similar to: Multivariate, multilevel regression?

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

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.

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

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

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:

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

Hello. I am running a multivariate multilevel mixed effects model, and am trying to understand what the interaction term tells me. A very simplified version of the model looks like this: model <- lmer (phq ~ -1 + as.factor(index_phq) * Neuro + ( -1 + as.factor(index_phq)|UserID), data=data) The phq variable is a categorical depression score on 9 depression items (classified by the variable

Hello, I've been trying to answer a problem I have had for some months now and came across multivariate multilevel modeling. I know MPLUS and SPSS quite well but these programs could not solve this specific difficulty. My problem: 9 correlated dependent variables (medical symptoms; categorical, 0-3), 5 measurement points, 10 time-varying covariates (life events; dichotomous, 0-1), N ~ 900.

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" > rats.lda =

Hi, I am 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

On 16/02/2015 8:20 AM, Therneau, Terry M., Ph.D. wrote: > >> > I'm testing out a new version of coxme and R CMD check fails with "could not find function >> > ranef" (or random.effects or fixef, or anything from nlme). The NAMESPACE file has the >> > line below >> >> > importFrom(nlme, ranef, random.effects, fixef,

Hi all. Belove is the example from the cluster-help page wtih the output. I simply cannot figure out how to relate the estimate and robust Std. Err to the p-value. I am aware this a marginal model applying the sandwich 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 :

Dear List, Can anyone please explain the difference between cluster() and frailty() in a coxph? I am a bit puzzled about it. Would appreciate any useful reference or direction. cheers, Ehsan > marginal.model <- coxph(Surv(time, status) ~ rx + cluster(litter), rats) > frailty.model <- coxph(Surv(time, status) ~ rx + frailty(litter), rats) > marginal.model Call: coxph(formula =

Dear friends - We have 25 rats, 14 of these subjected to partial removal of kidney tissue, 11 to sham operation, and then followed for 6 weeks. So far we have data on 26 urine metabolites measured by NMR 7 times during the observation. I have smoothed the measurements by b.splines in fda including a roughness penalty, and inspecting the mean curves for nephrectomized and sham animals indicate

Am 24.11.2015 um 16:04 schrieb L.P.H. van Belle: > Win8.1 use the RATS tools. > Win 10, no RATS. > > Look here : http://trekker.net/archives/group-policy-downloads/ I think you mean RSAT. What do you mean with "Win 10, no RSAT"? RSAT is available for Win10: https://wiki.samba.org/index.php/Installing_RSAT#Download Regards, Marc

Hello, I would like to use Bayesian Networks with R. I have already installed the package called deal which has succefully unpacked (package 'deal' successfully unpacked and MD5 sums checked) . But when I try to write " <- network (df) I have that kind of error message (be low)! rats <- network(rats.df) Error: couldn't find function "network" Thank u for your

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. >

Dear List, My student and I are looking for an optimizer for a nonlinear optimization problem we are working on. The problem we are working on is to try to pick a set of islands on which to eradicate rats for seabird conservation. We have about 50 islands, each of which has some subset of 17 seabird species. Rats are present on all islands, and will cause the seabirds to go extinct unless they

Statistical Programmer (SAS or Splus/R) About the Company: Edwards Lifesciences (NYSE: EW) is a global leader in products and technologies to treat advanced cardiovascular disease, the global leader in acute hemodynamic monitoring and the number-one heart valve company in the world. Headquartered in Irvine, California, Edwards has more than 5,000 employees'' worldwide, selling medical

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