similar to: lme() direction

In a lattice 11x11 with 6 repetitions, we want to compare lines to their two parents. 3 of the 6 repetitions are sprinkled and the 3 others not. There are 5 factors : hm (hydrous mode), variety, block, rep and grandrep. grandrep gathers two repetition, a sprinkled and a not (as in Split Plot !). I use lme but I have problems in random. Can someone help me ? Ibnou DIENG

Dear Douglas and R-help, Does lme assume normal distribution AND equal variance among groups like anova() does? If it does, is there any method like unequal variance T-test (Welch T) in lme when each group has unequal variance in my data? Thanks, Shirley

How does one implement a likelihood-ratio test, to test whether the variances of the random effects differ between two groups of subjects? Suppose your data consist of repeated measures on subjects belonging to two groups, say boys and girls, and you are fitting a linear mixed-effects model for the response as a function of time. The within-subject errors (residuals) have the same variance in

Dear R Experts, I am attempting to run a mixed effects model on a within-subjects repeated measures design, but I am unsure if I am doing it properly. I was hoping that someone would be able to offer some guidance. There are 5 independent variables (subject, condition, difficulty, repetition) and 1 dependent measure (value). Condition and difficulty are fixed effects and have 3 levels each

Dear all this is a question of model specification in lme which I'd for which I'd greatly appreciate some guidance. Suppose I have data in long format gene treatment rep Y 1 1 1 4.32 1 1 2 4.67 1 1 3 5.09 . . . . . . . . . . . . 1 4 1 3.67 1 4 2 4.64 1 4 3 4.87 .

Hi all, I have data from an experiment with 3 independent variables, 2 are within and 1 is between. In addition to the dependent variable, I have a covariate that is a single measure per subject. Below I provide an example generated data set and my approach to implementing the ANCOVA. However the output confuses me; why does the covariate only appear in the first strata? Presumably it should

Dear ExpeRts, Suppose I have a typical psychological experiment that is a within-subjects design with multiple crossed variables and a continuous response variable. Subjects are considered a random effect. So I could model > aov1 <- aov(resp~fact1*fact2+Error(subj/(fact1*fact2)) However, this only holds for orthogonal designs with equal numbers of observation and no missing values.

In Baron and Li's "Notes on the use of R for psychology experiments and questionnaires" http://cran.r-project.org/doc/contrib/rpsych.htm they describe a balanced data set for a drug experiment: "... a test of drug treatment effect by one between-subject factor: group (two groups of 8 subjects each) and two within-subject factors: drug (2 levels) and dose (3 levels). "

Hello all, I am trying to use lme to examine how a response variable (Chla) changes over time in different treatments (2 Temp & 2 Light levels). Within each treatment combination, there are two replicate tanks (each with unique TankID) with coral fragments in them. All tanks are subject to the same environment until Time=0, when treatments are imposed, and Chla is measured for each

Dear All, I have a complex survey data with observations having differing probabilities of selection into the sample. I would like to run a linear mixed effects model and also to use weighting that takes this into consideration. As far as I know, however, the weighting option for the lme command from nlme package (or glmmPQL from MASS, for that matter) is related to heteroscedasticity. Is there a

Hi, I wonder if anyone can help me with specifying a right model for my analysis. I am a beginner to lme methods and though have spent already many hours studying from various books an on-line helps, I was unfortunately not able to find a solution to my problem on my own. Data structure: I studied escape behavior of three species of a prey to a predator. The prey specimens (many) were in a

I'am trying to develop some code if R, which would correspond to what I did in SAS. The data look like: Treatment Replicate group1 GSI Control A 1 0.81301 Control B 1 1.06061 Control C 1 1.26350 Control D 1 0.93284 Low A 2 0.79359 Low B

Le 25/08/2019 ? 23:07, Stephen John Smoogen a ?crit?: > I have had several people explain to me that my comments were > out-of-place and out-of-turn. When I wrote them originally I thought I > was going from experience of previous releases and thought I was being > matter of fact. In rereading I realized I was crass and grumpy. My > un-reserved apologies for that. I should have

On my machine (Mac OS 10.5.6, R 2.8.1) the following plot is drawn such that a substantial portion of the descender of the title is covered by the plot: library(ggplot2) qplot(x=1:10,y=1:10,main='p q j g') -- Mike Lawrence Graduate Student Department of Psychology Dalhousie University www.thatmike.com Looking to arrange a meeting? Do so at: http://www.timetomeet.info/with/mike/ ~

Hi Everyone, I am trying to analyze a split plot experiment in the field that was arranged like this: I am trying to measure the fitness consequences of seed size. Factors (X): *Seed size*: a continuous variable, normally distributed. *Water*: Categorical Levels- wet and dry. *Density*: Categorical Levels- high, medium and solo *Plot*: Counts from 1 to 20 The *response variable *(Y) was the

Hello - I am fairly new to R, (i.e., ability to create functions/write programs insignificant) and was wondering if there might be a convenient way to model the following: I want to fit a gaussian glm to grouped data, while allowing for unequal variances in each of the groups. More specifically, my data set looks something like this: ---------------- data group 1 76 1 2 82 1 3

I'm attempting to analyze some survey data comparing multiple docks. I surveyed all of the slips within each dock, but as slips are nested within docks, getting multiple samples per slip, and don't really represent any meaningful gradient, slip is a random effect. There are also an unequal number of slips at each dock. I'm having syntactical issues, however. When I try

Hi all, I've noticed that many computational neuroscience research groups use MATLAB. While it's possible that MATLAB may have some features unavailable in R, I suspect that this may instead simply be a case of costly tradition, where researchers were taught MATLAB as students and pay for it as researchers because it's all they know. I'd like to attempt to break the cycle by

Any help here? I have included all of the output of the suggested diags that Thomas said I should run, but I admit that I'm not sure what I'm looking for, as I'm not familiar with RPC functionality on Linux. Something is not working with RPC on my Samba 4.0.3 server. (FWIW, it doesn't work with IPTables stopped either.) > On Mon, Feb 25, 2013 at 2:21 PM, Mike Stroven wrote:

Dear R-list, I have a statistical problem with the comparison of two short time-series of density data in an ecological framework. I have to compare two short time series (5 years, one value for each year) of species density data (it is the density of fish in two different streams) to test if the two means of the five densities are significantly different, so basically if the two mean