ll.call {gemtc} | R Documentation |
Call a likelihood/link-specific function
Description
GeMTC implements various likelihood/link combinations.
Functionality specific to the likelihood/link is handled by methods with names ending in
.<likelihood>.<link>
.
This convenience function calls such methods.
Usage
ll.call(fnName, model, ...)
Arguments
fnName |
The name of the function to call. See details for available functions. |
model |
An object of S3 class |
... |
Additional arguments to be passed to the function. |
Details
The following methods currently need to be implemented to implement a likelihood/link:
mtc.arm.mle
: calculates a (corrected) maximum likelihood estimate for an arm-level effect. Used to generate starting values.mtc.rel.mle
: calculates a (corrected) maximum likelihood estimate for a relative effect. Used to generate starting values.mtc.code.likelihood
: generates JAGS code implementing the likelihood.scale.log
: returns TRUE if plots should use the log scale.scale.name
: returns the user-facing name of the outcome metric.scale.limit.inits
: returns an upper and lower bound for the initial values, because some initial values might trigger boundary conditions such as probability 0 or 1 for the binomial.required.columns.ab
: returns the required columns for arm-based data.
The first two methods can now also be used to selectively apply continuity corrections in case the maximum likelihood estimates are used for other purposes. mtc.arm.mle
has an additional k=0.5
argument to specify the correction factor. mtc.rel.mle
has arguments correction.force=TRUE
to force application of the continuity correction even if unnecessary, correction.type="constant"
to specify the type of correction (specify "reciprocal"
) for a correction proportional to the reciprocal of the size of the other arm, and correction.magnitude=1
to specify the (total) magnitude of the correction. These corrections apply only for count data, and will be ignored for continuous likelihood/links.
Value
The return value of the called function.
Author(s)
Gert van Valkenhoef
See Also
Examples
# The "model" may be a stub.
model <- list(likelihood="poisson", link="log")
ll.call("scale.name", model)
# "Hazard Ratio"
ll.call("mtc.arm.mle", model, c('responders'=12, 'exposure'=80))
# mean sd
#-1.8562980 0.1118034