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My project involves stink bug sampling on soybeans and I'm using Taylor's Power law (logvar ~ logmean) to use its parameters in the development of a sampling plan.

I'm using a mixed effect model to analyze the effect of fixed and random effects on intercept and slopes of logvar:

  1. fixed effects: log mean + state(8 states) + location (field interior vs. field edge) + lifestage (adult insect vs. nymph insect)
  2. random effects: field(46 fields) and location (18 locations)

My data is normally distributed and I'm using lmer() to analyze it.

lmer(logvar ~ logmean + state + location + lifestage + (1|field) + (1|location), mydata)

In the R output I have 0 variances and Std. Error for either field and location random effects.

Does it means that adding these effects to my model does not explain any variance in the logvar?


I used sample_unit in fixed effects to describe location of the transect in the field. Location in random effects if location of fields. Here is my output:

> summary(lmm)
Linear mixed model fit by maximum likelihood  ['lmerMod']
Formula: alogvar ~ alogmean + lifestage + sample_unit + state + (1 | location) + (1 | field)
   Data: sbdata5

     AIC      BIC   logLik deviance df.resid 
   817.1    863.4   -397.6    795.1      485 

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-4.4644 -0.4447  0.0825  0.3992  3.1649 

Random effects:
 Groups   Name        Variance Std.Dev.
 field    (Intercept) 0.0000   0.0000  
 location (Intercept) 0.0000   0.0000  
 Residual             0.2909   0.5393  
Number of obs: 496, groups:  field, 32; location, 11

Fixed effects:
                    Estimate Std. Error t value
(Intercept)          0.09939    0.06138    1.62
alogmean             1.10635    0.02614   42.32
lifestageadults     -0.08014    0.05058   -1.58
sample_unitinterior  0.09683    0.05073    1.91
stateminnesota       0.09547    0.07320    1.30
statemissouri        0.10510    0.07176    1.46
statenebraska       -0.10565    0.09316   -1.13
statesouthdakota     0.05909    0.08437    0.70

Correlation of Fixed Effects:
            (Intr) alogmn lfstgd smpl_n sttmnn sttmss sttnbr
alogmean    -0.051                                          
lifestgdlts -0.383  0.266                                   
smpl_ntntrr -0.329  0.285  0.048                            
stateminnst -0.618  0.326  0.103  0.029                     
statemissor -0.588 -0.110 -0.064 -0.089  0.495              
statenebrsk -0.439  0.043 -0.049 -0.052  0.424  0.416       
statesthdkt -0.538  0.076  0.060 -0.027  0.477  0.451  0.357
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  • $\begingroup$ The variance is never 0. It either is positive or infinite. $\endgroup$ – Michael R. Chernick Mar 6 '17 at 5:18
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    $\begingroup$ You have used the same variable name 'location' for both a fixed and random effect: I suspect one of these is a mistake. It would help to see a summary of your data frame. $\endgroup$ – Matt Denwood Mar 6 '17 at 6:58

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