linearHypothesis | R Documentation |
Generic function for testing a linear hypothesis, and methods
for linear models, generalized linear models, multivariate linear
models, linear and generalized linear mixed-effects models,
generalized linear models fit with svyglm
in the survey package,
robust linear models fit with rlm
in the MASS package,
and other models that have methods for coef
and vcov
.
For mixed-effects models, the tests are Wald chi-square tests for the fixed effects.
linearHypothesis(model, ...)
lht(model, ...)
## Default S3 method:
linearHypothesis(model, hypothesis.matrix, rhs=NULL,
test=c("Chisq", "F"), vcov.=NULL, singular.ok=FALSE, verbose=FALSE,
coef. = coef(model), suppress.vcov.msg=FALSE, error.df, ...)
## S3 method for class 'lm'
linearHypothesis(model, hypothesis.matrix, rhs=NULL,
test=c("F", "Chisq"), vcov.=NULL,
white.adjust=c(FALSE, TRUE, "hc3", "hc0", "hc1", "hc2", "hc4"),
singular.ok=FALSE, ...)
## S3 method for class 'glm'
linearHypothesis(model, ...)
## S3 method for class 'lmList'
linearHypothesis(model, ..., vcov.=vcov, coef.=coef)
## S3 method for class 'nlsList'
linearHypothesis(model, ..., vcov.=vcov, coef.=coef)
## S3 method for class 'mlm'
linearHypothesis(model, hypothesis.matrix, rhs=NULL, SSPE, V,
test, idata, icontrasts=c("contr.sum", "contr.poly"), idesign, iterms,
check.imatrix=TRUE, P=NULL, title="", singular.ok=FALSE, verbose=FALSE, ...)
## S3 method for class 'polr'
linearHypothesis(model, hypothesis.matrix, rhs=NULL, vcov.,
verbose=FALSE, ...)
## S3 method for class 'linearHypothesis.mlm'
print(x, SSP=TRUE, SSPE=SSP,
digits=getOption("digits"), ...)
## S3 method for class 'lme'
linearHypothesis(model, hypothesis.matrix, rhs=NULL,
vcov.=NULL, singular.ok=FALSE, verbose=FALSE, ...)
## S3 method for class 'mer'
linearHypothesis(model, hypothesis.matrix, rhs=NULL,
vcov.=NULL, test=c("Chisq", "F"), singular.ok=FALSE, verbose=FALSE, ...)
## S3 method for class 'merMod'
linearHypothesis(model, hypothesis.matrix, rhs=NULL,
vcov.=NULL, test=c("Chisq", "F"), singular.ok=FALSE, verbose=FALSE, ...)
## S3 method for class 'svyglm'
linearHypothesis(model, ...)
## S3 method for class 'rlm'
linearHypothesis(model, ...)
## S3 method for class 'survreg'
linearHypothesis(model, hypothesis.matrix, rhs=NULL,
test=c("Chisq", "F"), vcov., verbose=FALSE, ...)
matchCoefs(model, pattern, ...)
## Default S3 method:
matchCoefs(model, pattern, coef.=coef, ...)
## S3 method for class 'lme'
matchCoefs(model, pattern, ...)
## S3 method for class 'mer'
matchCoefs(model, pattern, ...)
## S3 method for class 'merMod'
matchCoefs(model, pattern, ...)
## S3 method for class 'mlm'
matchCoefs(model, pattern, ...)
## S3 method for class 'lmList'
matchCoefs(model, pattern, ...)
model |
fitted model object. The default method of |
hypothesis.matrix |
matrix (or vector) giving linear combinations of coefficients by rows, or a character vector giving the hypothesis in symbolic form (see Details). |
rhs |
right-hand-side vector for hypothesis, with as many entries as
rows in the hypothesis matrix; can be omitted, in which case it defaults
to a vector of zeroes. For a multivariate linear model, |
singular.ok |
if |
error.df |
For the |
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 arguments relating to intra-subject factors. |
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. |
idesign |
a one-sided model formula using the “data” in |
iterms |
the quoted name of a term, or a vector of quoted names of terms, in the intra-subject design to be tested. |
check.imatrix |
check that columns of the intra-subject model matrix for
different terms are mutually orthogonal (default, |
P |
transformation matrix to be applied to the repeated measures in
multivariate repeated-measures data; if |
SSPE |
in |
test |
character string, |
title |
an optional character string to label the output. |
V |
inverse of sum of squares and products of the model matrix; if missing it is computed from the model. |
vcov. |
a function for estimating the covariance matrix of the regression
coefficients, e.g., |
coef. |
a vector of coefficient estimates. The default is to get the
coefficient estimates from the |
white.adjust |
logical or character. Convenience interface to |
verbose |
If |
x |
an object produced by |
SSP |
if |
digits |
minimum number of signficiant digits to print. |
pattern |
a regular expression to be matched against coefficient names. |
suppress.vcov.msg |
for internal use by methods that call the default method. |
... |
arguments to pass down. |
linearHypothesis
computes either a finite-sample F statistic or asymptotic Chi-squared
statistic for carrying out a Wald-test-based comparison between a model
and a linearly restricted model. The default method will work with any
model object for which the coefficient vector can be retrieved by
coef
and the coefficient-covariance matrix by vcov
(otherwise
the argument vcov.
has to be set explicitly). For computing the
F statistic (but not the Chi-squared statistic) a df.residual
method needs to be available. If a formula
method exists, it is
used for pretty printing.
The method for "lm"
objects calls the default method, but it
changes the default test to "F"
, supports the convenience argument
white.adjust
(for backwards compatibility), and enhances the output
by the residual sums of squares. For "glm"
objects just the default
method is called (bypassing the "lm"
method). The "svyglm"
method
also calls the default method.
Multinomial logit models fit by the multinom
function in the nnet package invoke the default method, and the coefficient names are composed from the response-level names and conventional coefficient names, separated by a period ("."
): see one of the examples below.
The function lht
also dispatches to linearHypothesis
.
The hypothesis matrix can be supplied as a numeric matrix (or vector), the rows of which specify linear combinations of the model coefficients, which are tested equal to the corresponding entries in the right-hand-side vector, which defaults to a vector of zeroes.
Alternatively, the hypothesis can be specified symbolically as a character vector with one or more elements, each of which gives either a linear combination of coefficients, or a linear equation in the coefficients (i.e., with both a left and right side separated by an equals sign). Components of a linear expression or linear equation can consist of numeric constants, or numeric constants multiplying coefficient names (in which case the number precedes the coefficient, and may be separated from it by spaces or an asterisk); constants of 1 or -1 may be omitted. Spaces are always optional. Components are separated by plus or minus signs. Newlines or tabs in hypotheses will be treated as spaces. See the examples below.
If the user sets the arguments coef.
and vcov.
, then the computations
are done without reference to the model
argument. This is like assuming
that coef.
is normally distibuted with estimated variance vcov.
and the linearHypothesis
will compute tests on the mean vector for
coef.
, without actually using the model
argument.
A linear hypothesis for a multivariate linear model (i.e., an object of
class "mlm"
) can optionally include an intra-subject transformation matrix
for a repeated-measures design.
If the intra-subject transformation is absent (the default), the multivariate
test concerns all of the corresponding coefficients for the response variables.
There are two ways to specify the transformation matrix for the
repeated measures:
The transformation matrix can be specified directly via the P
argument.
A data frame can be provided defining the repeated-measures factor or
factors
via idata
, with default contrasts given by the icontrasts
argument. An intra-subject model-matrix is generated from the one-sided formula
specified by the idesign
argument; columns of the model matrix
corresponding to different terms in the intra-subject model must be orthogonal
(as is insured by the default contrasts). Note that the contrasts given in
icontrasts
can be overridden by assigning specific contrasts to the
factors in idata
.
The repeated-measures transformation matrix consists of the
columns of the intra-subject model matrix corresponding to the term or terms
in iterms
. In most instances, this will be the simpler approach, and
indeed, most tests of interests can be generated automatically via the
Anova
function.
matchCoefs
is a convenience function that can sometimes help in formulating hypotheses; for example
matchCoefs(mod, ":")
will return the names of all interaction coefficients in the model mod
.
For a univariate model, an object of class "anova"
which contains the residual degrees of freedom
in the model, the difference in degrees of freedom, Wald statistic
(either "F"
or "Chisq"
), and corresponding p value.
The value of the linear hypothesis and its covariance matrix are returned
respectively as "value"
and "vcov"
attributes of the object
(but not printed).
For a multivariate linear model, an object of class
"linearHypothesis.mlm"
, which contains sums-of-squares-and-product
matrices for the hypothesis and for error, degrees of freedom for the
hypothesis and error, and some other information.
The returned object normally would be printed.
Achim Zeileis and John Fox jfox@mcmaster.ca
Fox, J. (2016) Applied Regression Analysis and Generalized Linear Models, Third Edition. Sage.
Fox, J. and Weisberg, S. (2019) An R Companion to Applied Regression, Third Edition, Sage.
Hand, D. J., and Taylor, C. C. (1987) Multivariate Analysis of Variance and Repeated Measures: A Practical Approach for Behavioural Scientists. Chapman and Hall.
O'Brien, R. G., and Kaiser, M. K. (1985) MANOVA method for analyzing repeated measures designs: An extensive primer. Psychological Bulletin 97, 316–333.
anova
, Anova
, waldtest
,
hccm
, vcovHC
, vcovHAC
,
coef
, vcov
mod.davis <- lm(weight ~ repwt, data=Davis)
## the following are equivalent:
linearHypothesis(mod.davis, diag(2), c(0,1))
linearHypothesis(mod.davis, c("(Intercept) = 0", "repwt = 1"))
linearHypothesis(mod.davis, c("(Intercept)", "repwt"), c(0,1))
linearHypothesis(mod.davis, c("(Intercept)", "repwt = 1"))
## use asymptotic Chi-squared statistic
linearHypothesis(mod.davis, c("(Intercept) = 0", "repwt = 1"), test = "Chisq")
## the following are equivalent:
## use HC3 standard errors via white.adjust option
linearHypothesis(mod.davis, c("(Intercept) = 0", "repwt = 1"),
white.adjust = TRUE)
## covariance matrix *function*
linearHypothesis(mod.davis, c("(Intercept) = 0", "repwt = 1"), vcov = hccm)
## covariance matrix *estimate*
linearHypothesis(mod.davis, c("(Intercept) = 0", "repwt = 1"),
vcov = hccm(mod.davis, type = "hc3"))
mod.duncan <- lm(prestige ~ income + education, data=Duncan)
## the following are all equivalent:
linearHypothesis(mod.duncan, "1*income - 1*education = 0")
linearHypothesis(mod.duncan, "income = education")
linearHypothesis(mod.duncan, "income - education")
linearHypothesis(mod.duncan, "1income - 1education = 0")
linearHypothesis(mod.duncan, "0 = 1*income - 1*education")
linearHypothesis(mod.duncan, "income-education=0")
linearHypothesis(mod.duncan, "1*income - 1*education + 1 = 1")
linearHypothesis(mod.duncan, "2income = 2*education")
mod.duncan.2 <- lm(prestige ~ type*(income + education), data=Duncan)
coefs <- names(coef(mod.duncan.2))
## test against the null model (i.e., only the intercept is not set to 0)
linearHypothesis(mod.duncan.2, coefs[-1])
## test all interaction coefficients equal to 0
linearHypothesis(mod.duncan.2, coefs[grep(":", coefs)], verbose=TRUE)
linearHypothesis(mod.duncan.2, matchCoefs(mod.duncan.2, ":"), verbose=TRUE) # equivalent
lh <- linearHypothesis(mod.duncan.2, coefs[grep(":", coefs)])
attr(lh, "value") # value of linear function
attr(lh, "vcov") # covariance matrix of linear function
## a multivariate linear model for repeated-measures data
## see ?OBrienKaiser for a description of the data set used in this example.
mod.ok <- lm(cbind(pre.1, pre.2, pre.3, pre.4, pre.5,
post.1, post.2, post.3, post.4, post.5,
fup.1, fup.2, fup.3, fup.4, fup.5) ~ treatment*gender,
data=OBrienKaiser)
coef(mod.ok)
## specify the model for the repeated measures:
phase <- factor(rep(c("pretest", "posttest", "followup"), c(5, 5, 5)),
levels=c("pretest", "posttest", "followup"))
hour <- ordered(rep(1:5, 3))
idata <- data.frame(phase, hour)
idata
## test the four-way interaction among the between-subject factors
## treatment and gender, and the intra-subject factors
## phase and hour
linearHypothesis(mod.ok, c("treatment1:gender1", "treatment2:gender1"),
title="treatment:gender:phase:hour", idata=idata, idesign=~phase*hour,
iterms="phase:hour")
## mixed-effects models examples:
## Not run: # loads nlme package
library(nlme)
example(lme)
linearHypothesis(fm2, "age = 0")
## End(Not run)
## Not run: # loads lme4 package
library(lme4)
example(glmer)
linearHypothesis(gm1, matchCoefs(gm1, "period"))
## End(Not run)
if (require(nnet)){
print(m <- multinom(partic ~ hincome + children, data=Womenlf))
print(coefs <- as.vector(outer(c("not.work.", "parttime."),
c("hincome", "childrenpresent"),
paste0)))
linearHypothesis(m, coefs) # ominbus Wald test
}
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