car (version 1.0-8)

linear.hypothesis: Test Linear Hypothesis

Description

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

Usage

linear.hypothesis(model, ...)

lht(...)

linear.hypothesis.lm(model, hypothesis.matrix, rhs=0, 
  summary.model=summary(model, corr = FALSE), 
  white.adjust=FALSE, error.SS, error.df, ...)

linear.hypothesis.glm(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.

Value

  • Returns an F.test or chisq.test object, with components:
  • SSHsum of squares for hypothesis (for a linear model).
  • SSEerror sum of squares (for a linear model).
  • fF-statistic for the hypothesis (for a linear model.)
  • Dfdegrees of freedom for F or chisquare.
  • pp-value for the hypothesis.
  • ChiSquarechisquare statistic for the hypothesis (for a generalized linear model).

Details

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

References

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

See Also

anova, Anova, hccm

Examples

Run this code
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

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