Fit Poisson GLM of the Farrington procedure for a single time point
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.
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 |
The vector of observed base counts |
wtime |
Vector of week numbers corresponding to |
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
log mu_t = log(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. |
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.
an object of class GLM with additional fields wtime,
response and phi. If the glm returns without
convergence NULL is returned.
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