# Side-by-side plot panels that compare latent function values to data for different estimation models Uses as input the output object from the gpdpgrow() and gmrfdpgrow() functions. ```r fit_compare( objects, H = NULL, label.object = c("gp_rq", "gmrf_rw2"), units_name = "Observation_Unit", units_label = NULL, date_field = NULL, x.axis.label = NULL, y.axis.label = NULL ) ``` ## Arguments - `objects`: A list input where each element is a returned object from estimation with either of `gpdpgrow` or `gmrfdpgrow` or an object that contains true `N x T` matrix of true latent function values, `f`. This latter input is only needed if want to compare estimated to true latent function values. - `H`: An `N x 1` with entries in `1,...,M` of cluster assignments for the `N` units of `y` under a known clustering. - `label.object`: A character vector of length equal to `objects` that contains labels for each element of `objects` to be used in rendering comparison plots. Defaults to `label.object = c("gp_rq","gmrf_rw2")`. - `units_name`: A character input that provides a label for the set of `N` observation units. Defaults to `units_name = "Observation_Unit"`. - `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, `y`. - `date_field`: 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"`. ## Returns A list object containing the plot of estimated functions, faceted by cluster, and the associated `data.frame` object. - **p.t**: A `ggplot2` plot object - **map**: A `data.frame` object that contains clustering structure of observation units. ## Examples ```r { 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) } ``` ## See Also `gpdpgrow`, `gmrfdpgrow` ## Author(s) Terrance Savitsky tds151@gmail.com

fit_compare function