algo.farrington.fitGLM(response, wtime, timeTrend = TRUE,
reweight = TRUE, ...)
algo.farrington.fitGLM.fast(response, wtime, timeTrend = TRUE,
reweight = TRUE, ...)
algo.farrington.fitGLM.populationOffset(response, wtime, population,
timeTrend=TRUE,reweight=TRUE, ...)
response
algo.farrington.fitGLM.populationOffset
the value
log(population)
is used as offset in the linear
predictor of the GLM:
$$\log \mu_t = \log(\texttt{populatwtime
,
response
and phi
. If the glm
returns without
convergence NULL
is returned.anscombe.residuals
function.
Note that algo.farrington.fitGLM
uses the glm
routine
for fitting. A faster alternative is provided by
algo.farrington.fitGLM.fast
which uses the glm.fit
function directly (thanks to Mikko Virtanen). This saves
computational overhead and increases speed for 500 monitored time
points by a factor of approximately two. However, some of the
routine glm
functions might not work on the output of this
function. Which function is used for algo.farrington
can be
controlled by the control$fitFun
argument.anscombe.residuals
,algo.farrington