train_ppgrid: Train prior period information.
In DexGroves/ppR: Make prior period summary variables from longitudinal data.

Description Usage Arguments Value Examples

Produces a grid of weighted mean and total weight for the preceding period of each group-time combination. All parameters are taken as vectors that are assumed to be associated and in the same order. train_ppgrid will compute ~ granularity * (max(time_var) - min(time_var) - window_size) distinct aggregations for each distinct id_var.

1 2	train_ppgrid(id_var, time_var, response_var, wt_var = rep(1, length(response_var)), lag, window_size, granularity)

`id_var`	Vector identifying the groups.
`time_var`	Numeric vector of time, there is no support for character time vectors or other datetime datatypes yet.
`response_var`	Numeric vector of responses to summarise.
`wt_var`	Optional numeric weight vector. Defaults to 1.0.
`lag`	Number of units of time to lag the variable accumulation before scoring. Useful if there is a period after data acquisition before the response is known; this parameter can mimic model implementation.
`window_size`	Number of units of time to aggregate the response and weight variables over.
`granularity`	How severely to round the input time variable. Times will be rounded to the nearest granularity. Improves runtime at nominal cost to accuracy.

ppgrid object, containing a data.table containing prior period information as well as some input parameters.

set.seed(1234)
ppdf <- make_longitudinal_data(1000)

ppgrid <- train_ppgrid(ppdf$id,
                       ppdf$date,
                       ppdf$resp,
                       lag = 25,
                       window_size = 25,
                       granularity = 25)

print(ppgrid)