Fits and calculates pvalues for all effects in a mixed
model fitted with lmer
. The default
behavior (currently the only behavior implemented)
calculates type 3 like pvalues using the KenwardRogers
approximation for degreesoffreedom implemented in
KRmodcomp
. print
,
summary
, and anova
methods for the returned
object of class "mixed"
are available (all return
the same data.frame).
1 2 
fixed 
character vector specifying the fixed part of the model. 
random 
character vector specifying the random part of the model (in the current implementation this random part is fit with all models). 
dv 
character vector specifying the dependent variable 
data 
data.frame containing the data. Should have
all the variables present in 
type 
type of sums of squares on which effects are
based. Currently only type 3 ( 
method 
character vector indicating which methods
for obtaining pvalues should be used. Currently only

... 
further arguments passed to 
Type 3 sums of squares are obtained by fitting a model in which only the corresponding effect is missing.
See Judd, Westfall, and Kenny (2012) for examples of how to specify the random effects structure for factorial experiments.
An object of class "mixed"
(i.e., a list) with the
following elements:
anova.table
a data.frame
containing the statistics returned from
KRmodcomp
.
full.model
the "mer"
object returned from
fitting the full mixed model.
restricted.models
a list of "mer"
objects
from fitting the restricted models (i.e., each model
lacks the corresponding effect)
tests
a list
of objects returned by the function for obtaining the
pvalues (objects are of class "KRmodcomp"
when
method = "KR"
).
type
The type
argument used when calling this function.
method
The method
argument used when
calling this function.
The following methods exist for objects of class
"mixed"
: print
, summary
, and
anova
(all return the same data.frame).
This functions may take some time especially with complex random structures.
Henrik Singmann with contributions from Ben Bolker and Joshua Wiley.
Judd, C. M., Westfall, J., & Kenny, D. A. (2012). Treating stimuli as a random factor in social psychology: A new and comprehensive solution to a pervasive but largely ignored problem. Journal of Personality and Social Psychology, 103(1), 54–69. doi:10.1037/a0028347
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23  ## Not run:
# example data from package languageR:
# Lexical decision latencies elicited from 21 subjects for 79 English concrete nouns, with variables linked to subject or word.
data(lexdec, package = "languageR")
# using the simplest model
m1 < mixed("Correct + Trial + PrevType * meanWeight + Frequency + NativeLanguage * Length", "(1Subject) + (1Word)", "RT", data = lexdec)
anova(m1)
# gives:
## Effect df1 df2 Fstat p.value
## 1 (Intercept) 1 96.64 13573.0985 0.000
## 2 Correct 1 1627.73 8.1452 0.004
## 3 Trial 1 1592.43 7.5738 0.006
## 4 PrevType 1 1605.39 0.1700 0.680
## 5 meanWeight 1 75.39 14.8545 0.000
## 6 Frequency 1 76.08 56.5348 0.000
## 7 NativeLanguage 1 27.12 0.6953 0.412
## 8 Length 1 75.83 8.6959 0.004
## 9 PrevType:meanWeight 1 1601.18 6.1823 0.013
## 10 NativeLanguage:Length 1 1555.49 14.2445 0.000
## End(Not run)

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