similar to: Randoms interactions in lme

Hi I have a linear model and I want to tests whether the model terms are significant I used anova but F and P value depend on the order of the terms in the model. I have repeated the same analysis in another stat software and F and P value did not differ with order of terms in the model. Can anyone can explain what happen or what am I doing wrong ? In R > anova (lm(Y~X.1+X.2)) Analysis of

Is it possible to get the empirical (sandwich) S.E. estimates for the fixed effects in lme () (thus allowing possibly correlated errors within the group)? In SAS you can get it by the 'empirical' option to PROC MIXED. Cheers, Michael -- Na (Michael) Li, Ph.D. Division of Biostatistics A443 Mayo Building, MMC 303 School of Public Health 420 Delaware

Hi In mixed-model with lme() How can I obtain Type II SS or Type III SS for fixed effect? Thanks Julien

Dear all; This an FAQ. I tried to access lme source script so I can step into it to debug the problems resulting from a lme() call. I used getAnywhere("lme") or nlme:::lme, both produced only the function definition and "UseMethod("lme"). Any idea how to list the source code? TIA, Richard Yang [[alternative HTML version deleted]]

I want to specify two factors and their interaction as random effects using the function lme(). This works okay when I specify these terms using the function Error() within the function aov(), but I can't get the same model fitted using lme(). The code below illustrates the problem. a <- factor(rep(c(1:3), each = 27)) b <- factor(rep(rep(c(1:3), each = 9), times = 3)) c <-

Hello, I have a question regarding the syntax of the lme function in the nlme package. What I'm trying to do is to calculate an estimate of R^2 based on the likelihood ratio test. For this calculation, I need to determine the maximum log-likelihood of the intercept-only model and the model of interest (with the desired factors and interactions). My model has four independent factors (i.e. A,

Hello, I am trying to make the switch from SAS, and I have a fairly elemental problem with syntax using the nlme package for analyzing mixed models. There was a previous question on this topic posted to this list, so I apologize for redundancy, but I didn't understand the advice given to that inquiry. The model I want to run has the following factors: Host (fixed) Sire (random) Dam

Is there anyway to have a function prompt the user for a working directory, equivalent to file.choose()? --Paul

Hi All, I'm taking an Experimental Design course this semester, and have spent many long hours trying to coax the professor's SAS examples into something that will work in R (I'd prefer that the things I learn not be tied to a license). It's been a long semester in that regard. One thing that has really frustrated me is that lme has an extremely counterintuitive way for

Hello everyone, I hope this is a fair enough question, but I don’t have access to a copy of Bates and Pinheiro. It is probably quite obvious but the answer might be of general interest. If I fit a fixed effect with an added quadratic term and then do it as an orthogonal polynomial using maximum likelihood I get the expected result- they have the same logLik.

I want to fit a linear mixed-model with my data but I want to know if I have to center all my responses variables on their median and why I have to do it? Thank Julien Martin [[alternative HTML version deleted]]

Newbies (and others!) may find useful the R Reference Card made available by Tom Short and Rpad at http://www.rpad.org/Rpad/Rpad-refcard.pdf or through the "Contributed" link on CRAN (where some other reference cards are also linked). It categorizes and organizes a bunch of R's basic, most used functions so that they can be easily found. For example, paste() is under the

Hi, How can I read data of unequal number of observations (rows) as is (i.e. without introducing NA for columns of less observations than the maximum. Example: A B C D 1 10 1 12 2 10 3 12 3 10 4 12 4 10 5 10 Thanks in advance. Sachin --------------------------------- [[alternative HTML version deleted]]

Hi I know that p-values doesn't appear anymore in the summary of a linear mixed-model with lmer. However, if I do a mixed logistic regression with lmer using family=binomial, the summary includes a p-values for fixed effects. Is it normal, could I use those p-values to interpret the fixed effects or should I use mcmcsamp to obtain 95% confidence interval? Thanks Julien

Hello everybody, i'm trying to run a lme but am having a problem. My model have many response variables and when i run the anova, the number of interactions (up to six) is great and the p-values of all variables not appear. I wanted to know if i could to control the number of interactions of the model, or if this method is the most advisable for so many variables. Thanks in advance!

Dear all, I wonder if anyone can help me with specifying a right model for my analysis. I am a beginner to lme methods. I was unfortunately not able to find a solution to my problem on my own. Data structure: I have sampled monthly 6 basins during two hydrological cycles, and I have taken several (2 to 4) samples (“replicate”) for each basin and month. I’m trying to relate Shannon diversity

Could someone please suggest a more clever solution to the following problem than my loop below? Given X a 2xN matrix X, and I a k-subset of N, Generate the (2^k)xN matrix Y with columns not in I all zero and the other columns with all choices of an entry from the first or second row of X. For example, with X <- matrix(1:8, nrow=2) I <- c(1,3) X is 1 3 5 7 2 4 6 8 and Y should be 1 0 5

1. I'm attempting to test for Random Effects. I've grouped the data on subject (grid) but want to use lme to build the model without subject as a RE then add it and do anova between the 2 models. This is the result I get and it appears it's adding Random Effects. tmp.dat4 <- groupedData(Trials ~ 1 | grid, data = tmp.dat4) mod2a <- lme(Trials ~ factor(group_id) + reversal,

I'm fitting a lme growth curve model with two predictors and their interaction as predictors. The multilevel model is nested so that level 1 is time within the individual, and level 2 is the individual. I would like to plot the mean group-level trajectories at plus and minus 1 SD from the mean of the main effects composing the interaction term. Thus, the plot should have 4 lines (mean

Newbies (and others!) may find useful the R Reference Card made available by Tom Short and Rpad at http://www.rpad.org/Rpad/Rpad-refcard.pdf or through the "Contributed" link on CRAN (where some other reference cards are also linked). It categorizes and organizes a bunch of R's basic, most used functions so that they can be easily found. For example, paste() is under the