[R-sig-ME] significance of slope (different than zero) in triple interaction

[Guillaume Adeux]

Hi mixmoders,

I have the following model:

mod=glmer(Weed_density~block+scale(year)*syst*timing+(1|year)+(1|plot)+(1|plot:year)+(1|ID_quadrat)+(1|OLRE)+offset(log(size_quadrat)),family=poisson(link="log"),dat=WEED)

I have a significant triple interaction between time : treatment : season.

Time is continuous, syst(=treatment) has 5 levels and season(=sampling
session) has two levels.

Here is the model output:

Generalized linear mixed model fit by maximum likelihood (Laplace
Approximation) ['glmerMod']
 Family: poisson  ( log )
Formula: WDall ~ block + scale(year) * syst * timing + (1 | year) + (1
|      plot) + (1 | plot:year) + (1 | ID_quadrat) + (1 | OLRE) +
offset(log(size_quadrat))
   Data: WEED_paired_2
Control: glmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 2e+05))

     AIC      BIC   logLik deviance df.resid
 21206.3  21371.9 -10577.2  21154.3     4286

Scaled residuals:
    Min      1Q  Median      3Q     Max
-1.6531 -0.4373 -0.1646  0.1426  2.6313

Random effects:
 Groups     Name        Variance  Std.Dev.
 OLRE       (Intercept) 4.456e-01 6.675e-01
 ID_quadrat (Intercept) 1.011e+00 1.006e+00
 plot:year  (Intercept) 1.429e+00 1.195e+00
 year       (Intercept) 5.635e-15 7.506e-08
 plot       (Intercept) 0.000e+00 0.000e+00
Number of obs: 4312, groups:  OLRE, 4312; ID_quadrat, 2156; plot:year,
86; year, 17; plot, 10

Fixed effects:
                                Estimate Std. Error z value Pr(>|z|)
(Intercept)                     -0.84765    0.33352  -2.542 0.011036 *
blockD                          -0.28663    0.27596  -1.039 0.298971
scale(year)                      0.11385    0.25128   0.453 0.650500
systS2                           2.21797    0.43765   5.068 4.02e-07 ***
systS3                           2.97934    0.42857   6.952 3.61e-12 ***
systS4                           2.64787    0.43488   6.089 1.14e-09 ***
systS5                           0.55059    0.45565   1.208 0.226912
timingavant1                     1.87971    0.10286  18.275  < 2e-16 ***
scale(year):systS2               0.40061    0.38882   1.030 0.302863
scale(year):systS3               0.44798    0.37297   1.201 0.229698
scale(year):systS4              -0.01245    0.36549  -0.034 0.972819
scale(year):systS5               1.06031    0.37957   2.793 0.005215 **
scale(year):timingavant1         0.07949    0.09954   0.799 0.424489
systS2:timingavant1             -0.36039    0.12128  -2.972 0.002963 **
systS3:timingavant1             -0.56704    0.11777  -4.815 1.47e-06 ***
systS4:timingavant1             -0.39785    0.11984  -3.320 0.000901 ***
systS5:timingavant1             -0.06724    0.14990  -0.449 0.653770
scale(year):systS2:timingavant1 -0.15246    0.11992  -1.271 0.203628
scale(year):systS3:timingavant1 -0.04057    0.11556  -0.351 0.725543
scale(year):systS4:timingavant1 -0.49134    0.11614  -4.231 2.33e-05 ***
scale(year):systS5:timingavant1 -0.34391    0.13427  -2.561 0.010429 *
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1


I wish to set up constrats to test if the slopes for scale(year):syst
differ from zero at level 1 of timing.

It seems like we can do this with testInteractions but I'm not sure if my
set up is correct:

testInteractions(mod1,custom=list(syst=c(1,0,0,0,0),timing=c(1,0)),
slope="scale(year)", adjustment="none")

The preceding code yields the following:

Adjusted slope for scale(year)
Chisq Test:
P-value adjustment method: none
                   Value Df  Chisq Pr(>Chisq)
syst1 : timing1 -0.82831  1 0.6464     0.4214

This doesn't seem correct because Value doesn't represent the slope for the
first level of "syst" at the first level of "timing".

Could anyone shed their light?

Thank you very much!

Guillaume ADEUX

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