growfunctions (version 0.11)

predict_plot: Plot estimated functions both at estimated and predicted time points with 95% credible intervals.

Description

Uses as input the output object from the gpdpgrow.predict() and gmrfdpgrow.predict() methods.

Usage

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)

Arguments

object

A gpdpgrow.predict or gmrfdpgrow.predict object, obtained from predict_functions(object,...).

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, y.

type_label

A character vector assigning a "fitted" or "predicted label for the time_points input. Defaults to type_label = c("fitted","predicted").

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 vari

date_label

A vector of Date values for labeling the x-axis tick marks. Defaults to 1:T .

x.axis.label

Text label for x-axis. Defaults to "time".

y.axis.label

Text label for y-axis. Defaults to "function values".

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 FALSE.

credible

A scalar boolean indicating whether to plot 95 percent credible intervals for estimated functions, bb, when plotting fitted functions versus data. Defaults to credible = TRUE .

Value

• A list object containing the plot of estimated functions, faceted by cluster, and the associated data.frame object.
• p.cluster
  A ggplot2 plot object
• dat.cluster
  A data.frame object used to generate p.cluster.

See Also

gpdpgrow, gmrfdpgrow

Examples

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)