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)
On 4/16/08, Simon Blomberg <[EMAIL PROTECTED]> wrote:
> 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.
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