search for: 094765

...nally think that p values, and sharp-null hypothesis tests are misguided and should be banned from publications, but it doesn't much matter what I think compared to what the editors want. Based on searching the archives, and finding this message: https://stat.ethz.ch/pipermail/r-help/2006-May/094765.html I am aware of the theoretical difficulties with p values from lmer results. I am also aware of the mcmcsamp function which performs some kind of bayesian sampling from the posterior distribution of the coefficients based on some kind of prior (I will need to do some more reading to more fully...

...to analyze data from an experiment with a single fixed factor (treatment, 6 levels) and a single random factor (block). I've been trying to follow the online guidance for estimating p-values for parameter estimates on these and other help threads: https://stat.ethz.ch/pipermail/r-help/2006-May/094765.html http://pidgin.ucsd.edu/pipermail/r-lang/2007-August/000057.html but have not yet been successful. This is my code: lnmass <- lmer(log.mass ~ treatment + (1|block), data=exp1) summary(lnmass) samp <- rnorm(n=10000) mcmcpvalue <- function(samp) {std <- backsolve(chol(var(samp...

...stributed as an exact F distribution, and that's the reason why the anova method on "mer" objects (calculated by lmer) do not calculate the denominator df nor a p-value. --- See for instance Douglas Bates' long post on this topic, in: https://stat.ethz.ch/pipermail/r-help/2006-May/094765.html However, Anova does calculate p-values from Wald chi-square tests for fixed terms from "mer" objects (as well as from "lme" objects, from lme). I suppose that the key to understand the logic for this is in Fox & Weisberg's commentary in "An R Companion to Appl...

...s that this wasn't an approprate method. I haven't found any other workarounds for this except some general advice that I should move onto using the lmer command (which I can't do because I need to get p-values for my fits and according to https://stat.ethz.ch/pipermail/r-help/2006-May/094765.html I won't get those from lmer). My questions are: (1) Is lmer the only way to do a binomial repeated-measures ANOVA in R? (which means that there's no way to do such an ANOVA in R without losing the p-values) and (2) if I am supposed to be using glmmPQL for this simple situation, what a...

windows Vista R 2.10.1 What is the difference (or differences) between lme and lmer? Both appear to perform mixed effects regression analyses. Thanks John John David Sorkin M.D., Ph.D. Chief, Biostatistics and Informatics University of Maryland School of Medicine Division of Gerontology Baltimore VA Medical Center 10 North Greene Street GRECC (BT/18/GR) Baltimore, MD 21201-1524 (Phone)

I am trying to replicate the repeated measures example from Dr.Faraway's book (Extending the linear model with R) as follows: data(vision) vision$npower <- rep(1:4,14) mmod <-lmer(acuity~power+(1|subject)+(1|subject:eye),vision) When I look at the fixed effects p-value, it is missing. Am I missing something here? Thanks. Fixed effects: Estimate Std. Error t value

Hello, I would appreciate if somebody could help me clear my mind about the below issues. I have a factorial experiment to study the effects of Grazing and Fire on Forest biomass production. The experimental unit (to which the treatment combinations are applied) are PLOTs. The measures were made repeatedly for 13 years. I am planning to use the linear mixed effect model function lme in R for this.

Dear forum members, What is the formula to calculate denominator degrees of freedom (den df) for nonlinear mixed-effect models with covariates? My model is similar to a CO2 uptake example from Pinheiro and Bates (2000, page 376). In this CO2 dataset, there are two treatments and two types (84 observations in total), but den df for each parameter of the model is 64. Isn’t it too high? Your

R 2.8.1 Windows XP I am trying to analyze repeated measures data (the data are listed at the end of this Email message) and I need help to make sure that I have properly specified my model, and would like to know why lmer does not return a p value for Group, my fixed effect. My subjects are divided into two groups (variable GROUP), individual subjects are indicated by the variable SS, Value is

Users are often surprised and alarmed that the summary of a linear mixed model fit by lmer provides estimates of the fixed-effects parameters, standard errors for these parameters and a t-ratio but no p-values. Similarly the output from anova applied to a single lmer model provides the sequential sums of squares for the terms in the fixed-effects specification and the corresponding numerator

First of all, I have GAMs noxd<-gam(newNOX~pressure+maxtemp+s(avetemp,bs="cr")+s(mintemp,bs="cr")+s(RH,bs="cr")+s(solar,bs="cr")+s(windspeed,bs="cr")+s(transport,bs="cr"),family=gaussian (link=log),groupD,methods=REML) Then I type " summary(noxd)". and show Family: gaussian Link function: log Formula: newNO2 ~ pressure