linearHypothesis: Test Linear Hypothesis
In car: Companion to Applied Regression
linearHypothesis R Documentation
Test Linear Hypothesis
Description
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.
Usage
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, ...)
Arguments
model
fitted model object. The default method of linearHypothesis works for models
for which the estimated parameters can be retrieved by coef and
the corresponding estimated covariance matrix by vcov. See the
Details for more information.
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, rhs is a
matrix, defaulting to 0. This argument isn't available for F-tests for linear mixed models.
singular.ok
if FALSE (the default), a model with aliased
coefficients produces an error; if TRUE, the aliased coefficients
are ignored, and the hypothesis matrix should not have columns for them.
For a multivariate linear model: will return the hypothesis and error SSP
matrices even if the latter is singular; useful for computing univariate
repeated-measures ANOVAs where there are fewer subjects than df for within-subject
effects.
error.df
For the default linearHypothesis method, if an F-test is requested and if error.df is missing, the error degrees of freedom will be computed by applying the df.residual function to the model; if df.residual returns NULL or NA, then a chi-square test will be substituted for the F-test (with a message to that effect.
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 idata and
specifying the intra-subject design.
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, TRUE). Set to FALSE
only if you have already checked that the intra-subject model matrix is
block-orthogonal.
P
transformation matrix to be applied to the repeated measures in
multivariate repeated-measures data; if NULL and no
intra-subject model is specified, no response-transformation is applied; if
an intra-subject model is specified via the idata, idesign,
and (optionally) icontrasts arguments, then P is generated
automatically from the iterms argument.
SSPE
in linearHypothesis method for mlm objects:
optional error sum-of-squares-and-products matrix; if missing,
it is computed from the model. In print method for
linearHypothesis.mlm objects: if TRUE,
print the sum-of-squares and cross-products matrix for error.
test
character string, "F" or "Chisq",
specifying whether to compute the finite-sample
F statistic (with approximate F distribution) or the large-sample
Chi-squared statistic (with asymptotic Chi-squared distribution). For a
multivariate linear model, the multivariate test statistic to report — one or more of
"Pillai", "Wilks", "Hotelling-Lawley", or "Roy",
with "Pillai" as the default.
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., hccm, or an estimated covariance matrix
for model. See also white.adjust.
For the "lmList" and "nlsList" methods, vcov.
must be a function (defaulting to vcov) to be applied to each model in the list.
Note that arguments supplied to ... are not passed to vcov. when it's a function; in this case either use an anonymous function in which the
additional arguments are set, or supply the coefficient covariance matrix directly (see the examples).
coef.
a vector of coefficient estimates. The default is to get the
coefficient estimates from the model argument, but the user can input
any vector of the correct length.
For the "lmList" and "nlsList" methods, coef.
must be a function (defaulting to coef) to be applied to each model in the list.
white.adjust
logical or character. Convenience interface to hccm
(instead of using the argument vcov.). Can be set either to a character value
specifying the type argument of hccm or TRUE,
in which case "hc3" is used implicitly. The default is FALSE.
verbose
If TRUE, the hypothesis matrix, right-hand-side
vector (or matrix), and estimated value of the hypothesis
are printed to standard output; if FALSE (the default),
the hypothesis is only printed in symbolic form and the value of the hypothesis
is not printed.
x
an object produced by linearHypothesis.mlm.
SSP
if TRUE (the default), print the sum-of-squares and
cross-products matrix for the hypothesis and the response-transformation matrix.
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.
Details
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.
Value
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.
Author(s)
Achim Zeileis and John Fox jfox@mcmaster.ca
References
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.
See Also
anova, Anova, waldtest,
hccm, vcovHC, vcovHAC,
coef, vcov
Examples
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* (where type = "hc3" is the default)
linearHypothesis(mod.davis, c("(Intercept) = 0", "repwt = 1"), vcov. = hccm)
## covariance matrix *estimate*, specifying type explicitly
linearHypothesis(mod.davis, c("(Intercept) = 0", "repwt = 1"),
vcov = hccm(mod.davis, type = "hc3"))
## specifying additional type argument to covariance matrix function
linearHypothesis(mod.davis, c("(Intercept) = 0", "repwt = 1"),
vcov = function(m) hccm(m, 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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| linearHypothesis | R Documentation |
Test Linear Hypothesis
Description
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.
Usage
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, ...)
Arguments
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., Note that arguments supplied to |
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. |
Details
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
Pargument.A data frame can be provided defining the repeated-measures factor or factors via
idata, with default contrasts given by theicontrastsargument. An intra-subject model-matrix is generated from the one-sided formula specified by theidesignargument; 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 inicontrastscan be overridden by assigning specific contrasts to the factors inidata. The repeated-measures transformation matrix consists of the columns of the intra-subject model matrix corresponding to the term or terms initerms. In most instances, this will be the simpler approach, and indeed, most tests of interests can be generated automatically via theAnovafunction.
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.
Value
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.
Author(s)
Achim Zeileis and John Fox jfox@mcmaster.ca
References
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.
See Also
anova, Anova, waldtest,
hccm, vcovHC, vcovHAC,
coef, vcov
Examples
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* (where type = "hc3" is the default)
linearHypothesis(mod.davis, c("(Intercept) = 0", "repwt = 1"), vcov. = hccm)
## covariance matrix *estimate*, specifying type explicitly
linearHypothesis(mod.davis, c("(Intercept) = 0", "repwt = 1"),
vcov = hccm(mod.davis, type = "hc3"))
## specifying additional type argument to covariance matrix function
linearHypothesis(mod.davis, c("(Intercept) = 0", "repwt = 1"),
vcov = function(m) hccm(m, 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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