Description Usage Arguments Author(s) Examples
Methods are defined for legacy lmerTest objects of class
merModLmerTest
generated with lmerTest version < 3.00
.
These methods are defined by interfacing code for lmerModLmerTest
methods and therefore behaves like these methods do (which may differ from
the behavior of lmerTest version < 3.00
.)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60  ## S3 method for class 'merModLmerTest'
anova(
object,
...,
type = c("III", "II", "I", "3", "2", "1"),
ddf = c("Satterthwaite", "KenwardRoger", "lme4")
)
## S3 method for class 'merModLmerTest'
summary(object, ..., ddf = c("Satterthwaite", "KenwardRoger", "lme4"))
## S3 method for class 'merModLmerTest'
ls_means(
model,
which = NULL,
level = 0.95,
ddf = c("Satterthwaite", "KenwardRoger"),
pairwise = FALSE,
...
)
## S3 method for class 'merModLmerTest'
lsmeansLT(
model,
which = NULL,
level = 0.95,
ddf = c("Satterthwaite", "KenwardRoger"),
pairwise = FALSE,
...
)
## S3 method for class 'merModLmerTest'
difflsmeans(
model,
which = NULL,
level = 0.95,
ddf = c("Satterthwaite", "KenwardRoger"),
...
)
## S3 method for class 'merModLmerTest'
drop1(
object,
scope,
ddf = c("Satterthwaite", "KenwardRoger", "lme4"),
force_get_contrasts = FALSE,
...
)
## S3 method for class 'merModLmerTest'
step(
object,
ddf = c("Satterthwaite", "KenwardRoger"),
alpha.random = 0.1,
alpha.fixed = 0.05,
reduce.fixed = TRUE,
reduce.random = TRUE,
keep,
...
)

object 
an 
... 
for the anova method optionally additional models; for other
methods see the corresponding 
type 
the type of ANOVA table requested (using SAS terminology) with Type I being the familiar sequential ANOVA table. 
ddf 
the method for computing the denominator degrees of freedom and
Fstatistics. 
model 
a model object fitted with 
which 
optional character vector naming factors for which LSmeans should
be computed. If 
level 
confidence level. 
pairwise 
compute pairwise differences of LSmeans instead? 
scope 
optional character vector naming terms to be dropped from the
model. Note that only marginal terms can be dropped. To see which terms are
marginal, use 
force_get_contrasts 
enforce computation of contrast matrices by a method in which the design matrices for full and restricted models are compared. 
alpha.random 
alpha for random effects elimination 
alpha.fixed 
alpha for fixed effects elimination 
reduce.fixed 
reduce fixed effect structure? 
reduce.random 
reduce random effect structure? 
keep 
an optional character vector of fixed effect terms which should
not be considered for eliminated. Valid terms are given by

Rune Haubo B. Christensen
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19  # Load model fits fm1 and fm2 generated with lmerTest version 2.337:
load(system.file("testdata","legacy_fits.RData", package="lmerTest"))
# Apply some methods defined by lmerTest:
anova(fm1)
summary(fm1)
contest(fm1, c(0, 1))
contest(fm1, c(0, 1), joint=FALSE)
drop1(fm1)
ranova(fm1)
# lme4methods also work:
fixef(fm1)
# Ditto for second model fit:
anova(fm2)
summary(fm2)
ls_means(fm2)
difflsmeans(fm2)

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