Use the model-estimated GP covariance parameters from gpdpgrow() to predict the GP function at future time points. Inputs the gpdpgrow object of estimated parameters.

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Description

A companion function to gpdpgrow

Usage

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## S3 method for class 'gpdpgrow'
predict_functions(object, J = 500, test_times,
  time_points = NULL, sn_order = NULL, ...)

Arguments

object

Object of class gpdpgrow returned from model run of gpdpgrow()

J

Scalar denoting number of draws to take from posterior predictive for each unit. Defaults to J = 500.

test_times

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.

time_points

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).

sn_order

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.

Value

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.

Note

Intended as a companion function for gpdpgrow for prediction

Author(s)

Terrance Savitsky tds151@gmail.com

See Also

gpdpgrow

Examples

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## Not run: 
library(growfunctions)
data(cps)
y_short   <- cps$y[,(cps$yr_label %in% c(2010:2013))]
t_train   <- ncol(y_short)
N         <- nrow(y_short)
t_test    <- 4
 
## Model Runs
res_gp              = gpdpgrow(y = y_short
                              n.iter = 50, 
                              n.burn = 25, 
                              n.thin = 1, 
                              n.tune = 0) 

## Prediction Model Runs
T_test             <- 4
T_yshort           <- ncol(y_short)
pred_gp            <- predict_functions( object = res_gp, 
                       test_times = (T_yshort+1):(T_yshort+T_test) )

## plot estimated and predicted functions
plot_gp         <- predict_plot(object = pred_gp, 
                               units_label = cps$st, 
                               single_unit = FALSE, 
                               credible = TRUE)  

## End(Not run)

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