`coeftest`

is a generic function for performing
z and (quasi-)t Wald tests of estimated coefficients.
`coefci`

computes the corresponding Wald confidence
intervals.

`coeftest(x, vcov. = NULL, df = NULL, …)`coefci(x, parm = NULL, level = 0.95, vcov. = NULL, df = NULL, …)

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 `x`

.

df

the degrees of freedom to be used. If this
is a finite positive number a t test with `df`

degrees of freedom is performed. In all other cases,
a z test (using a normal approximation) is performed.
By default it tries to use `x$df.residual`

and performs a z test if this is `NULL`

.

…

further arguments passed to the methods
and to `vcov.`

in the default method.

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.

`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.

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.

# NOT RUN { ## 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")) } # }

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