linear.hypothesis {car}R Documentation

Test Linear Hypothesis

Description

Test a linear hypothesis for a linear or generalized linear model.

Usage

linear.hypothesis(model, ...)

lht(...)

## S3 method for class 'lm':
linear.hypothesis(model, hypothesis.matrix, rhs=0, 
  summary.model=summary(model, corr = FALSE), 
  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), ...)
  
## S3 method for class 'chisq.test':
print(x, ...)

## S3 method for class 'F.test':
print(x, ...)

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.
white.adjust if TRUE use heteroscedasticity-corrected covariance matrix.
error.SS error sum of squares for the hypothesis; if not specified, will be taken from model.
error.df error degrees of freedom for the hypothesis; if not specified, will be taken from model.
x chisq.test or F.test object.
... aruments to pass down.

Details

Computes an F-test for the hypothesis in a linear model, or a Wald test in a generalized linear model.

Value

Returns an F.test or chisq.test object, with components:

SSH sum of squares for hypothesis (for a linear model).
SSE error sum of squares (for a linear model).
f F-statistic for the hypothesis (for a linear model.)
Df degrees of freedom for F or chisquare.
p p-value for the hypothesis.
ChiSquare chisquare statistic for the hypothesis (for a generalized linear model).

Author(s)

John Fox jfox@mcmaster.ca

References

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

See Also

anova, Anova, hccm

Examples

data(Davis)
mod<-lm(weight~repwt, data=Davis)
linear.hypothesis(mod, diag(2), c(0,1))
## F-Test 
## SS = 245.9738     SSE = 12828.03     F = 1.735312  Df = 2 and 181     p = 0.179266 

[Package car version 1.0-14 Index]