predict_plot | R Documentation |
Uses as input the output object from the gpdpgrow.predict() and gmrfdpgrow.predict() methods.
predict_plot(
object = NULL,
units_label = NULL,
type_label = c("fitted", "predicted"),
time_points = NULL,
date_label = NULL,
x.axis.label = NULL,
y.axis.label = NULL,
single_unit = FALSE,
credible = TRUE
)
object |
A |
units_label |
A vector of labels to apply to experimental units with length equal to the number of
unique units. Defaults to sequential numeric values as input with data, |
type_label |
A character vector assigning a "fitted" or "predicted
label for the |
time_points |
A list input of length 2 with each entry containing a numeric vector
of times - one for the observed times for the set of "fitted" functions and the other denotes
time values at which "predicted" values were rendered for the functions. This input variable
only applies to |
date_label |
A vector of |
x.axis.label |
Text label for x-axis. Defaults to |
y.axis.label |
Text label for y-axis. Defaults to |
single_unit |
A scalar boolean indicating whether to plot the fitted vs data curve for
only a single experimental units (versus a random sample of 6).
Defaults to |
credible |
A scalar boolean indicating whether to plot 95 percent credible intervals for
estimated functions, |
A list object containing the plot of estimated functions, faceted by cluster,
and the associated data.frame
object.
p.cluster |
A |
dat.cluster |
A |
Terrance Savitsky tds151@gmail.com
gpdpgrow
, gmrfdpgrow
## Not run:
library(growfunctions)
data(cps)
y_short <- cps$y[,(cps$yr_label %in% c(2008:2013))]
t_train <- ncol(y_short)
N <- nrow(y_short)
t_test <- 4
## Model Runs
res_gmrf <- gmrfdpgrow(y = y_short,
q_order = c(2,4),
q_type = c("tr","sn"),
n.iter = 40,
n.burn = 20,
n.thin = 1)
res_gp <- gpdpgrow(y = y_short
n.iter = 10,
n.burn = 4,
n.thin = 1,
n.tune = 0)
## Prediction Model Runs
T_test <- 4
pred_gmrf <- predict_functions( object = res_gmrf,
J = 1000,
T_test = T_test )
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_gmrf <- predict_plot(object = pred_gmrf,
units_label = cps$st,
single_unit = TRUE,
credible = FALSE)
plot_gp <- predict_plot(object = pred_gp,
units_label = cps$st,
single_unit = FALSE,
credible = TRUE)
## End(Not run)
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