<?xml version="1.0" encoding="utf-8"?><!DOCTYPE article  PUBLIC '-//OASIS//DTD DocBook XML V4.4//EN'  'http://www.docbook.org/xml/4.4/docbookx.dtd'><article><articleinfo><title>FAQ/refsp</title><revhistory><revision><revnumber>4</revnumber><date>2013-03-08 10:17:55</date><authorinitials>localhost</authorinitials><revremark>converted to 1.6 markup</revremark></revision><revision><revnumber>3</revnumber><date>2010-11-30 17:29:48</date><authorinitials>PeterWatson</authorinitials></revision><revision><revnumber>2</revnumber><date>2010-11-30 17:28:07</date><authorinitials>PeterWatson</authorinitials></revision><revision><revnumber>1</revnumber><date>2010-11-30 17:27:43</date><authorinitials>PeterWatson</authorinitials></revision></revhistory></articleinfo><section><title>Obtaining p-values in random effects models in R</title><para>The pvals.fnc procedure which uses Monte-Carlo Markov Chain simulations to compute p-values for terms in random effect models will run in R 2,.10 and above with the lmer procedure which is in the lme4 library. An example of its use to assess differences between conditions (assumed to be fixed effects) is given below. </para><screen><![CDATA[library(foreign)
x <- read.spss("C:\\Documents and Settings\\peterw\\Desktop\\My Documents\\My Documents2\\SAMI R LME4\\mask.sav")
attach(x)
library(lme4)
install.packages("languageR")
library(languageR)
model <- lmer(ptotalfs ~ condition + (1|subj))
pvals.fnc(model)]]></screen></section></article>