Usage
Anova(mod, ...)
Manova(mod, ...)
## S3 method for class 'lm':
Anova(mod, error, type=c("II","III", 2, 3),
white.adjust=c(FALSE, TRUE, "hc3", "hc0", "hc1", "hc2", "hc4"),
vcov.=NULL, singular.ok, ...)
## S3 method for class 'aov':
Anova(mod, ...)
## S3 method for class 'glm':
Anova(mod, type=c("II","III", 2, 3),
test.statistic=c("LR", "Wald", "F"),
error, error.estimate=c("pearson", "dispersion", "deviance"),
singular.ok, ...)
## S3 method for class 'multinom':
Anova(mod, type = c("II","III", 2, 3), ...)
## S3 method for class 'polr':
Anova(mod, type = c("II","III", 2, 3), ...)
## S3 method for class 'mlm':
Anova(mod, type=c("II","III", 2, 3), SSPE, error.df,
idata, idesign, icontrasts=c("contr.sum", "contr.poly"), imatrix,
test.statistic=c("Pillai", "Wilks", "Hotelling-Lawley", "Roy"),...)
## S3 method for class 'manova':
Anova(mod, ...)
## S3 method for class 'mlm':
Manova(mod, ...)
## S3 method for class 'Anova.mlm':
print(x, ...)
## S3 method for class 'Anova.mlm':
summary(object, test.statistic, univariate=TRUE,
multivariate=TRUE, ...)
## S3 method for class 'summary.Anova.mlm':
print(x, digits = getOption("digits"), ... )
## S3 method for class 'coxph':
Anova(mod, type=c("II", "III", 2, 3),
test.statistic=c("LR", "Wald"), ...)
## S3 method for class 'coxme':
Anova(mod, type=c("II", "III", 2, 3),
test.statistic=c("Wald", "LR"), ...)
## S3 method for class 'lme':
Anova(mod, type=c("II","III", 2, 3),
vcov.=vcov(mod), singular.ok, ...)
## S3 method for class 'mer':
Anova(mod, type=c("II", "III", 2, 3),
test.statistic=c("Chisq", "F"), vcov.=vcov(mod), singular.ok, ...)
## S3 method for class 'merMod':
Anova(mod, type=c("II", "III", 2, 3),
test.statistic=c("Chisq", "F"), vcov.=vcov(mod), singular.ok, ...)
## S3 method for class 'svyglm':
Anova(mod, ...)
## S3 method for class 'rlm':
Anova(mod, ...)
## S3 method for class 'default':
Anova(mod, type=c("II", "III", 2, 3),
test.statistic=c("Chisq", "F"), vcov.=vcov(mod),
singular.ok, ...)
Arguments
mod
lm
, aov
, glm
, multinom
, polr
mlm
, coxph
, coxme
, lme
, mer
, merMod
, svyglm
,
rlm
error
for a linear model, an lm
model object from which the
error sum of squares and degrees of freedom are to be calculated. For
F-tests for a generalized linear model, a glm
object from which the
dispersion is to be e
type
type of test, "II"
, "III"
, 2
, or 3
.
singular.ok
defaults to TRUE
for type-II tests, and FALSE
for type-III tests (where the tests for models with aliased coefficients
will not be straightforwardly interpretable);
if FALSE
, a model with aliased coef
test.statistic
for a generalized linear model, whether to calculate
"LR"
(likelihood-ratio), "Wald"
, or "F"
tests; for a Cox
or Cox mixed-effects model, whether to calculate "LR"
(partial-likelihood ratio) o
error.estimate
for F-tests for a generalized linear model, base the
dispersion estimate on the Pearson residuals ("pearson"
, the default); use the
dispersion estimate in the model object ("dispersion"
); or base the dispersion estimate
white.adjust
if not FALSE
, the default,
tests use a heteroscedasticity-corrected coefficient
covariance matrix; the various values of the argument specify different corrections.
See the documentation for h
SSPE
The error sum-of-squares-and-products matrix; if missing, will be computed
from the residuals of the model.
error.df
The degrees of freedom for error; if missing, will be taken from the model.
idata
an optional data frame giving a factor or factors defining the
intra-subject model for multivariate repeated-measures data. See
Details for an explanation of the intra-subject design and for
further explanation of the other argume
idesign
a one-sided model formula using the ``data'' in idata
and
specifying the intra-subject design.
icontrasts
names of contrast-generating functions to be applied by default
to factors and ordered factors, respectively, in the within-subject
``data''; the contrasts must produce an intra-subject model
matrix in which different terms are orthogonal.
imatrix
as an alternative to specifying idata
, idesign
, and
(optionally) icontrasts
, the model matrix for the within-subject design
can be given directly in the form of list of named elements. Each element gives
x, object
object of class "Anova.mlm"
to print or summarize.
multivariate, univariate
compute and print multivariate and univariate tests for a repeated-measures
ANOVA; the default is TRUE
for both.
digits
minimum number of significant digits to print.
vcov.
in the default
method, an optional coefficient-covariance matrix or function
to compute a covariance matrix, computed by default by applying the generic vcov
function to the model object.
A similar argument may be suppl