significance of slope (different than zero) in triple interaction

Thu, Sep 6, 2018 3:03 AM

Ok i found out that was messing up everything.

When an offset is included in the model, the slopes produced by
emtrends(model,~factor1|factor2,"num_var") are very different than the ones
that can be computed with the model output (the coefficients for the slopes
in the output need to be divided by the standard deviation of "num_var" in
case of a scaled(num_var) in order to be directly comparable with emtrends
output). I don't know what emtrends is doing in this case.

However, the slopes produced by emtrends(model,~factor1|factor2,"num_var")
are equivalent to the back transformed scaled coefficients of slopes of the
summary output when the offset is taken out. Is there any way to keep the
offset and have the correct values of slope? I do not know.

To come back to my original questions, I was indeed looking for the "null"
argument which I need to set to 0 because the tests are done on the scale
of the linear predictor and a slope of 0 on the log scale is equivalent to
a multiplicative factor of 1 on the original scale (which means a flat
regression line - no effect).

Don't hesitate to give me your feedback if you believe something I have
said above is incorrect.

Thank you a thousand time for you help.

Guillaume ADEUX


Le mer. 5 sept. 2018 ? 19:08, Guillaume Adeux <guillaumesimon.a2 at gmail.com>
a ?crit :

Sorry for the trouble.

What I want to test is that the slopes are effectively increasing or
decreasing; that back on the original scale, the regression line is not
parralel to the x-axis. This must be possible but I don't even know if this
is the way to go.

Thank you very much for you interest,

Guillaume ADEUX

Le mer. 5 sept. 2018 ? 18:54, Ben Bolker <bbolker at gmail.com> a ?crit :

 Even more confusing, your slope CI for apr?s2 seem to (mostly) include
-1, not 1 (except for S5).  Maybe use `null = -1.0` ?

On 2018-09-05 12:38 PM, Lenth, Russell V wrote:

I?m confused. Do you want to test the slopes against zero, or against

1? I ask because you note that most of the confidence intervals include 1.
To test against 1, add `null = 1.0` to the summary() call. To perform an
equivalence test that the slopes do not differ from 1 by more than a
specified threshold, also add `delta = (desired threshold)` to the call.
See help(?summary.emmGrid?) for details.

Russ

From: Guillaume Adeux <guillaumesimon.a2 at gmail.com>
Sent: Wednesday, September 5, 2018 10:13 AM
To: Lenth, Russell V <russell-lenth at uiowa.edu>
Cc: R-mixed models mailing list <r-sig-mixed-models at r-project.org>
Subject: Re: [R-sig-ME] significance of slope (different than zero) in

triple interaction

Hi Russell,
Thank you very much for your answer. It's very nice of you.
I ran what you recommended and it produced the following output:

em=emtrends(mod1,~syst|timing,var="year")
summary(em,infer=c(TRUE,TRUE))

timing = apr?s2:
 syst year.trend         SE  df  asymp.LCL  asymp.UCL z.ratio p.value
 S1   -0.9946823 0.05952514 Inf -1.1113495 -0.8780152 -16.710  <.0001
 S2   -0.8997828 0.07019881 Inf -1.0373699 -0.7621956 -12.818  <.0001
 S3   -0.8885606 0.06511346 Inf -1.0161806 -0.7609405 -13.646  <.0001
 S4   -0.9976324 0.06288404 Inf -1.1208828 -0.8743819 -15.865  <.0001
 S5   -0.7435081 0.06728649 Inf -0.8753872 -0.6116291 -11.050  <.0001

timing = avant1:
 syst year.trend         SE  df  asymp.LCL  asymp.UCL z.ratio p.value
 S1   -0.9758510 0.05696640 Inf -1.0875031 -0.8641990 -17.130  <.0001
 S2   -0.9170666 0.06973012 Inf -1.0537351 -0.7803980 -13.152  <.0001
 S3   -0.8793391 0.06482135 Inf -1.0063867 -0.7522916 -13.566  <.0001
 S4   -1.0951932 0.06253345 Inf -1.2177565 -0.9726298 -17.514  <.0001
 S5   -0.8061455 0.06589310 Inf -0.9352936 -0.6769974 -12.234  <.0001

However, I find this highly surprising because I know the slopes are

not different than 0 for at least 4 of my systems at level "apr?s2" (which
means after). We can actually see that the confidence interval for all
levels of syst at "timing=apr?s2" embrace 1.

Do you have any explanation to this?
Thanks again for your interest.
Guillaume ADEUX

=========================
Le mer. 5 sept. 2018 ? 16:25, Lenth, Russell V <mailto:

russell-lenth at uiowa.edu> a ?crit :

I'm a little confused because you refer to predictors by different

names in different places, and 'year' seems to be used as both a covariate
and a grouping factor. But try something like this:

    library(emmeans)
    emt <- emtrends(mod, ~ syst:timing, var = "year")
    summary(emt, infer = c(TRUE, TRUE))

This will estimate the slope for year at each combination of the two

factors syst and timing. (You may need to re-fit the model after creating
an additional variable, say WEED$syear <- scale(WEED$year), and with syear
in place of scale(year) in the model formula and the emtrends call. You may
follow-up with call(s) to emmeans::contrast(emt, ...) to compare or
contrast these slopes.

Hope that helps.
-- Russ

Russell V. Lenth  -  Professor Emeritus
Department of Statistics and Actuarial Science
The University of Iowa  -  Iowa City, IA 52242  USA
Voice (319)335-0712 (Dept. office)  -  FAX (319)335-3017



-----Original Message-----
Date: Wed, 5 Sep 2018 10:43:39 +0200
From: Guillaume Adeux <mailto:guillaumesimon.a2 at gmail.com>
To: R-mixed models mailing list <mailto:

r-sig-mixed-models at r-project.org>

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


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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significance of slope (different than zero) in triple interaction

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