Solved – code needed for p values in GLMM

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I need to get p values for the fixed effects in the following GLMM's I ran. Does anyone know of code that I can run that will give me the p values I need? At the moment the output from the ANOVA only gives me one p value and I believe I need a separate p value for each of the fixed effects in the models.

Thanks in advance.
Code is as follows –

For GLMM 1 I ran this code –

m1<-lmer(step~Depth*threshold+(1|ind)) m2<-lmer(step~(1|ind)) anova(m1,m2)

For GLMM 2 I ran this code –

m2<-lmer(PDBA~step*threshold+Depth*threshold+(1|ind)) m3<-update(m2,~.-step*threshold) anova(m2,m3)

and this one:

m2<-lmer(PDBA~step*threshold+Depth*threshold+(1|ind)) m4<-update(m2,~.-Depth*threshold) anova(m2,m4)

When I ran GLMM 1 code this is what I got:

m2: step ~ (1 | ind) m1: step ~ Depth * threshold + (1 | ind) Df    AIC    BIC  logLik deviance  Chisq Chi Df Pr(>Chisq)    m2  3 373235 373259 -186615   373229                             m1  8 373225 373290 -186605   373209 19.767      5   0.001382 ** --- Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

summary

> summary(m1) Linear mixed model fit by REML ['lmerMod'] Formula: step ~ Depth * threshold + (1 | ind)   REML criterion at convergence: 373184   Random effects:  Groups   Name        Variance Std.Dev.  ind      (Intercept) 196519   443.3     Residual             469370   685.1    Number of obs: 23473, groups: ind, 11  Fixed effects:                  Estimate Std. Error t value   (Intercept)      160.95895  134.80279   1.194 Depth              0.06438    0.44777   0.144 threshold2        51.18065   17.62222   2.904 threshold3         1.47733   21.43879   0.069 Depth:threshold2  -1.23654    0.60029  -2.060 Depth:threshold3  -0.09587    0.65088  -0.147  Correlation of Fixed Effects:             (Intr) Depth  thrsh2 thrsh3 Dpth:2 Depth       -0.094                             threshold2  -0.090  0.712                      threshold3  -0.075  0.588  0.567               Dpth:thrsh2  0.071 -0.737 -0.745 -0.435        Dpth:thrsh3  0.064 -0.674 -0.490 -0.857  0.502

OUTPUT FROM SUGGESTED CODE BY SETH (IN COMMENTS)

Models: m6: step ~ Depth + threshold + (1 | ind) m5: step ~ Depth + threshold + Depth:threshold + (1 | ind)    Df    AIC    BIC  logLik deviance  Chisq Chi Df Pr(>Chisq)   m6  6 373227 373275 -186607   373215                            m5  8 373225 373290 -186605   373209 5.2901      2      0.071 . --- Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
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Best Answer

I am going to walk through how I do these:

m1<-lmer(step~Depth*threshold+(1|ind)) m2<-lmer(step~(1|ind)) anova(m1,m2)

This anova tells you if the depth*threshold made a significant contribution to model fit. The way it is written with a * indicates that your model m1 should have direct effects and interactions. So the anova is a omnibus type test for all the levels and interaction of the levels of Depth and threshold. If you are interested in particular levels, or if you are interested in the direct effect and not the interaction you should enter them seperately. and leave them out individually and run anovas.

For example, if you are interested in the interaction.

m5<-lmer(step~Depth + threshold + Depth:threshold+(1|ind)) m6<-lmer(step ~ Depth + threshold +(1|ind)) anova(m5,m6)

The p-value from this anova will tell you if the interaction of depth and threshold is significant given the direct effects.

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