create_model_data.RdThis function generates a dataset containing all the necessary columns for fitting a Generalized Linear Model (GLM) in the context of epidemiological time series analysis. The generated dataset can include components for seasonality, time trends, and baseline adjustments, depending on the specified model type and intervention parameters.
create_model_data(
ts_len,
model = "mean",
time_trend = TRUE,
intervention_start = NULL,
min_timepoints_baseline = 12,
min_timepoints_trend = 12,
past_weeks_not_included = 4
)integer, specifying the length of the aggregated timeseries of case counts
character, default "mean" one of c("mean", "sincos", "FN") specifying which kind of model the glm is fitting. "mean" fits an intercept model, "sincos" a harmonic sincos model, "FN" uses the seasgroups from farrington to fit parameters for seasonality.
boolean, default TRUE, when TRUE a timetrend is fitted in the glm describing the expected number of cases.
integer, specifying the rownumber in the aggregated timeseries which corresponds to the intervention date.
integer, default 12, this parameter is only used when intervention_date is not NULL, specifying the number of weeks at least needed for fitting a new baseline after the intervention.
integer, default 12, this parameter is only used when intervention_date is not NULL, specifying the number of weeks at least needed for fitting a new timetrend after the intervention.
An integer specifying the number of past weeks to exclude from the fitting process. This can be useful for excluding recent data with outbreaks or data that may not be fully reported. Default is `4`.
data.frame containing all columns needed for the glm model. These are columns for the seasonality, time_trend and intercepts. This model data is used to fit the parameters for these coviariates.
if (FALSE) {
create_model_data(100)
create_model_data(100, intervention_start = 50)
}