Returns a vector of p-values from testing whether each estimated coefficient of a VGLM regression model is 0 or not. The methodology is based on a likelihood ratio test.
lrp(object, ...) lrp.vglm(object, which = NULL, omit1s = TRUE, trace = NULL, ...)
Numeric or character. Specifies which regression coefficient are to be selected. The default is to select them all, except the intercepts.
TRUE then some output is produced as
each regression coefficient is deleted (set to 0) and the
IRLS iterations proceed. The default is to use the
value of the fitted object;
further arguments passed into the other methods functions.
By default, a vector of (2-sided test) p-values.
If the model is intercept-only then a
NULL is returned
This function has not yet been thoroughly tested.
Convergence failure is possible for some models applied to
certain data sets; it is a good idea to set
trace = TRUE
to monitor convergence.
summary() is applied to a
a Wald table is produced.
The corresponding p-values are generally viewed as inferior to
those from a likelihood ratio test (LRT).
For example, the Hauck and Donner (1977) effect (HDE) produces
p-values that are biased upwards (see
Other reasons are that the Wald test is often less accurate
(especially in small samples) and is not invariant to
This function returns p-values based on the LRT by
deleting one column at a time from the big VLM matrix
and then starting up IRLS to convergence (hopefully).
Twice the difference between the log-likelihoods
(or equivalently, the difference in the deviances if they are defined)
is asymptotically chi-squared with 1 degree of freedom.
One might expect the p-values from this function
therefore to be more accurate
and not suffer from the HDE.
Thus this function is an alternative to
for testing for the significance of a regression coefficient.