## ----include = FALSE---------------------------------------------------------- knitr::opts_chunk$set( collapse = TRUE, comment = "#>", warning = FALSE, message = FALSE, fig.width = 8, fig.height = 5 ) library(ggplot2) library(dplyr) library(knitr) library(kableExtra) pair_results <- readRDS("data/pair_results.rds") plot_df2 <- readRDS("data/plot_df2.rds") table_diag <- readRDS("data/table_diag.rds") section_sizes <- readRDS("data/section_sizes.rds") diag_table <- readRDS("data/diag_table.rds") plot_df_ess <- readRDS("data/plot_df_ess.rds") ## ----setup, include=FALSE----------------------------------------------------- library(EcoEnsemble) ## ----eval=F------------------------------------------------------------------- # #generate priors # priors <- EnsemblePrior(4) # # #run the model # fit_sample <- fit_ensemble_model(observations = list(SSB_obs, Sigma_obs), # simulators = list(list(SSB_ewe, Sigma_ewe, "EwE"), # list(SSB_lm, Sigma_lm, "LeMans"), # list(SSB_miz, Sigma_miz, "mizer"), # list(SSB_fs, Sigma_fs, "FishSUMS")), # priors = priors, # sampler = "kalman") ## ----echo=F, out.width = "75%",fig.align ='center'---------------------------- # ggplot2::ggplot(plot_df_ess %>% # dplyr::filter(setup == "Model + hierarchical") %>% # dplyr::mutate(sampler = dplyr::if_else(sampler == "E", "Explicit", sampler))%>%mutate(sampler = dplyr::if_else(sampler == "K", "Kalman", sampler)) # , ggplot2::aes(x = setup, y = value, fill = sampler)) + # ggplot2::geom_boxplot( # position = ggplot2::position_dodge(width = 0.72), # width = 0.58, # outlier.shape = NA # ) + # ggplot2::facet_wrap(metric ~ ., scales = "free_y", nrow=1) + # ggplot2::labs( # x = NULL, # y = NULL, # fill = "Sampler" # ) + # ggplot2::theme_bw(base_size = 11) + # ggplot2::theme( # legend.position = "top", # strip.placement = "outside", # strip.background = ggplot2::element_blank(), # panel.spacing.y = grid::unit(0.15, "lines"), # plot.margin = grid::unit(c(0.3, 0.4, 0.2, 0.2), "cm") # )+ ggplot2::theme( # axis.title.x = ggplot2::element_blank(), # axis.text.x = ggplot2::element_blank(), # axis.ticks.x = ggplot2::element_blank() # ) knitr::include_graphics("data/explicit_plot1.png") ## ----echo=F, out.width = "85%",fig.align ='center'---------------------------- knitr::include_graphics("data/explicit_plot4.png") ## ----echo=F------------------------------------------------------------------- table_diag %>% select(-section) %>% mutate(across(where(is.numeric), ~ formatC(., format = "fg", digits = 3))) %>% kbl( booktabs = TRUE, align = c("l", rep("r", 8)), col.names = c("Diagnostic", "E", "K", "E", "K", "E", "K", "E", "K") ) %>% add_header_above(c( " " = 1, "Drivers" = 2, "Model" = 2, "Model + hierarchical" = 2, "Drivers + hierarchical" = 2 )) %>% kable_styling( full_width = FALSE, font_size = 10, latex_options = "hold_position" ) %>% row_spec(0, bold = TRUE) %>% pack_rows("Run time", 1, section_sizes["Run time"]) %>% pack_rows( "Convergence", section_sizes["Run time"] + 1, section_sizes["Run time"] + section_sizes["Convergence"] ) %>% pack_rows( "Bulk diagnostics", section_sizes["Run time"] + section_sizes["Convergence"] + 1, section_sizes["Run time"] + section_sizes["Convergence"] + section_sizes["Bulk diagnostics"] ) %>% pack_rows( "Tail diagnostics", section_sizes["Run time"] + section_sizes["Convergence"] + section_sizes["Bulk diagnostics"] + 1, sum(section_sizes) ) %>% column_spec(1, width = "4.2cm") %>% column_spec(2:9, width = "1.5cm") ## ----echo=F------------------------------------------------------------------- diag_table %>% mutate(across(where(is.numeric), ~ formatC(., format = "fg", digits = 3))) %>% knitr::kable( booktabs = TRUE, align = c("l", rep("r", 8)), col.names = c("Diagnostic", "E", "K", "E", "K", "E", "K", "E", "K") ) %>% kableExtra::add_header_above(c( " " = 1, "Drivers" = 2, "Model" = 2, "Model + hierarchical" = 2, "Drivers + hierarchical" = 2 )) %>% kableExtra::kable_styling( full_width = FALSE, font_size = 10, latex_options = "hold_position" ) %>% kableExtra::column_spec(1, width = "4.2cm") %>% kableExtra::column_spec(2:9, width = "1.5cm")