search for: rats

..., I thought it would be very interesting to analyse it also using lmer and to see how the different approaches and outputs differs - from the more or less manual old-school (?) approach in Sokal, aov in Crawley, and to mixed models by lmer. In the example, three treatments (Treatment) with two rats (Rat) each (i.e six unique rats in total). Three liver preparations (Liver) are taken from each rat (i.e 18 unique liver preparations), and two glycogen readings (Glycogen) are taken from each liver preparation (36 readings). We want to test if treatments has affected the glycogen levels. The...

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 <- factor(Treatment) Rat <-factor(Rat) Liver<-factor(Liver) m1<-lmer(Glycogen~Treatment+(1|Treatment/Rat/Liver)) T...

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 1st Qu.:1 1st Qu.:1.0 1st Qu.:1 Median :141.0...

...hn -----Original Message----- From: John Wilkinson (pipex) [mailto:wilks at dial.pipex.com] Sent: 07 September 2005 12:04 PM To: "Ronaldo Reis-Jr." Cc: r-help Subject: Re: [R] Doubt about nested aov output Ronaldo , It looks as though you have specified your model incorrectly. In the Rats example ,the Treatment is the only fixed effect,Rat and Liver are random effects In aov testing for sig of 'Means' of Random Effects is pointless and that is why 'p' values are not given.Further more the interaction between a Random Effect and a Fixed Effect is also a Random Effect...

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...

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: model<-aov(Glycogen~Treatment/Rat/Liver + Error(Tre...

...new to R and AIC scores but what I get from coxme seems wrong. The AIC score increases as p-values decrease. Since lower AIC scores mean better models and lower p-values mean stronger effects or differences then shouldn't they change in the same direction? I found this happens with the data set rats as well as my own data. Below is the output for two models constructed with the rats data set. >library(survival) >data(rats) > str(rats) 'data.frame': 150 obs. of 4 variables: $ time : int 101 104 104 77 89 88 104 96 82 70 ... $ tumor : int 0 0 0 0 0 1 1 1 0 1 ... $ tr...

...ce between models like lmer(Glycogen~Treatment+(1|Rat)+(1|Rat:Liver)) and lmer(Glycogen~Treatment+(1|Treatment:Rat)+(1|Treatment:Rat:Liver)) is more about the meaning of the levels of "Rat" than about the meaning of "Treatment". As I understood it there are three different rats labelled 1. There is a rat 1 on treatment 1 and a rat 1 on treatment 2 and a rat 1 on treatment 3. Thus the levels of Rat do not designate the "experimental unit", it is the levels of Treatment:Rat that do this. -- Ken Knoblauch Inserm U371 Cerveau et Vision Dept. of Cognitive Neurosc...

Hi! All, I am working on a dataset 'rat' with dimension 20500x363. I have calculated pca of samples (columns). Now I am trying to plot first two principle components with specified columns in different color. I have done following so far: > dim(rat) [1] 20500 363 >#specifying columns to be colored in red >

Dear R-users, I am trying to analyse the data of the box 10.5 in the Biometry from Sokal and Rohlf (2001) using R. This is a three-level nested anova with equal sample size : 3 different treatments are compared ; 2 rats (coded 1 or 2) / treatment are studied ; 3 preparations (coded 1, 2 or 3) / rats are available ; 2 readings of the glycogen content / preparations are realised. Treatment is fixed whereas Rats (nested in Treatment) and Prep (nested in Rats) are random effects. According to a previous discuss...

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. > rat.toxic<-read.table(file="Rats.csv",header=T,row.names=NULL,sep=",") > attach(rat.toxic) > names(rat.toxic) [1] "Dose" "Sex" "Dead&q...

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]]

Fellow R users, I'm using the BCE {BCE} function to run a Bayesian sediment mixing model. The aim is to find the optimum % contribution from each of the 4 source areas that can yield the target geochemistry. I have geochemistry for 4 source areas called Rat: Rat<-read.table(text="CaO MgO Na2O Al2O3 Topsoils 2.511250 0.7445500 0.7085500 14.10375 ChannelBanks

I am using the following: library(RODBC) chan = odbcConnectExcel("rats-lda") rats.lda = sqlFetch(chan, "data") close(chan) And getting the following error message: > library(RODBC) Error in library(RODBC) : there is no package called ?RODBC? > chan = odbcConnectExcel("rats-lda") Error: could not find function "odbcConnectExcel&quo...

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

Hi, I have taken one microarray experiment and trying to implement same statistical measures what they have done.I have taken datasets from GEO platform with accession number GSE1557. In the experiment,about half of double transgenic rats (dTGR) over-expressing the human renin and angiotensinogen genes die by age 7 weeks of terminal heart failure (THF); the other (preterminal) half develops cardiac damage, but survives to week 7. The aim of the study was to elucidate the difference in cardiac gene expression of dTGR with THF compare...

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 <-

Steffen, When the new biomaRt tries to load it errors out because I do not have RMySQL installed. There is not a Windows binary for RMySQL and it does contain C code that I do not know how to build. I do not use the MySQL option in biomaRt. Does RMySQL need to be a required dependency? Below is my screen output and sessionINfo. require(biomaRt) Loading required package: biomaRt Loading required

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 =

...ch estimator using (here I guess) an emperical (unstructered/exchangeable?) ICC. Shouldent it be, at least to some extend, comparable to the robust z-test, for rx : 2*pnorm(-0.239/0.0816)=0.0034 ? Any help/hints are appreciated. Steen and R 2.2.1, survival 2.21 on win XP. library(survival) data(rats) marginal.model <- survreg(Surv(time, status) ~ rx + cluster(litter), rats ) summary(marginal.model) > library(survival) > data(rats) > marginal.model <- survreg(Surv(time, status) ~ rx + cluster(litter), rats ) > summary(marginal.model) Call: survreg(formula = Surv(time, stat...