Summaries of Linear Mixed Models with coefficient tables including t-tests and p-values using Satterthwaites's or Kenward-Roger's methods for degrees-of-freedom and t-statistics.
an lmerModLmerTest object.
additional arguments passed on to lme4::summary.merMod
the method for computing the degrees of freedom and
t-statistics. ddf="Satterthwaite" (default) uses Satterthwaite's method;
ddf="Kenward-Roger" uses Kenward-Roger's method,
ddf = "lme4" returns the lme4-summary i.e., using the summary
method for lmerMod objects as defined in the lme4-package and
ignores the type argument. Partial matching is allowed.
A summary object with a coefficient table (a matrix) including
t-values and p-values. The coefficient table can be extracted with
coef(summary(<my-model>)).
The returned object is of class
c("summary.lmerModLmerTest", "summary.merMod") utilizing print,
coef and other methods defined for summary.merMod objects.
The "Kenward-Roger" method use methods from the pbkrtest package internally
to compute t-statistics and associated degrees-of-freedom.
# Fit example model:
data("sleepstudy", package="lme4")
fm <- lmer(Reaction ~ Days + (1|Subject) + (0+Days|Subject), sleepstudy)
# Get model summary:
summary(fm) # Satterthwaite df and t-tests
#> Linear mixed model fit by REML. t-tests use Satterthwaite's method [
#> lmerModLmerTest]
#> Formula: Reaction ~ Days + (1 | Subject) + (0 + Days | Subject)
#> Data: sleepstudy
#>
#> REML criterion at convergence: 1743.7
#>
#> Scaled residuals:
#> Min 1Q Median 3Q Max
#> -3.9626 -0.4625 0.0204 0.4653 5.1860
#>
#> Random effects:
#> Groups Name Variance Std.Dev.
#> Subject (Intercept) 627.57 25.051
#> Subject.1 Days 35.86 5.988
#> Residual 653.58 25.565
#> Number of obs: 180, groups: Subject, 18
#>
#> Fixed effects:
#> Estimate Std. Error df t value Pr(>|t|)
#> (Intercept) 251.405 6.885 18.156 36.513 < 2e-16 ***
#> Days 10.467 1.560 18.156 6.712 2.59e-06 ***
#> ---
#> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#>
#> Correlation of Fixed Effects:
#> (Intr)
#> Days -0.184
# Extract coefficient table:
coef(summary(fm))
#> Estimate Std. Error df t value Pr(>|t|)
#> (Intercept) 251.40510 6.885381 18.15620 36.512883 1.893381e-18
#> Days 10.46729 1.559569 18.15609 6.711653 2.594097e-06
# Use the Kenward-Roger method
if(requireNamespace("pbkrtest", quietly = TRUE))
summary(fm, ddf="Kenward-Roger")
#> Linear mixed model fit by REML. t-tests use Kenward-Roger's method [
#> lmerModLmerTest]
#> Formula: Reaction ~ Days + (1 | Subject) + (0 + Days | Subject)
#> Data: sleepstudy
#>
#> REML criterion at convergence: 1743.7
#>
#> Scaled residuals:
#> Min 1Q Median 3Q Max
#> -3.9626 -0.4625 0.0204 0.4653 5.1860
#>
#> Random effects:
#> Groups Name Variance Std.Dev.
#> Subject (Intercept) 627.57 25.051
#> Subject.1 Days 35.86 5.988
#> Residual 653.58 25.565
#> Number of obs: 180, groups: Subject, 18
#>
#> Fixed effects:
#> Estimate Std. Error df t value Pr(>|t|)
#> (Intercept) 251.405 6.885 18.187 36.513 < 2e-16 ***
#> Days 10.467 1.560 18.187 6.712 2.57e-06 ***
#> ---
#> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#>
#> Correlation of Fixed Effects:
#> (Intr)
#> Days -0.184
# The lme4-summary table:
summary(fm, ddf="lme4") # same as summary(as(fm, "lmerMod"))
#> Linear mixed model fit by REML ['lmerMod']
#> Formula: Reaction ~ Days + (1 | Subject) + (0 + Days | Subject)
#> Data: sleepstudy
#>
#> REML criterion at convergence: 1743.7
#>
#> Scaled residuals:
#> Min 1Q Median 3Q Max
#> -3.9626 -0.4625 0.0204 0.4653 5.1860
#>
#> Random effects:
#> Groups Name Variance Std.Dev.
#> Subject (Intercept) 627.57 25.051
#> Subject.1 Days 35.86 5.988
#> Residual 653.58 25.565
#> Number of obs: 180, groups: Subject, 18
#>
#> Fixed effects:
#> Estimate Std. Error t value
#> (Intercept) 251.405 6.885 36.513
#> Days 10.467 1.560 6.712
#>
#> Correlation of Fixed Effects:
#> (Intr)
#> Days -0.184