coeftest {lmtest} | R Documentation |
Inference for Estimated Coefficients
Description
coeftest
is a generic function for performing
z and (quasi-)t Wald tests of estimated coefficients.
coefci
computes the corresponding Wald confidence
intervals.
Usage
coeftest(x, vcov. = NULL, df = NULL, ...)
## Default S3 method:
coeftest(x, vcov. = NULL, df = NULL, ..., save = FALSE)
coefci(x, parm = NULL, level = 0.95, vcov. = NULL, df = NULL, ...)
Arguments
x |
an object (for details see below). |
vcov. |
a specification of the covariance
matrix of the estimated coefficients. This can be
specified as a matrix or as a function yielding
a matrix when applied to |
df |
the degrees of freedom to be used. If this
is a finite positive number a t test with |
... |
further arguments passed to the methods
and to |
save |
logical. Should the object |
parm |
a specification of which parameters are to be given confidence intervals, either a vector of numbers or a vector of names. If missing, all parameters are considered. |
level |
the confidence level required. |
Details
The generic function coeftest
currently has a default
method (which works in particular for "lm"
objects) and
dedicated methods for objects of class
"glm"
(as computed by glm
),
"mlm"
(as computed by lm
with multivariate responses),
"survreg"
(as computed by survreg
), and
"breakpointsfull"
(as computed by breakpoints.formula
).
The default method assumes that a coef
methods exists,
such that coef(x)
yields the estimated coefficients.
To specify the corresponding covariance matrix vcov.
to be used, there
are three possibilities:
1. It is pre-computed and supplied in argument vcov.
.
2. A function for extracting the covariance matrix from
x
is supplied, e.g., sandwich
,
vcovHC
, vcovCL
,
or vcovHAC
from package sandwich.
3. vcov.
is set to NULL
, then it is assumed that
a vcov
method exists, such that vcov(x)
yields
a covariance matrix. Illustrations are provided in the examples below.
The degrees of freedom df
determine whether a normal
approximation is used or a t distribution with df
degrees
of freedom. The default method computes df.residual(x)
and if this is NULL
, 0
, or Inf
a z test is performed.
The method for "glm"
objects always uses df = Inf
(i.e., a z test).
The corresponding Wald confidence intervals can be computed either
by applying coefci
to the original model or confint
to the output of coeftest
. See below for examples.
Finally, nobs
and logLik
methods are provided which work, provided that there are such methods
for the original object x
. In that case, "nobs"
and
"logLik"
attributes are stored in the coeftest
output
so that they can be still queried subsequently. If both methods are
available, AIC
and BIC
can also be applied.
Value
coeftest
returns an object of class "coeftest"
which
is essentially a coefficient matrix with columns containing the
estimates, associated standard errors, test statistics and p values.
Attributes for a "method"
label, and the "df"
are
added along with "nobs"
and "logLik"
(provided that
suitable extractor methods nobs
and
logLik
are available). Optionally, the full
object x
can be save
d in an attribute "object"
to facilitate further model summaries based on the coeftest
result.
coefci
returns a matrix (or vector) with columns giving
lower and upper confidence limits for each parameter. These will
be labeled as (1-level)/2 and 1 - (1-level)/2 in percent.
See Also
Examples
## load data and fit model
data("Mandible", package = "lmtest")
fm <- lm(length ~ age, data = Mandible, subset=(age <= 28))
## the following commands lead to the same tests:
summary(fm)
(ct <- coeftest(fm))
## a z test (instead of a t test) can be performed by
coeftest(fm, df = Inf)
## corresponding confidence intervals
confint(ct)
coefci(fm)
## which in this simple case is equivalent to
confint(fm)
## extract further model information either from
## the original model or from the coeftest output
nobs(fm)
nobs(ct)
logLik(fm)
logLik(ct)
AIC(fm, ct)
BIC(fm, ct)
if(require("sandwich")) {
## a different covariance matrix can be also used:
(ct <- coeftest(fm, df = Inf, vcov = vcovHC))
## the corresponding confidence interval can be computed either as
confint(ct)
## or based on the original model
coefci(fm, df = Inf, vcov = vcovHC)
## note that the degrees of freedom _actually used_ can be extracted
df.residual(ct)
## which differ here from
df.residual(fm)
## vcov can also be supplied as a function with additional arguments
coeftest(fm, df = Inf, vcov = vcovHC, type = "HC0")
## or as a matrix
coeftest(fm, df = Inf, vcov = vcovHC(fm, type = "HC0"))
}