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"))