similar to: : Repeated measurements data

Doug, I thought perhaps that you might be interested in the comparison of lme to the results for the same models fitted by Richard Jones' carma (I just wrote the R interface to his Fortran code). The code to run the example from the lme help and for the equivalent with carma is in the file below. The two main differences in results are 1. the random coefficients covariance matrix is quite

Dear all, I am sorry , I have not enough English or I am fool to understand the helping system 1- if someone has written the code for Hannan-Rissanen model selection in ARMA models, how can I find this R module or the code ? 2- I wrote the algorithm and the code for the Levy driven CARMA and COGARCH models, could anybody check or verify that I have done? Best regards yours sincerely

Hi list I am just beginning to understand the complexities of linear mixed effects models. Maybe someone can give advise concerning the following problem: I have two groups of surgical patients in which repeated laboratory measurements were taken over time after surgery. I decided that lme would be the best model to fit the data. I already fitted the model lme(logratio ~ gr*I(pod-10) +

Dear all, Does anybody know how to get more evaluation points in performing Nonparametric analysis of repeated measurements data with "sm" library. The following command gives the estimation for 50 points, by I would like to increase to 100 points But I do not know how to do that. library(sm) provide.data(citrate, options=list(describe=FALSE)) provide.data(dogs,

Hello, I am using the package gamlss in R to plot repeated measurements. The command I am using is par.plot(). It works great except one thing about the label of the axises. I tried to label both x and y axises using ylab and xlab options. But the plot only gives variable variables. The labels did not show up. Below is the code I used. Any comments are appreciated! Thanks. library(gamlss)

Analysis of Repeated Measurements By Dr. Ronald Geskus April 27, 2007 Amsterdam, The Netherlands http://www.can.nl/events/details.php?id=31 This course is aimed at everyone who is working with data that contain repeated measurements on persons or otherwise related data and who wants to analyse such data in a proper way. In many situations one may have data that show some

Dear R Users, I am using the LLTM and the LRSM functions in the eRm package to do repeated measurements where there are 2 measurement points for a list of 10 items. I am trying to get ability estimates but am having trouble. I don't think that it is appropriate to use the pmat function since the person parameters are based on all 20 items. Rather, I think it would be more appropriate to

Hello, I want to perform a permanova where the first factor called Treatment has four levels. The second factor involves sampling the same research plots for four consecutive years, hence the repeated measurements. I have been able to use the adonis function from the package vegan to run this analysis. code below: TC.perMANOVA.adonis<-adonis(TC.PerMANOVA ~ Treatment*Year,

Dear R-users, I have some human serum lipid concentrations (cholesterol, apoB ...), each lipid was measured (in the same person) for 6 times in different time points (start, 3 months, 6 months, 12 months, 18 months, 24 months). There were 2 groups of participants: one with a nutritional intervention and the other without it. I would like to know, whether lipid concentrations differ among different

Hello, I have a question regarding performing manova. I have an experiment where I want to measure 10 output variables for 3 different measurement methods. Since each of the methods requires some user interaction, I would also like to include repeated measures for each of the output variables to include intraobserver variability in the design. How can I perform such a repeated-measures

I am trying to do a repeated measurement anova using an lme. I have the following variables: -ID, the identification of the individual -trail, with 2 levels -treatment, with 3 -time, measure 5 times the same individual -VCL, the response variable I tried the following in R, within.gr<-groupedData(VCL~time|ID/treatment/time,data=within)

I am using lme to handle repeated measurements. So far i can follow the examples from the book from pinheiro and bates. Now i get the problem , that i have "nested" repeated measuremnts, and i cant find out how to do the grouping part of the lme formula. 1.The simple problem ist that i have different Samples , from which i make repeated measurements (each sample is measured 6 times) and

Dear list, i am setting up a GLM for a repeated measurement ANOVA using the lm and ANOVA function. my design contains four factors with 5, 5, 2 and 2 (= 14) levels, respectively. the data are stored in a data.frame with six columns, one for the data themselves and the remainings for the factors where strings indicate the factor levels in each row. now i would like to restructure this

Dear all, I'm working on a design with rotating panels. Each site is sampled every X year. Have a look at the code below the get an idea of the design. In reality the number of sites will be (much) higher. However I'm not sure if I coded the design correctly in svydesign(). Can someone confirm that it is either correct or wrong? I've added the lmer() equivalent that I want to

Dear, I want to analyze an outcome in an RCT using lme but I am not sure that I have chosen the right way for the model. We measured the outcome three times repeatedly in the same patient. One time before intervention and two times after intervention. I wanted to adjust for the correlated data in the repeated measurement and baseline differences in the variable in order to get the treatment

Thank you so much all for your invaluable inputs. On Sat, Jun 4, 2011 at 3:36 AM, Patrick Burns <patrick at burns-stat.com> wrote: > A common thing to do is the Ljung-Box > test on the residuals. ?For garch it > would be the residuals squared. > > Actually for garch it should be the > rank of the squared residuals -- see >

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

I have the following problem: I measured co2 on 6 paired sites (one grubbed and one non-grubbed fence per site -> grubbing = treatment). These measurements I repeated 15 times over 2 years. So, now my problem is how to analyze these data. I tried the following model: mod1_CO2<-lme(co2~treatment+time,random=~1|site,data=CO2_t1_t15) I think with the random effect I included the paired

Dear all, I have simple question regarding how to fit a model (i.e. linear) to the data. Say I have 10 subjects with different phenotypes (dependent var Y, identical for a particular subject) and one predictor variable measured 3 times for each subject (X). By other words: Y Subj X 1 1 1.2 1 1 1.3 1 1 0.7 3 2 2.1 3 2 2.5 3 2 4 5 3 3 5 3 4 5 3 4 ... 20 10 12 20 10 13 20 10 12.5 Subj is a

Dear R-helpers: My question is how do I efficient and valid correct for multiple tests in a repeated measurement design: Suppose we measure at two distinct visits with repeated subjects a treatment difference on the same variable. The treatment differences are assessed with a mixed model and adjusted by two methods for multiple tests: # 1. Method: Adjustment with library(multcomp)