I am trying to decipher, via post hoc test (Tukey), which of my sites
differ from eachother. I have 4 sites, 2 sets of In vs Out (MPA) in
separate Regions. Therefore my Mixed Effects Model code has 2 fixed
effects:

 

CB.lme <- lme(AsinCB~ In_Out*Region, random =  (~1|  site.trans/Quadrat)
,  data = Subsampled_props, control = lmeControl(maxIter = 500,
msMaxIter = 500, msMaxEval = 500))

 

When I run the summary and anova output, I still need to differentiate
which categories are the same/different and the results only tell me for
each subgroup. i.e., I know that there is a sig difference in coral
branching In vs Out of the MPA but I don't know if this is in both
regions or only one and if so, which one! 

 

I've got the following code to run a Tukey test, but it doesn't allow
for more than one fixed effect and I have two : In_Out*Region.  As is it
only works for one or the other.

 

summary(glht(CB.lme,linfct=mcp(In_Out="Tukey")))

 

 

Does anyone know how to incorporate more than one fixed effect in lme
post hoc tests?

 

Thanks!

 

Melissa Evanson

 

 

The summary output for the model:

 

Linear mixed-effects model fit by REML

 Data: Subsampled_props 

        AIC       BIC   logLik

  -1200.400 -1166.024 607.1998

 

Random effects:

 Formula: ~1 | site.trans

        (Intercept)

StdDev:  0.06543219

 

 Formula: ~1 | Quadrat %in% site.trans

                  (Intercept)   Residual

StdDev:   0.1171801 0.04607132

 

Fixed effects: AsinCB ~ In_Out * Region 

                               Value                     Std.Error
DF           t-value                  p-value

(Intercept)        0.2317220              0.02211590          967
10.477617            0.0000

In_OutOut         -0.2061529           0.03131558          36
-6.583079             0.0000

RegionB               -0.0390728          0.03131977          36
-1.247544            0.2203

In_OutOut:RegionB  0.1460696 0.04433322           36           3.294813
0.0022

 Correlation: 

                                (Intr)     In_OtO                 ReginB

In_OutOut         -0.706              

RegionB           -0.706        0.499       

In_OutOut:RegionB  0.499 -0.706              -0.706

 

Standardized Within-Group Residuals:

        Min          Q1         Med          Q3         Max 

-1.01939542 -0.20739978 -0.03831860  0.23440502  1.45546317 

 

Number of Observations: 1007

Number of Groups: 

             site.trans Quadrat %in% site.trans 

                     40                    1007  

 

 

My anova output is:

 

                numDF                 denDF                   F-value
p-value

(Intercept)       1                967                         172.90334
<.0001

In_Out            1                   36
36.13478              <.0001

Region            1                    36
2.32907                 0.1357

In_Out:Region     1           36                           10.85579
0.0022


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