| 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, ...)
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 |
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. For illustrations see below.
The degrees of freedom df determine whether a normal
approximation is used or a t distribution with df degrees
of freedoms is used. The default method uses df.residual(x)
and if this is NULL 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.
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.
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)
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"))
}