You didn't do anything wrong, lmer doesn't give them. And, for good reason. I've been a bit indoctrinated by D. Bates, so let me share what I've learned.
With simple analysis of variance models with simple error structures, it is known that the ratio of the variances follow and F distribution. However, with more complex error structures, the null distribution is unknown. Most other multilevel programs accept by analogy that the ratio of the variances do follow an F distribution. That is, it works well for the simple case, therefore it probably is also true for the more complex case. In SAS, one can choose ddf options, such as Kenward-Roger, which hopes that after assuming the ratio of variances follow an F distribution, the only remaining challenge is to properly estimate the denominator degrees of freedom. These kinds of options do not currently exist in lmer and after many discussions on this list Doug Bates decided to remove the p-values for now. This topic has been discussed often on this list and you can see other discussions on the archive which may be more insightful. Harold -----Original Message----- From: [EMAIL PROTECTED] [mailto:[EMAIL PROTECTED] On Behalf Of Amelie LESCROEL Sent: Thursday, April 20, 2006 1:41 PM To: r-help@stat.math.ethz.ch Subject: [R] Missing p-values using lmer() Hello, I'm trying to perform a REML analysis using the lmer() function (lme4 package). Well, it seems to work well, except that I'm not getting any p-value (see example below). Can someone tell me what I did wrong? Thanks for your help, Amélie > library(gdata) > dive <- read.xls("C:/Documents and Settings/Amelie/My > Documents/Postdoc/CE 2005-2006/divebydive.xls", perl="C:/perl/bin/perl.exe") > library(lme4) Loading required package: Matrix Loading required package: lattice > reml.res <- lmer(UNDS~SUCCESSMN+(1|BIRD), dive) > summary(reml.res) Linear mixed-effects model fit by REML Formula: UNDS ~ SUCCESSMN + (1 | BIRD) Data: dive AIC BIC logLik MLdeviance REMLdeviance 60032.37 60053.8 -30013.19 60031.9 60026.37 Random effects: Groups Name Variance Std.Dev. BIRD (Intercept) 4.4504 2.1096 Residual 36.4240 6.0352 number of obs: 9324, groups: BIRD, 12 Fixed effects: Estimate Std. Error t value (Intercept) 13.39764 0.63887 20.9707 SUCCESSMN 4.22197 4.11527 1.0259 Correlation of Fixed Effects: (Intr) SUCCESSMN -0.276 > anova(reml.res) Analysis of Variance Table Df Sum Sq Mean Sq SUCCESSMN 1 38.337 38.337 [[alternative HTML version deleted]] ______________________________________________ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html