coeftest | R Documentation |

`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, ...) ## Default S3 method: coeftest(x, vcov. = NULL, df = NULL, ..., save = FALSE) 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 |

`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. |

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.

`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.

`lm`

, `waldtest`

## 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")) }

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