Description Usage Arguments Details Value Author(s) See Also Examples
Tests of vector or matrix contrasts for lmer
model fits.
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 | ## S3 method for class 'lmerModLmerTest'
contest(
model,
L,
rhs = 0,
joint = TRUE,
collect = TRUE,
confint = TRUE,
level = 0.95,
check_estimability = FALSE,
ddf = c("Satterthwaite", "Kenward-Roger", "lme4"),
...
)
## S3 method for class 'lmerMod'
contest(
model,
L,
rhs = 0,
joint = TRUE,
collect = TRUE,
confint = TRUE,
level = 0.95,
check_estimability = FALSE,
ddf = c("Satterthwaite", "Kenward-Roger", "lme4"),
...
)
|
model |
a model object fitted with |
L |
a contrast vector or matrix or a list of these.
The |
rhs |
right-hand-side of the statistical test, i.e. the hypothesized value (a numeric scalar). |
joint |
make an F-test of potentially several contrast vectors? If
|
collect |
collect list of tests in a matrix? |
confint |
include columns for lower and upper confidence limits? Applies
when |
level |
confidence level. |
check_estimability |
check estimability of contrasts? Only single DF
contrasts are checked for estimability thus requiring |
ddf |
the method for computing the denominator degrees of freedom.
|
... |
passed to |
If the design matrix is rank deficient, lmer
drops columns for the
aliased coefficients from the design matrix and excludes the corresponding
aliased coefficients from fixef(model)
. When estimability is checked
the original rank-deficient design matrix is recontructed and therefore
L
contrast vectors need to include elements for the aliased
coefficients. Similarly when L
is a matrix, its number of columns
needs to match that of the reconstructed rank-deficient design matrix.
a data.frame
or a list of data.frame
s.
Rune Haubo B. Christensen
contestMD
for multi
degree-of-freedom contrast tests,
and contest1D
for tests of
1-dimensional contrasts.
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 | data("sleepstudy", package="lme4")
fm <- lmer(Reaction ~ Days + I(Days^2) + (1|Subject) + (0+Days|Subject),
sleepstudy)
# F-test of third coeffcients - I(Days^2):
contest(fm, c(0, 0, 1))
# Equivalent t-test:
contest(fm, L=c(0, 0, 1), joint=FALSE)
# Test of 'Days + I(Days^2)':
contest(fm, L=diag(3)[2:3, ])
# Other options:
contest(fm, L=diag(3)[2:3, ], joint=FALSE)
contest(fm, L=diag(3)[2:3, ], joint=FALSE, collect=FALSE)
# Illustrate a list argument:
L <- list("First"=diag(3)[3, ], "Second"=diag(3)[-1, ])
contest(fm, L)
contest(fm, L, collect = FALSE)
contest(fm, L, joint=FALSE, confint = FALSE)
contest(fm, L, joint=FALSE, collect = FALSE, level=0.99)
# Illustrate testing of estimability:
# Consider the 'cake' dataset with a missing cell:
data("cake", package="lme4")
cake$temperature <- factor(cake$temperature, ordered=FALSE)
cake <- droplevels(subset(cake, temperature %in% levels(cake$temperature)[1:2] &
!(recipe == "C" & temperature == "185")))
with(cake, table(recipe, temperature))
fm <- lmer(angle ~ recipe * temperature + (1|recipe:replicate), cake)
fixef(fm)
# The coefficient for recipeC:temperature185 is dropped:
attr(model.matrix(fm), "col.dropped")
# so any contrast involving this coefficient is not estimable:
Lmat <- diag(6)
contest(fm, Lmat, joint=FALSE, check_estimability = TRUE)
|
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