| algo.farrington.fitGLM {surveillance} | R Documentation |
The function fits a Poisson regression model (GLM) with mean predictor
log mu_t = alpha + beta * w
as specified by the Farrington procedure. That way we are able to predict the value c0. 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)
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 |
Compute weights from an initial fit and rescale using
Anscombe based residuals as described in the
anscombe.residuals function.
An object of class GLM with additional fields wtime,
response and phi. If the glm returns without
convergence NULL is returned.