A companion function to gpdpgrow
1 2 3  ## S3 method for class 'gpdpgrow'
predict_functions(object, J = 500, test_times,
time_points = NULL, sn_order = NULL, ...)

object 
Object of class 
J 
Scalar denoting number of draws to take from posterior predictive for each unit.
Defaults to 
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 
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 
sn_order 
An integer vector of length, 
... 
further arguments passed to or from other methods. 
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 RaoBlackwellized fashion.
Intended as a companion function for gpdpgrow
for prediction
Terrance Savitsky tds151@gmail.com
gpdpgrow
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28  ## 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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