This function make a prediction base on a dataframe and a list made of the intercept and the regression curve. The input curve_type in needed to select the good way of evaluate the integrals \(\int_0^T delta(t) X(t) dt\).
Usage
smoothPLS_predict_uni(
df_predict,
delta_list,
curve_type = NULL,
int_mode = 1,
id_col = "id",
time_col = "time",
nb_pt = 10,
subdivisions = 100,
regul_time = seq(delta_list[[2]]$basis$rangeval[1], delta_list[[2]]$rangeval[2], 1),
parallel = TRUE
)Arguments
- df_predict
a dataframe ('id', 'time', 'state or value') to predict from
- delta_list
a list of delta_spls : list(intercept, delta_fd)
- curve_type
a character, 'cat' for Categorical FD, 'num' for Scalar FD
- int_mode
a value of the integration mode, default 1
- id_col
a character for the id column, default 'id'
- time_col
a character for the time column, default 'time'
- nb_pt
number of points for the integration, default value : 10
- subdivisions
default parameter of R function integrate; default value : 100
- regul_time
a vector of time regularization values default delta_fd basis rangeval per 1, NEEDED for curve_type = 'num'!
- parallel
a boolean to use parallelization, default TRUE