| linear.hypothesis {car} | R Documentation |
Generic function for testing a linear hypothesis, and methods for fitted linear or generalized linear models.
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, ...)
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 or TRUE,
then "hc3" is used implicitly. |
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. |
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.
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.
John Fox jfox@mcmaster.ca and Achim Zeleis
Fox, J. (1997) Applied Regression, Linear Models, and Related Methods. Sage.
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"))