similar to: standard format for newdata objects

Is anybody working on a way to standardize the creation of "newdata" objects for predict methods? When using predict, I find it difficult/tedious to create newdata data frames when there are many variables. It is necessary to set all variables at the mean/mode/median, and then for some variables of interest, one has to insert values for which predictions are desired. I was at a

I am new to R. I want to graph group data using a "Traditional Bar Chart with Standard Error Bar", like the kind shown here: http://samiam.colorado.edu/~mcclella/ftep/twoGroups/twoGroupGraphs.html Is there a simple way to do this? So far, I have only figured out how to plot the bars using barplot. testdata <- scan(, list(group=0,xbar=0,se=0)) 400 0.36038 0.02154 200 0.35927

Among others, datam contains the columns: logconc, tm, dose, subj, bilirubin. None of these are factor variables. The following compartment models work (the first still has not converged after 100 interations): res1 <- nlme(logconc~p2+p3+log(dose/(exp(p1)-exp(p2))* (exp(-exp(p2)*tm)-exp(-exp(p1)*tm))),start=list(fixed=c(5,-2,-0.1)), fixed=list(p1+p2+p3~1),control=list(maxIter=100),

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 <-

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

Perhaps you can see somethign I can't - or perhaps there is a better way for me to get information for you ? Let me know if there is as this server is not live yet... All the best, Noel NB ATTACHMENTS REMOVED FOR LIST POSTING Domain/network info: Domain = UK Win2000 DC (192.168.5.4) = BRAIN Live Samba server 2.2.3a (192.168.5.5) = BELLY New Samba server 2.2.3a

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 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 List, After including cluster() option the coxreg (from eha package) produces results slightly different than that of coxph (from survival) in the following time-dependent treatment effect calculation (example is used just to make the point). Will appreciate any explaination / comment. cheers, Ehsan ############################ require(survival) require(eha) data(heart) # create weights

Hi everybody: I?m trying to rewrite some routines originally written for SAS?s PROC NLMIXED into LME4's glmer. These examples came from a paper by Nelson et al. (Use of the Probability Integral Transformation to Fit Nonlinear Mixed-Models with Nonnormal Random Effects - 2006). Firstly the authors fit a Poisson model with canonical link and a single normal random effect bi ~ N(0;Sigma^2).The

Hello, I'm new to R and I'm trying to define new quite simple variable but I'm struggling with R syntax (when coming to dates) for a while and still getting <errors> on it. I would be very grateful if someone could help me with this, to be able to move on. I have the following variables: - Transplant.date - Faildate - Death.date The new variable Time should do the

Hi, I've been doing an experiment, measuring the dead-zone-diameters of bacteria, when they've been grown with paper diffusion disks of antimicrobial. There are two groups, or treatments - one is bacteria that have been cultured in said antimicrobial for the past year, the other group is of the same species, but lab stock and has not gone had any prior contact with the antimicrobial.

Hello all! As beginner I'm struggling for a while with time zones issue and can't find a suitable solution. I would be grateful for any help. Dataset imported from excel has a variable transplant.date which has been recorded with CET time zone. > subDataset$transplant.date [1] "2000-01-01 CET" "2000-01-01 CET" "2000-01-02 CET" "2000-01-02 CET"

Hi, I make this model using lme m.lme <- lme(Glycogen~Treatment,random=~1|rTrt/Liver) How to make this using lmer? I try > m.lmer <- lmer(Glycogen~Treatment+(1|rTrt/Liver)) Erro em lmer(Glycogen ~ Treatment + (1 | rTrt/Liver)) : entry 0 in matrix[0,0] has row 2147483647 and column 2147483647 Al??m disso: Mensagem de aviso: / not meaningful for factors in: Ops.factor(rTrt, Liver)

Hello, unfortunately I have I big problem I can't solve. I have to analyse if a gene is tissue specific. For example for the gene xyz I have following expression values: Heart Liver Brain 8.998497 10.013561 12.277407 9.743556 10.137574 11.033957 For every tissue I have two values from two different experiments. Now I want to test if Heart is significant higher

Notification: You are hereby challenged to see What the Media doen't want you to know. The Truth about Abortion with your own eyes. at http://www.getabortion.info What you will never see on TV. Its not a blob of tissue. Every scientist , even those for abortion, will tell you that life begins at conception. Once the egg and sperm join there is human life. Why won't the media show

Notification: You are hereby challenged to see What the Media doen't want you to know. The Truth about Abortion with your own eyes. at http://www.getabortion.info What you will never see on TV. Its not a blob of tissue. Every scientist , even those for abortion, will tell you that life begins at conception. Once the egg and sperm join there is human life. Why won't the media show

Hi! How to calculate the correct SE of mean in a nested or spliplot anova? Nested example: --------------------- m <- aov(Glycogen~Treatment+Error(Treatment/Rat/Liver)) > m Call: aov(formula = Glycogen ~ Treatment + Error(Treatment/Rat/Liver)) Grand Mean: 142.2222 Stratum 1: Treatment Terms: Treatment Sum of Squares 1557.556 Deg. of Freedom 2 Estimated

Since I read the list in digest form (and was out ill yesterday) I'm late to the discussion. There are 3 steps for predicting survival, using a Cox model: 1. Fit the data fit <- coxph(Surv(time, status) ~ age + ph.ecog, data=lung) The biggest question to answer here is what covariates you wish to base the prediction on. There is the usual tradeoff between too few (leave out something