Re: [R] mixed-effects model with two fixed effects: interaction

From: Setlhare Lekgatlhamang <SetlhareL_at_bob.bw>
Date: Tue, 29 Jun 2010 13:52:27 +0200

When I replied to this message I just hit the reply button. I am resending it using reply to all, in case it did not go to the list.

Dear Ilona,

Looking at the estimation results you have, I think your regression equations are correctly specified. Just thinking aloud, I do not think the results are surprising. Model2 includes more (relevant) regressors than model1. In this green house experiment, one would expect performance in some cases to be jointly determined by the specifies and level of moisture. In that case, the explanatory power of non-interactive terms will drop or vanish when the interactive terms are also included - meanwhile the interactive terms would be significant. I may be wrong but that is my initial thought.

Regards
Lexi

-----Original Message-----
From: r-help-bounces_at_r-project.org [mailto:r-help-bounces_at_r-project.org] On Behalf Of Ilona Leyer
Sent: Tuesday, June 29, 2010 11:10 AM
To: r-help_at_r-project.org
Subject: [R] mixed-effects model with two fixed effects: interaction

Dear all,
In a greenhouse experiment we tested performance of 4 different species
(B,H,P,R) under 3 different water levels in 10 replications. As response
variable e.g. the number of emerging sprouts were measured on three dates. A simple Anova considering every measurement date separately shows a higly significant effect of species and moisture (and partly the interaction of both). The mixed-effects model with species and moisture shows a highly significant effect of species and moisture as well. However, when I included the interaction the t-values of the species dropped strongly and the SE increase and the results for the species are not significant anymore. For me this does not seem plausible. Has anybody an idea, how this can be interpreted and if I have done a mistake in calculating the data?

Thanks in advance for any help!
Ilona

model1<-lme(sprouts~species+moisture,random=~time|ID) model2<-lme(sprouts~species*moisture,random=~time|ID)

Fixed effects: sprouts ~ species + moisture

                         Value Std.Error  DF   t-value p-value

(Intercept) 7.971267 1.330500 240 5.991180 0.0000
speciesH -6.459344 1.536329 114 -4.204400 0.0001 speciesP -10.063604 1.536329 114 -6.550421 0.0000 speciesR -5.051894 1.536329 114 -3.288288 0.0013 moisturemoist 2.228835 1.330500 114 1.675185 0.0966
moisturewaterlogged 17.111149 1.330500 114 12.860688 0.0000

Fixed effects: sprouts ~ species * moisture

                                  Value Std.Error  DF   t-value p-value

(Intercept) 4.831965 1.750970 240 2.759594 0.0062
speciesH -4.464197 2.476245 108 -1.802809 0.0742 speciesP -3.986787 2.476245 108 -1.610013 0.1103 speciesR -0.809376 2.476245 108 -0.326856 0.7444 moisturemoist 3.505506 2.476245 108 1.415654 0.1598 moisturewaterlogged 24.766934 2.476245 108 10.001811 0.0000 speciesH:moisturemoist -0.457291 3.501939 108 -0.130582 0.8963 speciesP:moisturemoist -2.458125 3.501939 108 -0.701932 0.4842 speciesR:moisturemoist -2.555356 3.501939 108 -0.729697 0.4672 speciesH:moisturewaterlogged -5.597498 3.501939 108 -1.598400 0.1129
speciesP:moisturewaterlogged -15.538272 3.501939 108 -4.437048 0.0000 speciesR:moisturewaterlogged -10.206874 3.501939 108 -2.914635 0.0043

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