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.
1 2 3 4 5 6 7 8 9 10 11 | 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)
|
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