similar to: mean as a condition of an effect?

I am trying to understand better an analysis mean RT in various conditions in a within subjects design with the overall mean RT / subject as one of the factors. LME seems to be the right way to do this. using something like m<- lme(rt~ a *b *subjectRT, random= ~1|subject) and then anova(m,type = "marginal"). My understanding is that lme is an easy interface for dummy coding

what does this mean in R-1.8.1 release notes? o median() no longer `works' for odd-length factor variables.

Hi folks, I am working with repeated measures data and I ran into issues where the paired t-test results did not match those obtained by employing glht() contrasts on a lme model. While the lme model itself appears to be fine, there seems to be some discrepancy with using glht() on the lme model (unless I am missing something here). I was wondering if someone could help identify the issue. On

Full_Name: Robert King Version: 1.6.2 OS: linux Submission from: (NULL) (134.148.20.33) In optim, non-zero values of trace in the control list are treated as postitive, even if they are negative. >From documentation: trace Integer. If positive, tracing information on the progress of the optimization is produced. Higher values may produce more tracing information: for method

Hello, I have some conflicting output from an aov summary and tukey contrasts with a mixed effects model I was hoping someone could clarify. I am comparing the abundance of a species across three willow stand types. Since I have 2 or 3 sites within a habitat I have included site as a random effect in the lme model. My confusion is that the F test given by aov(model) indicates there is no

Hello, I have used R in the past to conduct multiple comparisons on standard linear models, but am a bit confused as to how to go about doing it with a mixed effects model. I am conducting a bioindication study using carabid beetles in which I have four treatment types (forest harvest types with varying levels of canopy structure retention), and am using canopy closure percent as a covariate in

Dear all, I studied in tank prey fish behavior. Using the design described below (and R code), I want to test the effects of both habitat and predator (and interaction) on prey fish's vertical distribution, which was recorded (with repeated measures) as a categorical variable. I found that package mlogit might fit to my need but I don't know how to specify my complex design in the

Dear Brian, As Duncan mentioned, the terms type-I, II, and III sums of squares originated in SAS. The type-II and III SSs computed by the Anova() function in the car package take a different computational approach than in SAS, but in almost all cases produce the same results. (I slightly regret using the "type-*" terminology for car::Anova() because of the lack of exact

I'm a programmer in a biology lab who is starting to use R to automate some of our statistical analysis of growth rate determination. But I'm running into some problems as I re-code. 1) Hypotheses concerning Slope similarity/difference: I'm using R's anova(lm()) methods to analyse a model which looks like this: growth.metric ~ time * test.tube I understand that

Dear useRs, `multcomp' version 0.991-1 will be shortly available from CRAN near you. Nearly all functionality contained in the package has been re-implemented from scratch. The focus of the package has been extended to general linear hypotheses in arbitrary parametric models and the most important function to check out is `glht()'. Multiple comparison of means procedures (for example

Dear useRs, `multcomp' version 0.991-1 will be shortly available from CRAN near you. Nearly all functionality contained in the package has been re-implemented from scratch. The focus of the package has been extended to general linear hypotheses in arbitrary parametric models and the most important function to check out is `glht()'. Multiple comparison of means procedures (for example

Hi, I am experimenting with using glht() from multcomp package together with coxph(), and glad to find that glht() can work on coph object, for example: > (fit<-coxph(Surv(stop, status>0)~treatment,bladder1)) coxph(formula = Surv(stop, status > 0) ~ treatment, data = bladder1) coef exp(coef) se(coef) z p treatmentpyridoxine -0.063 0.939 0.161

Dear all, For analysis of a longitudinal data set with fixed measurement in time I built a gls model (nlme). For testing hypotheses in this model I used the linear.hypothesis function from the car package. A check with the results obtained in SAS proc MIXED with a repeated statement revealed an inconsistency in the results. The problem can be that the linear.hypothesis function (1) only gives the

Dear R-help members, I'm trying to run LME model on some behavioral data and need confirmations about what I'm doing... Here's the story... I have some behavioral reaction time (RT) data (participants have to detect dome kind of auditory stimuli). the dependant variable is RT measured in milliseconds. 61 participants were tested separated in 4 age groups (unblanced groups,

Hi all, I am fitting a linear mixed model with lme4 in R. The model has a single factor (des_days) with 4 levels (-1,1,14,48), and I am using random intercept and slopes. Fixed effects: data ~ des_days Value Std.Error DF t-value p-value (Intercept) 0.8274313 0.007937938 962 104.23757 0.0000 des_days1 -0.0026322 0.007443294 962 -0.35363 0.7237 des_days14 -0.0011319

Hello all! I would like to start off by saying that I am still really new to the vast world of R so please excuse my very limited vocabulary in the program. I have collected data from monkey videos and would like to setup some model(s) in R that can help with my hypotheses. I am having trouble figuring out which statistical tests/models to use for my two hypotheses. #1: Comparing the presence

Hello! Just would like to make sure I am not doing something wrong. I am running an OLS regression. I have several subgroups in the data set (locations) - and in each location I have weekly data for 2 years - on my DV and on all predictors. Looks like this: location week DV Predictor1 Predictor 2 location1 week1 xxx xxxxxxx xxxxxxxxx location1 week2 xxx xxxxxxx xxxxxxxxx . .

[Env: Tested under Win Xp, R 2.9.2 and R 2.10.1; sessionInfo() at end] I've run into a problem with the heplot3d() function in my heplots package which causes the R GUI to crash ('R for Windows GUI encountered a problem and needs to close...'). I think the problem comes from an rgl call, but, I can't get a traceback or other information because my R session crashes. I've

Dear R-list members, I've been struggling with the proper setup for analysing my data. I've performed a route choice experiment, in which participants had to make a choice at each junction for the next road. During the experiment they received traffic information, but also encountered two different accidents. They also made trips without accidents. What I'm interested in is to

Hi there, as part of a 2 x 2 contingency table analysis I would like to estimate conditional probabilities (success rates) in a Bernoulli experiment. In particular I want to test a null hypothesis p <= p0 versus the alternative hypothesis p > p0. As far as I understand the subject, there are UMPU tests for these types of hypotheses. Now I know about R's "binom.test" but the