growfunctions (version 0.12)

informative_plot: Plot credible intervals for parameters to compare ignoring with weighting an informative sample

Description

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

Usage

informative_plot(objects = NULL, objects_labels = c("ignore", "weight"), map = NULL, units_name = NULL, model = "gp", true_star = NULL, map_true = NULL)

Arguments

objects

A list of objects, either all outputs from gpdpgrow(), or all from gmrfdpgrow(). objects includes a model estimated under ignoring the informativeness of the sampling design and another that employs weighting to account for the informativeness. An additional object may be added to represent a separate "iid" (or non-informative) sample from the same population, which will typically be available if the dataset was generated as synthetic data using function, "gen_informative_sample()".

objects_labels

A character vector of length equal to objects that provides labels for each entry in objects. Allowed entries in objects_labels are c("ignore","weight","iid"), where "ignore" denotes a model that ignores the informativeness, while "weight" denotes a model that employs sampling weights and "iid" denotes a model run on a non-informative, iid sample from the same population. Defaults to objects_labels = c("ignore","weight").

map

A list matrices, where each entry is produced from cluster_plot(object)$map. It is comprised of unit labels and cluster assignments for each object in objects. The length of map must be equal to the length of objects.

units_name

The label in each "map" matrix for the observation units. Will be the same as the units_name entry for the previously run, cluster_plot() function.

model

A scalar character input indicating the estimation model for all of the entries in objects. Allowable values for model are c("gp","gmrf"). Defaults to model = "gp".

true_star

An optional, P x M matrix, of true parameter location values, where P denotes the number of parameters per cluster and M denotes the number of clusters. For example, in model = "gp" with a single, rational quadratic covariance, P = 3 and if there are 3 clusters, then M = 3. For a model = "gmrf", with a single covariance, P = 1.

map_true

An optional data.frame object with n rows, the size of the informative sample used for c("ignore","weight") objects that maps the units_name to a true cluster. map_true must have 2 columns (and the rest are ignored), one must be named the same value as input for units_name. The second column must be named, cluster. If the true values derive from running gen_informative_sample() as the source of the true values, one may just input the map_obs data.frame that is listed in the object returned by gen_informative_sample().

Value

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

p.compare

A ggplot2 plot object

dat.compare

A data.frame object used to generate p.compare.

See Also

gpdpgrow, gmrfdpgrow

Examples

## Not run: 
# library(growfunctions)
# ## use gen_informative_sample() to generate an 
# ## N X T population drawn from a dependent GP
# ## By default, 3 clusters are used to generate 
# ## the population.
# ## A single stage stratified random sample of size n 
# ## is drawn from the population using I = 4 strata. 
# ## The resulting sample is informative in that the 
# ## distribution for this sample is
# ## different from the population from which 
# ## it was drawn because the strata inclusion
# ## probabilities are proportional to a feature 
# ## of the response, y (in the case, the variance.
# ## The stratified random sample over-samples 
# ## large variance strata).
# ## (The user may also select a 2-stage 
# ## sample with the first stage
# ## sampling "blocks" of the population and 
# ## the second stage sampling strata within blocks). 
# dat_sim        <- gen_informative_sample(N= 10000, 
#                                 n = 500, T = 5,
#                                 noise_to_signal = 0.1)
# 
# y_obs                       <- dat_sim$y_obs
# T                           <- ncol(y_obs)
# 
# an informative sampling design that inputs inclusion
# probabilities, ipr
# res_gp_w            <- gpdpgrow(y = y_obs, 
#                                ipr = dat_sim$map_obs$incl_prob, 
#                                n.iter = 5, n.burn = 2,  
#                                n.thin = 1, n.tune = 0)
# and fit vs. data for experimental units
# fit_plots_w         <- cluster_plot( object = res_gp_w,  
#                            units_name = "establishment", 
#                            units_label = dat_sim$map_obs$establishment, 
#                            single_unit = FALSE, credible = TRUE )
# 
# ## estimate parameters ignoring sampling design
# res_gp_i            <- gpdpgrow(y = y_obs, 
#                                n.iter = 5, n.burn = 2, 
#                                n.thin = 1, n.tune = 0)
# ## plots of estimated functions, faceted by cluster and fit vs. 
# ## data for experimental units
# fit_plots_i         <- cluster_plot( object = res_gp_i,  
#                                     units_name = "establishment", 
#                                     units_label = dat_sim$map_obs$establishment, 
#                                     single_unit = FALSE, credible = TRUE )
# 
# ## We also draw an iid (non-informative, exchangeable) 
# ## sample from the same population to 
# ## compare estimation results to our weighted 
# ## (w) and unweighted/ignoring (i) models
# 
# ## estimate parameters under an iid sampling design
# res_gp_iid          <- gpdpgrow(y = dat_sim$y_iid, 
#                                n.iter = 5, n.burn = 2,   
#                                n.thin = 1, n.tune = 0)
# ## plots of estimated functions, faceted by cluster and 
# ## fit vs. data for experimental units
# fit_plots_iid       <- cluster_plot( object = res_gp_iid,  
#                            units_name = "establishment", 
#                            units_label = dat_sim$map_iid$establishment, 
#                            single_unit = FALSE, credible = TRUE )
# 
# ## compare estimations of covariance parameter credible 
# ## intervals when ignoring informativeness vs.
# ## weighting to account for informativeness
# objects                  <- map <- vector("list",3)
# objects[[1]]             <- res_gp_i
# objects[[2]]             <- res_gp_iid
# objects[[3]]             <- res_gp_w
# map[[1]]                 <- fit_plots_i$map
# map[[2]]                 <- fit_plots_iid$map
# map[[3]]                 <- fit_plots_w$map
# objects_labels           <- c("ignore","iid","weight")
# 
# parms_plots_compare      <- informative_plot( objects = objects, 
#                                 objects_labels = objects_labels,
#                                 map = map, units_name = "establishment", 
#                                 model = "gp",
#                                 true_star = dat_sim$theta_star, 
#                                 map_true = dat_sim$map_obs)
# 
# ## End(Not run)