Directly specify estimated model parameters and their covariance matrix.
Usage
parm(coef, vcov, df = 0)
Arguments
coef
estimated coefficients.
vcov
estimated covariance matrix of the coefficients.
df
an optional specification of the degrees of freedom to be used in
subsequent computations.
Details
When only estimated model parameters and the corresponding
covariance matrix is available for simultaneous inference
using glht (for example, when only the results
but not the original data are available or, even worse, when the model
has been fitted outside R), function parm sets up an
object glht is able to compute on (mainly
by offering coef and vcov methods).
Note that the linear function in glht can't
be specified via mcp since the model terms
are missing.
Value
An object of class parm with elements
coef
model parameters
vcov
covariance matrix of model parameters
df
degrees of freedom
Examples
## example from
## Bretz, Hothorn, and Westfall (2002).
## On multiple comparisons in R. R News, 2(3):14-17.
beta <- c(V1 = 14.8, V2 = 12.6667, V3 = 7.3333, V4 = 13.1333)
Sigma <- 6.7099 * (diag(1 / c(20, 3, 3, 15)))
confint(glht(model = parm(beta, Sigma, 37),
linfct = c("V2 - V1 >= 0",
"V3 - V1 >= 0",
"V4 - V1 >= 0")),
level = 0.9)