Post hoc tests with lme

Thanks for the suggestion, Somon! I did try glht from multcomp
package, but the problem is that for the hypothesis

H0: TypeT1 =0 and TypeT2 = 0

it gives results for two separate hypotheses H01: TypeT1 =0 and H02:
TypeT2 = 0, not exactly one statistic for the original hypothesis H0.
So my question is, how can I get only one statistic for H0? Any more
suggestions?

Thanks,
Gang

library(nlme)
fm <- lme(effort~Type-1, data=ergoStool, random=~1|Subject)
library(multcomp)
summary(glht(fm, linfct=c("TypeT1=0", "TypeT2=0")))

Simultaneous Tests for General Linear Hypotheses

Fit: lme.formula(fixed = effort ~ Type - 1, data = ergoStool, random = ~1 |
    Subject)

Linear Hypotheses:
            Estimate Std. Error z value p value
TypeT1 == 0    8.556      0.576   14.85  <1e-10 ***
TypeT2 == 0   12.444      0.576   21.60  <1e-10 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Adjusted p values reported -- single-step method)

Try glht in package multcomp.

 Simon.


 On Wed, 2008-04-16 at 12:00 -0400, Gang Chen wrote:

Using the "ergoStool" data cited in Mixed-Effects Models in S and
S-PLUS by Pinheiro and Bates as an example, we have

========

library(nlme)
fm <- lme(effort~Type-1, data=ergoStool, random=~1|Subject)
summary(fm)

Now suppose I want to test the following hypothesis

H0: TypeT1 =0 and TypeT2 = 0

I've tried estimable() and glh.test() in package gmodels, esticon() in
package boBy, and linear.hypothesis() in package car, but it seems
none of them would work with objects from lme.