Solved – How to interpret the lme function result

anovacategorical datalme4-nlmer

the following is the command I used and the results I got for my question, whether the visitation frequency of my bee is different in different experiment types in different locations. I used the lme function of R. I used experiment type as the fixed effect and location as the random effect. I used ANOVA after this to get the F value. Is this right? WIthout ANOVA how I can interpret the results? I had 5 experiment types, but in results it is showing only 4 experiment types. The top most one (expt.antless) is missing.

> names(acera.freq1)
[1] "expt.type"  "visit.freq" "location"  
> model<-lme(visit.freq~expt.type,random=~1|location,method="ML")
> summary(model)
Linear mixed-effects model fit by maximum likelihood
 Data: NULL 
       AIC      BIC    logLik
  1065.928 1087.919 -525.9638

Random effects:
 Formula: ~1 | location
        (Intercept) Residual
StdDev:    4.617241 4.682169

Fixed effects: visit.freq ~ expt.type 
                            Value Std.Error  DF   t-value p-value
(Intercept)             10.192564  1.216852 148  8.376177  0.0000
expt.typeblack.ant      -3.579023  1.074261 148 -3.331615  0.0011
expt.typecrazy.ant      -5.804671  1.740132 148 -3.335765  0.0011
expt.typeother.ants     -5.352438  1.936756 148 -2.763610  0.0064
expt.typered.biting.ant -2.680195  2.048081 148 -1.308637  0.1927
 Correlation: 
                        (Intr) expt.typb. expt.typc. expt.typt.
expt.typeblack.ant      -0.173                                 
expt.typecrazy.ant      -0.128  0.126                          
expt.typeother.ants     -0.081  0.146      0.027               
expt.typered.biting.ant -0.132  0.117      0.143      0.046    

Standardized Within-Group Residuals:
        Min          Q1         Med          Q3         Max 
-1.65727163 -0.70522770 -0.02959964  0.53356588  3.28792891 

Number of Observations: 171
Number of Groups: 19 
> anova(model)
            numDF denDF  F-value p-value
(Intercept)     1   148 56.51744  <.0001
expt.type       4   148  6.28705   1e-04

Best Answer

In short, "expt.antless" condition was used as a reference.

For example, based on the summary table, you can expect that (1) visiting frequency (visit.freq) to decrease by an average of 3.58 from "expt.antless" condition to "expt.typeblack.ant" condition, (2) visiting frequency to decrease by an average of 5.8 from "expt.antless" condition to "expt.typecrazy.ant" condition, and so on.

Fixed effects: visit.freq ~ expt.type 
                            Value Std.Error  DF   t-value p-value
(Intercept)             10.192564  1.216852 148  8.376177  0.0000
expt.typeblack.ant      -3.579023  1.074261 148 -3.331615  0.0011
expt.typecrazy.ant      -5.804671  1.740132 148 -3.335765  0.0011
expt.typeother.ants     -5.352438  1.936756 148 -2.763610  0.0064
expt.typered.biting.ant -2.680195  2.048081 148 -1.308637  0.1927
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