why is lmer splitting up my factor into all its levels in the results?

Question

I am running a linear mixed model for a single response variable on a predictor root.type that has 4 levels; when I run the model, I just want the information on the entire factor, but it keeps splitting it up into the levels. Any ideas?

Ca.auto <- lmer(Ca ~ root.type + (1|pot), data)
summary(Ca.auto)

Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: Ca ~ root.type + (1 | pot)
   Data: autotroph

REML criterion at convergence: -17.5

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-2.2204 -0.4872 -0.1147  0.4371  3.6250 

Random effects:
 Groups   Name        Variance Std.Dev.
 pot      (Intercept) 0.00000  0.0000  
 Residual             0.02884  0.1698  
Number of obs: 42, groups:  pot, 12

Fixed effects:
                               Estimate Std. Error       df t value
(Intercept)                     1.62108    0.05120 38.00000  31.660
root.typeunparasitized host    -0.99282    0.07241 38.00000 -13.711
root.typeattached hemiparasite -0.57593    0.07420 38.00000  -7.762
root.typeparasitized host      -0.97373    0.07420 38.00000 -13.123
                               Pr(>|t|)    
(Intercept)                     < 2e-16 ***
root.typeunparasitized host    2.72e-16 ***
root.typeattached hemiparasite 2.35e-09 ***
root.typeparasitized host      1.09e-15 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:
            (Intr) rt.typnh rt.typth
rt.typnprsh -0.707                  
rt.typttchh -0.690  0.488           
rt.typprsth -0.690  0.488    0.476  
optimizer (nloptwrap) convergence code: 0 (OK)
boundary (singular) fit: see ?isSingular

I was expecting just the one fixed effect (root.type). I updated the packages, but nothing changed.

Answer 1

This is fairly standard behaviour for R modeling packages (i.e., the summary() function reports parameter-level information). Some options for getting the term-level results are: car::Anova(), afex::mixed(); since you're using lmerTest rather than lme4, anova() and drop1() will also work.

For the particular example you show above,

• since you have 38 df, the finite-size correction is pretty small (e.g. 2.5% upper tail is at qt(0.975, 38)==2.02 rather than 1.96)
• your termwise result for root will likely be reported as < 2e-16, since the p-values for the individual contrasts are all already quite small ...