Default GAM count negative binomial model.

This function configures a very simple negative binomial count model and using:

Usage

gam_nb_model_fn(window, ..., knots_fn = ~gam_knots(.x, window, ...))

Arguments

window

controls the knot spacing in the GAM (if the default)

...

Named arguments passed on to gam_knots

data

the function will be called with incidence data - a dataframe with columns:

• count (positive_integer) - Positive case counts associated with the specified time frame

• time (ggoutbreak::time_period + group_unique) - A (usually complete) set of singular observations per unit time as a `time_period`

Any grouping allowed.

k

alternative to window, if k is given then the behaviour of the knots will be similar to the default mgcv::s(...,k=...) parameter.

...

currently not used

knots_fn

a function that takes the data as an input and returns a set of integers as time points for GAM knots, for s(time) term. The default here provides a roughly equally spaced grid determined by window, by a user supplied function could do anything. The input this function is the raw dataframe of data that will be considered for one model fit. It is guaranteed to have at least a time and count column. It is possible to

Value

a function suitable as the input for poisson_gam_model(model_fn = ...).

Details

count ~ s(time, bs = "cr", k = length(kts))

The control of knot positions is defined by the knots_fn and window parameters.

Examples

model_fn = gam_nb_model_fn(14)
fit = model_fn(test_poisson_rt %>% dplyr::ungroup())
summary(fit)
#> 
#> Family: Negative Binomial(56.499) 
#> Link function: log 
#> 
#> Formula:
#> count ~ s(time, bs = "cr", k = length(kts))
#> 
#> Parametric coefficients:
#>             Estimate Std. Error z value Pr(>|z|)    
#> (Intercept)  7.08122    0.01796   394.3   <2e-16 ***
#> ---
#> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#> 
#> Approximate significance of smooth terms:
#>           edf Ref.df Chi.sq p-value    
#> s(time) 6.812  6.986   8940  <2e-16 ***
#> ---
#> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#> 
#> R-sq.(adj) =  0.882   Deviance explained = 98.8%
#> -REML = 566.17  Scale est. = 1         n = 81