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

1 2 3 |

`objects` |
A list input where each element is a returned object
from estimation with either of |

`H` |
An |

`label.object` |
A character vector of length equal to |

`units_name` |
A character input that provides a label
for the set of |

`units_label` |
A vector of labels to apply to the observation units
with length equal to the
number of unique units. Defaults to sequential
numeric values as input with data, |

`date_field` |
A vector of |

`x.axis.label` |
Text label for x-axis. Defaults to |

`y.axis.label` |
Text label for y-axis. Defaults to |

A list object containing the plot of estimated functions, faceted by cluster,
and the associated `data.frame`

object.

`p.t` |
A |

`map` |
A |

Terrance Savitsky tds151@gmail.com

`gpdpgrow`

, `gmrfdpgrow`

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 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 | ```
{
library(growfunctions)
## load the monthly employment count data
## for a collection of
## U.S. states from the Current
## Population Survey (cps)
data(cps)
## subselect the columns of N x T, y,
## associated with
## the years 2009 - 2013
## to examine the state level
## employment levels
## during the "great recession"
y_short <- cps$y[,(cps$yr_label %in%
c(2010:2013))]
## run DP mixture of GP's to
## estimate posterior distributions
## for model parameters
## uses default setting of a
## single "rational quadratic"
## covariance formula
res_gp <- gpdpgrow(
y = y_short,
n.iter = 3,
n.burn = 1,
n.thin = 1,
n.tune = 0)
## 2 plots of estimated functions:
## 1. faceted by cluster and fit;
## 2. data for experimental units.
## for a group of randomly-selected
## functions
fit_plots_gp <- cluster_plot(
object = res_gp, units_name = "state",
units_label = cps$st, single_unit = FALSE,
credible = TRUE )
## Run the DP mixture of iGMRF's to
## estimate posterior
## distributions for model parameters
## Under default
## RW2(kappa) = order 2 trend
## precision term
res_gmrf <- gmrfdpgrow(y = y_short,
n.iter = 13,
n.burn = 4,
n.thin = 1)
## 2 plots of estimated functions:
## 1. faceted by cluster and fit;
## 2. data for experimental units.
## for a group of randomly-selected functions
fit_plots_gmrf <- cluster_plot( object = res_gmrf,
units_name = "state", units_label = cps$st,
single_unit = FALSE,
credible = TRUE )
## visual comparison of fit performance
## between gpdpgrow() and gmrfdpgrow()
## or any two objects returned from any
## combination of these estimation
## functions
objects <- vector("list",2)
objects[[1]] <- res_gmrf
objects[[2]] <- res_gp
label.object <- c("gmrf_tr2","gp_rq")
## the map data.frame object
## from fit_plots gp
## includes a field that
## identifies cluster assignments
## for each unit (or domain)
H <- fit_plots_gp$map$cluster
fit_plot_compare_facet <-
fit_compare( objects = objects,
H = H, label.object = label.object,
y.axis.label = "normalized y",
units_name = "state", units_label = cps$st)
}
``` |

Questions? Problems? Suggestions? Tweet to @rdrrHQ or email at ian@mutexlabs.com.

All documentation is copyright its authors; we didn't write any of that.