similar to: Repeated measures using lme

Dear R-user: After the fitting the Tobit model using zelig, if I use the following command then I can get the regression coefficents: beta=coefficients(il6.out) > beta (Intercept) apache 4.7826 0.9655 How may I extract the "Naive SE" from the following output please? > summary(il6w.out) Call: zelig(formula = il6.data$il6 ~ il6.data$apache, model =

Dear R-gurus, I am pretty much new on R. I am trying to to do a repeated analysis of a linear mixed model with R, and I consistently fail... The problem is: Cow is the random factor, treatment is the fixed factor. The dependent variable is milk yield, which is measured several times (repeatedly over time), thus there is another variable which is time (i.e. week). The model would be

This produced quite a mouthful of text. I''m stuck on the error messages and wondering if I messed something up. Any advice would be very appreciated. Thank you. dust:~ Lex$ gem install rails WARNING: Installing to ~/.gem since /Library/Ruby/Gems/1.8 and /usr/bin aren''t both writable. WARNING: You don''t have /Users/Lex/.gem/ruby/1.8/bin in your PATH, gem executables

Dear readers, I have a basic question about how to use lme for my design. I haven't been able to find an example in r-help that made it clear to me how to tackle this problem and unfortunately I also cannot get hold of Pinheiro & Bates 2000. I hope someone can help. Data for the response variable "foan" were collected in: · 60 plots · plots were re-sampled yearly

Hello, I am trying to fit a repeated measures linear mixed model (using lme) but I don't want to include any random effects. I'm having trouble (even after consulting Pinheiro & Bates 2000) figuring out how to specify the repeated measure without including it in the specification of a random effect. My data consist of repeated "counts" in "plots" that I wish

I haven't been able to find an answer on this that's direct, only implied. In several places I have read that when people asked for sphericity tests they were guided toward lme or mlm models. But, there is no direct indication that the lme method is not subject to the sphericity assumption. In fact, it seems like it should be. Its just a linear model that handles random and

Hello, We are attempting to use nlme to fit a linear mixed model to explain bird abundance as a function of habitat: lme(abundance~habitat-1,data=data,method="ML",random=~1|sampleunit) The data consist of repeated counts of birds in sample units across multiple years, and we have two questions: 1) Is it necessary (and, if so, how) to specify the repeated measure (years)? As written,

Dear R-Users, I'm trying to set up a repeated measures anova with two within subjects factors. I tried it by 3 different anova functions: aov, Anova (from car package) and lme (from nlme package). I managed to get the same results with aov and Anova, but the results that I get from lme are slightly different and I don't figure out why. I guess I did not set up the error structure

hello list, the topic is covered extensively but from none of the postings i could conclude the correct statement for my design: a 2-level within and a 2-level between subjects factor, both fixed, subjects as random factor. i want to test wheter there is a within effect and if it is different for levels of the between-factor. i want to do it with lme and multicomp for post-hocs. i tried am2

Hi Everyone, I’m trying to figure out how to get R to analyze this experiment properly. I have a series of subjects each with two legs. Within each leg there are two bones that I am interested in. There are also two treatments that I am interested in. That results in four different combinations of treatments. Obviously, since the subjects only have two legs, they can’t receive each

Dear all, I'm not very sure on the use of repeated measures in R, so some advice would be very appreciate. Here is a simple example similar to my real problem (R 2.6.0 for windows): Lets supose I have annual tree production measured in 9 trees during 3 years; the 9 trees are located in 3 different mountains (sites), and each tree receive different annual rainfall (different locations). I would

Dear R Friends, I need help with an error associated with corCompSymm in an lme fit. I am using a mixed effects model to analyze a split-plot with repeated measures and would like to fit with the compound symmetry correlation structure. This problem doesn't occur when using corAR1 or any of the other structures. I would greatly appreciate help on how to solve this issue. Here's my

I am trying to do Tukey HSD comparisons on a repeated measures expt. I found the following example on r-help and quoted approvingly elsewhere. It is broken. Can anyone please tell me how to get it to work? I am using R 2.4.1. > require(MASS) ## for oats data set > require(nlme) ## for lme() > require(multcomp) ## for multiple comparison stuff > Aov.mod <- aov(Y ~ N + V +

Hi, what is the appropriate syntax to get the random error correct when performing repeated measures anova with 'lme'. let's say i have 3 independent variables, with 'aov', i would write something like: aov(dep_var~(indep_var1*indep_var2*indep_var3) + Error(subject/(indep_var1*indep_var2*indep_var3)). With 'lme' however, i can't find the right formula. i tried

JtestR allows you to test your Java code with Ruby frameworks. Homepage: http://jtestr.codehaus.org Download: http://dist.codehaus.org/jtestr JtestR 0.1 is the first public release of the JtestR testing tool. JtestR integrates JRuby with several Ruby frameworks to allow painless testing of Java code, using RSpec, Test/Unit, dust and Mocha. Features: - Integrates with Ant and Maven - Includes

Hello I am trying to fit a REML to some soil mineral data which has been collected over the time period 1999 - 2004. I want to know if the 19 different treatments imposed, differ in terms of their soil mineral content. A tree model of the data has shown differences between the treatments can be attributed to the Magnesium, Potassium and organic matter content of the soil, with Magnesium being the

I am a new user of R. I am trying to fit gam model with our air pollution data. I used Foreign package to call data from SPSS and used MGCV package to fit gam. The following are the steps I used: > dust<- read.spss("a:dust9600jan.sav") > c<-gam(MRESPALL~s(DUSTM)+s(TEMP)+s(RH),family=poisson,data=dust) > summary(c) Family: poisson Link function: log Formula: MRESPALL ~

Hello, I have a simulated data structure in which students are nested within teachers, and with each student are associated two test scores. There are 20 classrooms and 25 students per classroom, for a total of 500 students and two scores per student. Here are the first 10 lines of my dataframe "d": studid tchid Y time 1 1 1 -1.0833222 0 2 1 1

Hi As I can't find an example of my data structure I'd like some advice on which is the most appropriate test for significant effects. If I should be using either lme or anova, is the relevant example below the best/correct way to do the test? The Data... 2 groups of patients (5 in GroupA, 7 in GroupB) 3 short acting drugs, (I'm not concerned with residual effects from the previous

how can i put the row labels on the left hand of the heatmap in heatmap.2? abnr<-structure(c(1, 0.678622097406395, 0.670294749652918, -0.0016314464654279, -0.000519068106572792, 0.199581999119988, -0.0106623494189115, 0.0840111691399559, -0.0461494399639137, 0.249279171677728, NA, 1, 0.757114062773504, 0.0352642759270137, -0.0255518450373996, 0.0943268190664674, -0.0536269679247722,