similar to: Variance components from lm?

Could someone identify a function that I might use to perform a components of variance analysis? In addition to the variance attributable to each factor, I would also like to obtain the SE of the variances. Thank you, John John Sorkin M.D., Ph.D. Chief, Biostatistics and Informatics Baltimore VA Medical Center GRECC and University of Maryland School of Medicine Claude Pepper OAIC University of

Hi, The question is as follows: has anyone coded panel data unit root tests with R? Even the "first generation" tests (see e.g. Levin & Lin 1993; Pesaran, & Smith & Im 1996; Maddala & Wu 1999) would be sufficient for my needs. To my understanding, these are rather easy to code, but as I have taken just my first steps in coding with R, existing code would save me

Hi, I'm trying to fit a model using variance components in R, but if very new on it, so I'm asking for your help. I have imported the SPSS database onto R, but I don't know how to convert the commands... the SPSS commands I'm trying to convert are: VARCOMP RATING BY CHAIN SECTOR RESP ASPECT ITEM /RANDOM = CHAIN SECTOR RESP ASPECT ITEM /METHOD = MINQUE (1) /DESIGN

Hey, I am estiamting a partially linear model y=X\beta+f(\theta) where the f(\theta) is estiamted using wavelets. Has anyone heard of methods to test if the betas are significant or to address model fit? Thanks for any thoughts or comments. Elizabeth Lawson __________________________________________________ [[alternative HTML version deleted]]

Dear all, I'm interested in analysing a reapeated measure desing where plant height (H) was measured 3 times (Time). The experimental design include 2 fixed factor (say A and B) in which A is nested in B, and a random factor (C, the plot), using the aov(). So my first idea would be something like: aov(H ~ B * A %in% B * Time + Error(id) ) where id is the factor coded for the repeated

Suppose I have the following data: y x id 44 0 104 48 58 104 48 55 204 47 105 204 41 275 206 18 67 209 ....... I fit the model >fit=lme(y~x+I(x^2),random=~1|id) Now I want to make a prediction plot: >time=seq(0,300,len=100) >plot(predict(fit,data.frame(x=time),level=0)) Very fine. It gives me the prediction curve based on the model. My further request is to make a confidence bands

Dear R-users, Is anyone aware of any function/package for generating a random vector from a joint distribution defined by an independent graph? Or I have to work it out myself? Thanks. Jinfang ------------------------------ Jinfang Wang, Associate Professor Chiba University, Japan

Hello, has someone written by chance a function to extract the variance-covariance matrix from a lmer-object? I've noticed the VarCorr function, but it gives unhandy output. Regards, Roel de Jong

I'm using the lme function in nmle to estimate the variance components of a fully nested two-level model: Y_ijk = mu + a_i + b_j(i) + e_k(j(i)) lme computes estimates of the variances for a, b, and e, call them v_a, v_b, and v_e, and I can use the intervals function to get confidence intervals. My understanding is that these intervals are probably not that robust plus I need intervals on the

Dear listers, I am trying to assess variance components for a nested, mixed-effects model. I think I got an answer that make sense from R, but I have a warning message and I wanted to check that what I am looking at is actually what I need: my data are organized as transects within stations, stations within habitats, habitats within lagoons. lagoons: random, habitats: fixed the question is:

Dear R-help group, I would like to model directly following random effect model: Y_ik = M_ik + E_ik where M_ik ~ N(Mew_k,tau_k^2) E_ik ~ N(0,s_ik^2) i = number of study k = number of treatment --------------------------------------------------------------------------- I have practiced using the command from 'Mixed -Effects models in S and S-plus'

Dear All -- I am trying to use within a little table producing code an anova comparison of two gls fitted objects, contained in a list of such object, obtained using nlme function gls. The anova procedure fails to locate the second of the objects. The following code, borrowed from the help page of anova.gls, exemplifies: --------------- start example code --------------- library(nlme) ##

Hello, I am looking for a way to obtain standard errors for emprirical Bayes estimates of a model fitted with lmer (like the ones plotted on page 14 of the document available at http://www.eric.ed.gov/ERICDocs/data/ericdocs2/content_storage_01/0000000b/80/2b/b3/94.pdf). Harold Doran mentioned (http://tolstoy.newcastle.edu.au/~rking/R/help/05/08/10638.html) that the posterior modes' variances

I am currently trying to replicate the results I got from RATS for a panel regression. The codes in RATS looks like this: * Final equation for Office Cap Rate Spread * Regression, Panel Data preg(effects=time, method=random) CapRate # CapRate{1} RentCycle{1} VacancyChangeYTY InflationYTY RealGDPyty Just wonder what R package also allow me to have the options like (effects=time, method=random).

Dear R-helpers, I was wondering if anyone has or knows someone who might have an implementation of algorithm for estimating garcht-t, egarch and gjr models. I try to use Fseries but I don't know how to code these models. Thanks a million in advance, Sincerely, Nongluck

I'm trying to produce empirical bayes estimates based on the lognormal model in disease mapping Is there a way this can be done in R? thanks Oarabile

Dear listers, glmmPQL (package MASS) is given to work by repeated call to lme. In the classical outputs glmmPQL the Variance Structure is given as " fixed weights, Formula: ~invwt". The script shows that the function varFixed() is used, though the place where 'invwt' is defined remains unclear to me. I wonder if there is an easy way to specify another variance

I am trying to understand the proper way to encode the nesting structure for data in the context of nlme, in the specific case of individuals nested within species for which each individual is unique. I have searched through Pinheiro & Bates and also past postings, but without success. Take the Wafer data set which has 2 levels: Wafer (8 values) and Site nested within Wafer (10 values for

Hello, I'm working on the multiple categorical data (5-points scale) using linear mixed effect model and wondering if anyone knows about or works on the linear mixed effect model with ordered logit or probit link. I found that the "lmer" function in R is very flexible and supports various models, but not ordered logit/probit models. I may conduct my analysis by turning my DVs

HI I wonder if anyone can help. I have a longitudinal sample of 100 subjects: 200 data points were acquired starting at different ages and at irregular intervals (subjects have different numbers of repeated data points, so some have only one data point). I have been examining the relationship over time (it is quadratic) of continuous variables A on variable B. To model this I have been using