# linear.hypothesis

From car v1.0-18
by John Fox

##### Test Linear Hypothesis

Generic function for testing a linear hypothesis, and methods for fitted linear or generalized linear models.

- Keywords
- models, regression, htest

##### Usage

```
linear.hypothesis(model, ...)
lht(...)
## S3 method for class 'lm':
linear.hypothesis(model, hypothesis.matrix, rhs=0,
summary.model=summary(model, corr = FALSE),
test=c("F", "Chisq"), vcov=NULL,
white.adjust=FALSE, error.SS, error.df, ...)
## S3 method for class 'glm':
linear.hypothesis(model, hypothesis.matrix, rhs=0,
summary.model=summary(model, corr = FALSE),
test=c("Chisq", "F"), vcov=NULL, error.df, ...)
```

##### Arguments

- model
- model object produced by
`lm`

or`glm`

. - hypothesis.matrix
- matrix (or vector) giving linear combinations of coefficients by rows.
- rhs
- right-hand-side vector for hypothesis, with as many entries as
rows in
`hypothesis.matrix`

. - summary.model
- a
`summary`

object for the model; usually specified only when`linear.hypothesis`

is called from another function that has already computed the summary. - test
- character specifying wether to compute the finite sample F statistic (with approximate F distribution) or the large sample Chi-squared statistic (with asymptotic Chi-squared distribution).
- 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`

. - white.adjust
- logical or character. Convenience interface to
`hccm`

(instead of using the argument`vcov`

). Can be set either to a character specifying the`type`

argument of`hccm`

- error.SS
- error sum of squares for the hypothesis; if not specified, will be
taken from
`summary.model`

. - error.df
- error degrees of freedom for the hypothesis; if not specified,
will be taken from
`summary.model`

. - ...
- aruments to pass down.

##### Details

Computes either a finite sample F statistic (default for `"lm"`

objects)
or asymptotic Chi-squared statistic (default for `"glm"`

objects) for
carrying out a Wald-test-based comparison between a model and a linearly
restricted model.

##### Value

- 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.

##### References

Fox, J. (1997)
*Applied Regression, Linear Models, and Related Methods.* Sage.

##### See Also

##### Examples

```
data(Davis)
mod<-lm(weight~repwt, data=Davis)
linear.hypothesis(mod, diag(2), c(0,1))
## use asymptotic Chi-squared statistic
linear.hypothesis(mod, diag(2), c(0,1), test = "Chisq")
## use HC3 standard errors via
## white.adjust option
linear.hypothesis(mod, diag(2), c(0,1), white.adjust = TRUE)
## covariance matrix *function*
linear.hypothesis(mod, diag(2), c(0,1), vcov = hccm)
## covariance matrix *estimate*
linear.hypothesis(mod, diag(2), c(0,1), vcov = hccm(mod, type = "hc3"))
```

*Documentation reproduced from package car, version 1.0-18, License: GPL version 2 or newer*

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