BICvlm {VGAM} | R Documentation |
Bayesian Information Criterion
Description
Calculates the Bayesian information criterion (BIC) for a fitted model object for which a log-likelihood value has been obtained.
Usage
BICvlm(object, ..., k = log(nobs(object)))
Arguments
object , ... |
Same as |
k |
Numeric, the penalty per parameter to be used;
the default is |
Details
The so-called BIC or SBC (Schwarz's Bayesian criterion)
can be computed by calling AICvlm
with a
different k
argument.
See AICvlm
for information and caveats.
Value
Returns a numeric value with the corresponding BIC, or ...,
depending on k
.
Warning
Like AICvlm
, this code has not been double-checked.
The general applicability of BIC
for the VGLM/VGAM classes
has not been developed fully.
In particular, BIC
should not be run on some VGAM family
functions because of violation of certain regularity conditions, etc.
Many VGAM family functions such as
cumulative
can have the number of
observations absorbed into the prior weights argument
(e.g., weights
in vglm
), either
before or after fitting. Almost all VGAM family
functions can have the number of observations defined by
the weights
argument, e.g., as an observed frequency.
BIC
simply uses the number of rows of the model matrix, say,
as defining n
, hence the user must be very careful
of this possible error.
Use at your own risk!!
Note
BIC, AIC and other ICs can have have many additive constants added to them. The important thing are the differences since the minimum value corresponds to the best model.
BIC has not been defined for QRR-VGLMs yet.
Author(s)
T. W. Yee.
See Also
AICvlm
,
VGLMs are described in vglm-class
;
VGAMs are described in vgam-class
;
RR-VGLMs are described in rrvglm-class
;
BIC
,
AIC
.
Examples
pneumo <- transform(pneumo, let = log(exposure.time))
(fit1 <- vglm(cbind(normal, mild, severe) ~ let,
cumulative(parallel = TRUE, reverse = TRUE), data = pneumo))
coef(fit1, matrix = TRUE)
BIC(fit1)
(fit2 <- vglm(cbind(normal, mild, severe) ~ let,
cumulative(parallel = FALSE, reverse = TRUE), data = pneumo))
coef(fit2, matrix = TRUE)
BIC(fit2)