similar to: nlme & lme for complex mixed ANOVAs

Dear list, Has anyone created a version of SSmicmen that allows testing for group differences? The basic Michaelis-Menten equation is: (Bmax * X) / (Kd + X). The nlme package allows modeling of random effects for Bmax and Kd as needed, but I curious how I can build in group differences? I have receptor binding data for strains of mice, and following Pinheiro and Bates' lead in their

I have assayed the concentrations of various metal elements in different anatomic regions of two strains of mice. Now, for each element, in each region, I want to do a t test to find whether there is any difference between the two strains. Here is what I did (using simulated data as an example): # create the data frame > elemconc = data.frame(expand.grid(id=1:3, geno=c('exp',

dfhfsdhf at ghghgr.com I ran a simple lme model: modelrandom=lmer(y~ (1|Test) + (1|strain), data=tempsub) Extracted the BLUPs: blups=ranef(modelrandom)[1] Even wrote myself a nice .csv file....: write.csv(ranef(modelrandom)[1],paste(x,"BLUPs.CSV")) This all works great. I end up with a .csv file with the names of my strains in the first column and the BLUP in the second

I've scoured the archives but have found no concrete answer to my question. Problem: Two data sets 1st data set(x) = 20,000 rows 2nd data set(y) = 5,000 rows Both have the same column names, the column of interest to me is a variable called strain. For example, a strain named "Chab1405" appears in x 150 times and in y 25 times... strain "Chab1999" only appears 200

Dear R user, I have a question on using R to analyze data with repeated measurements. I have 2 species with several strains (12) per species, each of which has been measured twice with for a given trait. No particular covariance, just two measures. Now I want to analyze the data with an ANOVA (aov) considering these repeated measures to get the MSq and SSq for the species and strain level. I

Dear all, I have 3 models (from simple to complex) and I want to compare them in order to see if they fit equally well or not. From the R prompt I am not able to see where I can get this information. Let´s do an example: fit1<- lm(response ~ stimulus + condition + stimulus:condition, data=scrd) #EQUIVALE A lm(response ~ stimulus*condition, data=scrd) fit2<- lm(response ~ stimulus +

Dear All, I have a problem in understanding how the interactions of 2 ways ANOVA work, because I get conflicting results from a t-test and an anova. For most of you my problem is very simple I am sure. I need an help with an example, looking at one table I am analyzing. The table is in attachment and can be imported in R by means of this command: scrd<-

Dear all, I have got two vectors coding for a stimulus presented in the current trial (mydat$Stimulus) and a prediction in the same trial (mydat$Prediciton), respectively. By applying an if-conditional I want to create a new vector that indicates if there is a match between both vectors in the same trial. That is, if the prediction equals the stimulus. When I pick out some trials randomly, I get

Dear List, This is my first post, and I'm a relatively new R user trying to work out a mixed effects model using lme() with random effects, and a correlation structure, and have looked over the archives, & R help on lme, corClasses, & etc extensively for clues. My programming experience is minimal (1 semester of C). My mentor, who has much more programming experience, but a comparable

Dear list, Following up on an earlier post, I would like to reorder a dataset and compute pairwise correlations. But I'm having some real problems getting this done. My data looks something like: Participant Stimulus Measurement p1 s`1 5 p1 s`2 6.1 p1 s`3 7 p2 s`1 4.8 p2

Hi, I am analyzing a repeated-measures Greco-Latin Square with the aov command. I am using aov to calculate the MSs and then picking by hand the appropriate neumerator and denominator terms for the F tests. The data are the following: responseFinger mapping.code Subject.n index middle ring little ---------------------------------------------------------------------------- 1 1

Hi there, I have some simple frequencies I want to plot into one graph. I had it working, and now I can't figure out whats going wrong. All the data is stored in a dataframe, and i finally managed to order the factor correctly! Each column is a variable and contains integers for the same set of values in the column that contains the headers for each row (graphLabels). So, I get the data

Dear all, I need an help because I am really not able to find over internet a good example in R to analyze an unbalanced table with Anova with repeated measures. For unbalanced table I mean that the questions are not answered all by the same number of subjects. For a balanced case I would use the command aov1 = aov(response ~ stimulus*condition + Error(subject/(stimulus*condition)), data=scrd)

Dear all, I need to understand a thing in the beheaviour of the two functions aov and anova in the following case involving an analysis of ANOVA with repeated measures: If I use the folowing command I don´t get any problem: >aov1 = aov(response ~ stimulus*condition + Error(subject/(stimulus*condition)), >data=scrd) > summary(aov1) Instead if I try to fit the same model for the

Package bqtl version 1.0 is now available on CRAN. Description: QTL mapping toolkit for inbred crosses and recombinant inbred lines. Includes maximum likelihood and Bayesian tools. I am keen to have comments about this package including implementation details, additional functionality, and (of course) problems and bugs. Chuck Berry -- Charles C. Berry (858) 534-2098

Dear all, how can I perform a post hoc analysis for ANOVA with repeated measures (in presence of a balanced design)? I am not able to find a good example over internet in R...is there among you someone so kind to give me an hint with a R example please? For example, the aov result of my analysis says that there is a statistical difference between stimuli (there are 7 different stimuli). ...I

Hello, I am trying to test some results I have for significance. It has been recommended that I use R and I am completely new to this. Set-up: Groups: two groups of 8 subjects (16 total) Two conditions: alert and passive Measurements: responses for three different stimuli (A, B, and C) measured in each condition Experiment: Testing the order of conditions Group one: Alert A, B

Dear R-Experts, I was wondering how to fit a cumulative gaussian to a set of empirical data using R. On the R website as well as in the mail archives, I found a lot of help on how to fit a normal density function to empirical data, but unfortunately no advice on how to obtain reasonable estimates of m and sd for a gaussian ogive function. Specifically, I have data from a psychometric function

To estimate saturation binding parameters Bmax and Kd in a receptor saturation binding experiment, I use the following nonlinear equation and the nls() function: bmax*X*dummy ------------ + ns*X + background = total binding kd+X where X is concentration, and dummy is an indicator to allow shared estimation of the nonspecific binding parameter ns. This equation describes two fitted

hello everyone, I would like to as for advice for the use of ?lmer? (package ?lme4?) and writing the proper syntax to best describe my data using a mixed-effects model. I have just started to use these models, and although I have read some good examples (Extending the Linear Model with R, Faraway 2005; and the R book, Crawley 2007), I am still not sure of the syntax to test my hypothesis.