.3.1 Windows XP I have a dataset just imported from SPSS. It has any number of 99's as missing data and it looks like the next dataset will have custom missing codes. I have abouat 120 variables and an N of 2000. "I think" thatI would like to apply a function to the data.frame (or to a matrix of the data if needed) to recode all the 99's to NA. I thought that I

Dear R Users, Am using lm() with contrasts as below. If I skip the contrasts() statement, I get the coefficient names to be > names(results$coef) [1] "(Intercept)" "VarAcat" "VarArat" "VarB" which are much more meaningful than ones based on integers. Can anyone tell me how to get R to keep the coefficient names based on the factor levels

Dear friends - I'm having troubles with nlme fitting a simplified model as shown below eliciting the error Error in chol.default((value + t(value))/2) : ? the leading minor of order 1 is not positive definite - I have seen the threads on this error but it didn't help me solve the problem. The model runs well in brms and identifies the used parameters even with fixed effects for TRT?

Hello, I am working on some examples of GLMM for my students but I am afraid that my way of preparing a dataframe to pass to lmer will make them think that R is a very difficult and un-natural language. Here is for example a simple data set about approval ratings on two different surveys for a random sample of 1600 individuals. > ## Example: Ratings of prime minister (Agresti, Table 12.1,

As a followup to pi-day, I attempted to make a .gif of a simulation based estimation of pi by plotting points inside a single quadrant of a circle (a la?http://www.drewconway.com/zia/?p=2667 ). ?When rendering the individual x,y pairs with points() I intermittently see points crop up around (2,0.5) but the input values for x and y are bounded between 0 and 1. square<-structure(c(0, 0, 1, 1, 0,

I am having two problems creating data frames that I have solutions, but they really seem like kludges and I assume I just don't understand the proper R way of doing things. The first situation is I have an set of uneven data vectors. When I try to use them to create a data frame I would like the bottoms of them padded with NAs, without explicitly specifying that. When I do: anxiety.data =

As if last weekend's UEFI debacle wasn't bad enough, it now seems the latest C8 kernel (4.18.0-193.14.2) is incompatible with the current ZFSOnLinux packages (0.8.4-1). When booted to the latest kernel, ZFS is inaccessible on my C8 storage server. When I back off to the prior kernel (4.18.0-193.6.3), all is well. If a local ZFS system is unavailable to the C8 kernel support folks,

This is extremely annoying (and it smells of rat). As of this time (1013 am ET), Google issues the following nasty warning: "This site may harm your computer" when I try to visit any of the CRAN package repository sites or CRAN task view. The funny thing is that Google's Safe Browsing Diagnostic page (it gives diagnostics about any page that it may contain Malware) does not seem

I have used foreach() for parallel computing but in the current problem, it is not working. Given the volume and type of the data involved in the analysis, I will try to give below the complete code without reproducible example. In short, each R environment will draw a set of separate files, perform the analysis and dump in separate folders. splist <- c("juoc", "juos",