car (version 0.8-4)

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=F, error.SS, error.df)

linear.hypothesis.glm(model, hypothesis.matrix, rhs=0, 
  summary.model=summary(model, corr = FALSE))

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.

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).

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

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