gpdpgrow object of estimated parameters.A companion function to gpdpgrow
# S3 method for gpdpgrow
predict_functions(
object,
J = 500,
test_times,
time_points = NULL,
sn_order = NULL,
...
)out A list object containing containing two matrices; the first is a K x (N*T) matrix of predicted function values for each of K sampled iterations. N is slow index and denotes the number of experimental units. The second matrix is an N x T average over the K sampled draws, composed in Rao-Blackwellized fashion.
Object of class gpdpgrow returned from model run of gpdpgrow()
Scalar denoting number of draws to take from posterior predictive for each unit.
Defaults to J = 500.
A numeric vector holding test times at which to predict GP function values
Will use the estimated covariance parameters from the training data to predict
functions at the test_times for the N observation units.
Inputs a vector of common time points at which the collections of functions were
observed (with the possibility of intermittent missingness). The length of time_points
should be equal to the number of columns in the data matrix, y. Defaults to
time_points = 1:ncol(y).
An integer vector of length, L_s, equal to the number of seasonal terms.
Conveys the order of the seasonality for each term on the scale of T; for example,
if T is dimensioned in months, and one wishes to model quarterly seasonality, then
the applicable seasonality term would be of order 3.
further arguments passed to or from other methods.
Terrance Savitsky tds151@gmail.com
gpdpgrow