# Plot credible intervals for parameters to compare ignoring with weighting an informative sample Uses as input the output object from the gpdpgrow() and gmrfdpgrow() functions. ```r 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()`. ## Returns 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`. ## Examples ```r ## 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) ``` ## See Also `gpdpgrow`, `gmrfdpgrow` ## Author(s) Terrance Savitsky tds151@gmail.com

informative_plot function