similar to: help with 'lm' function: contrast and separate variance terms

Hello All, I have posted to this list before regarding the same issue so I apologize for the multiple e-mails. I am still struggling with this issue so I thought I'd give it another try. This time I have included reproducible code and a subset of the data I am analyzing. I am running an ANOVA with three factors: GROUP (5 levels), FEATURE (2 levels), and PATIENT (2 levels), where

Dear All, I'm trying to use the ols function in the Design library (version 2.1.1) of R to estimate parameters of a linear model, and then use the contrast function in the same library to test various contrasts. As a simple example, suppose I have three factors: feature (3 levels), group (2 levels), and patient (3 levels). Patient is coded as a non-unique identifier and is

I am using RW 1.2.3. on an IBM PC 300GL. Using the data bp.dat which accompanies Helen Brown and Robin Prescott 1999 Applied Mixed Models in Medicine. Statistics in Practice. John Wiley & Sons, Inc., New York, NY, USA which is also found at www.med.ed.ac.uk/phs/mixed. The data file was opened and initialized with > dat <- read.table("bp.dat") >

I'm working with a nested model (mixed). I have four factors: Patients, Tissue, sex, and tissue_stage. Totally I have 10 patients, for each patient, there are 2 tissues (Cancer vs. Normal). I think Tissue and sex are fixed. Patient is nested in sex,Tissue is nested in patient, and tissue_stage is nested in Tissue. I tried aov and lme as the following, > aov(gene ~ tissue + gender +

Dear lme and lmer -ers, I have some problems using "home-made" contrast matrix in lme and lmer. I did an experiment to investigate the relationship between the response of an animal and some factors, namely the light wavelength (WA), the light intensity to which this animal was exposed and the sex of the animal tested. - The response can be a variable LA (normal distribution) or

Hi, I am quite new to R and trying to analyze the following data. I have 28 controls and 25 patients. I measured X values of 4 different locations (A,B,C,D) in the brain image of each subject. And X ranges from 0 to 1. I think "control or patient" is a between subject factor and location is a within subject factor. So, controls: 28 patients: 25 (unbalanced data set) respone measure:

Hello, I have been asked to help perform a meta-analysis with individual- and aggregate-level data. I found a nice article on this, and the idea is easy to understand: use a mixed effects models, but for the studies where there is only aggregate level data, force the variance to be that which was observed. Unfortunately, I am struggling to see how to implement this in R. I am familiar with

Hi, I have what I believe is a repeated-measures dataset that I'm trying to analyze using lme(). This is *not* homework, but an exercise in my trying to self-teach myself repeated-measure ANOVA for other *real* datasets that I have and that are extremely similar to the following design. I'm fairly sure the dataset described below would work with lme() -- but it'd be great if anybody

Dear list, I am confused on how to explain the interaction term in the context of "treatment contrast". for example, I have an data frame as below: sub group val 1 a group1 3.685625 2 a group1 3.407445 3 a group1 4.040920 4 a group1 2.890875 5 b group1 3.853280 6 b group1 4.113585 7 b group1 3.043250 8 b group1 3.800920 9 c group1 5.394305 10 c

I am using lme from the nlme package to fit a mixed model. We have observations nested in patients(encounters) and patients nested in groups (2 different treatments). We are interested in the differences between the 2 groups, both the means and the standard deviations (are patients in group A less variable than those in group B? both within patient and between patient within group). Here is

Hi I have been asked by a colleague to perform a statistical analysis which uses random effects - but I am struggling to get this to work with nlme in R. Help would be very much appreciated! Essentially, the data consists of: 10 patients. Each patient has been given three different treatments (on three separate days). 15 measurements (continuous variable) have been taken from each patient

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

Hi David: In looking at your original post it is a bit difficult to ascertain exactly what your null hypothesis was. That is, you want to assess whether there is a treatment effect at time 3, but compared to what. I think your second post clears this up. You should refer to pages 224- 225 of Pinhiero and Bates for your answer. This shows how to specify contrasts. > -----Original Message-----

Hi dear R community, I have up to 12 measures of a protein for each of 6 patients, taken every two or three days. The pattern of the protein looks periodic, but the height of the peaks is highly variable. It's something like this: patient <- data.frame( day = c(1, 3, 5, 8, 10, 12, 15, 17, 19, 22, 24, 26), protein = c(5, 3, 10, 7, 2, 8, 25, 12, 7, 20, 10, 5) ) plot(patient$day,

Dear R-users, I want to fit a linear model with Y as response variable and X a categorical variable (with 4 categories), with the aim of comparing the basal category of X (category=1) with category 4. Unfortunately, there is another categorical variable with 2 categories which interact with x and I have to include it, so my model is s "reg3: Y=x*x3". Using fit.contrast to make the

All, The code below is for a pseudo dataset of repeated measures on patients where there is also a treatment factor called "drug". Time is treated as categorical. What code is necessary to test for a treatment effect at a single time point, e.g., time = 3? Does the answer matter if the design is a crossover design, i.e, each patient received drug and placebo? Finally, what would

Dear R community, I have trouble implementing a nested variance-covariance matrix in the lme function. The model has two fixed effects called End and logpgc, the response variable is the logarithm to base 2 of Intensity ( log2(Intensity) ) and the random effects are called Probe and ProbeNo. The model has the following nesting structure: A Pixel is nested within the ProbeNo,the ProbeNo is

All, How does one extract the level-1 variance from a model fit via lmer()? In the code below the level-2 variance component may be obtained via subscripting, but what about the level-1 variance, viz., the 3.215072 term? (actually this term squared) Didn't see anything in the archives on this. Cheers, David > fm <- lmer( dv ~ time.num*drug + (1 | Patient.new), data=dat.new )

Hello, I have a 2 factor linear model, in which the only terms I am interested in estimating and testing are the interaction terms. I want to control for the main effects but have no interest in estimating or testing them. However, I would like an estimate of the interaction effects for every level of the interactions, whereas what I get is one fewer estimate than this, with the first level

Dear All, I am a bit struggling with the many packages for Markov models available in R. Apologies for now posting a code snippet, but I am looking for some guidance here. Please consider a set like the one below (which you can get with data<-read.csv('http://dl.dropboxusercontent.com/u/5685598/data_table.csv') ). ID therapy age1 age2 EFS 7308 ormo_lunga 78