Fit Poisson GLM of the Farrington procedure for a single time point
algo.farrington.fitGLM.Rd
The function fits a Poisson regression model (GLM) with mean predictor $$\log \mu_t = \alpha + \beta t$$ as specified by the Farrington procedure. If requested, Anscombe residuals are computed based on an initial fit and a 2nd fit is made using weights, where base counts suspected to be caused by earlier outbreaks are downweighted.
Usage
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, ...)
Arguments
- response
The vector of observed base counts
- wtime
Vector of week numbers corresponding to
response
- timeTrend
Boolean whether to fit the \(\beta t\) or not
- reweight
Fit twice – 2nd time with Anscombe residuals
- population
Population size. Possibly used as offset, i.e. in
algo.farrington.fitGLM.populationOffset
the valuelog(population)
is used as offset in the linear predictor of the GLM: $$\log \mu_t = \log(\texttt{population}) + \alpha + \beta t$$ This provides a way to adjust the Farrington procedure to the case of greatly varying populations. Note: This is an experimental implementation with methodology not covered by the original paper.- ...
Used to catch additional arguments, currently not used.
Details
Compute weights from an initial fit and rescale using
Anscombe based residuals as described in the
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.
Value
an object of class GLM with additional fields wtime
,
response
and phi
. If the glm
returns without
convergence NULL
is returned.