From fcd790baf0df8b592fb18dedc87593e8e317d656 Mon Sep 17 00:00:00 2001 From: saraloo <45245630+saraloo@users.noreply.github.com> Date: Mon, 2 Oct 2023 10:32:37 -0400 Subject: [PATCH] fix output notebook tabs --- postprocessing/model_output_notebook.Rmd | 94 +++++++++++++++--------- 1 file changed, 60 insertions(+), 34 deletions(-) diff --git a/postprocessing/model_output_notebook.Rmd b/postprocessing/model_output_notebook.Rmd index 4ed788dcb..c5ce8189a 100644 --- a/postprocessing/model_output_notebook.Rmd +++ b/postprocessing/model_output_notebook.Rmd @@ -110,7 +110,7 @@ import_model_outputs <- config <- flepicommon::load_config(opt$config) res_dir <- file.path(opt$results_path, config$model_output_dirname) -print(res_dir) +# print(res_dir) ``` @@ -146,13 +146,14 @@ theme_small <- ) ``` -📸 -Here is a snapshot of your model outputs for run ID `r opt$run_id`, from config `r opt$config`, stored in `r opt$results_path`. +Here is a snapshot 📸 of your model outputs for run ID `r opt$run_id`, from config `r opt$config`, stored in `r opt$results_path`. + # Infection model: SEIR model output +These are the SEIR outputs for your infection model, showing infection states (aggregated across other strata). -```{r seir, cache = TRUE, fig.dim = c(8, 20), results='hide',fig.keep='all'} +```{r seir, cache = TRUE, fig.dim = c(10, 20), results='hide',fig.keep='all'} # read in model outputs seir_outputs_global <- setDT(import_model_outputs(res_dir, "seir", 'global', 'final')) @@ -193,8 +194,9 @@ print( # Infection model: SNPI model output +Here are the snpi parameters for your model. If inference is run, parameters are coloured by their likelihoods in a given subpopulation. -```{r snpi, cache = TRUE, fig.dim = c(8,20), results='hide',fig.keep='all'} +```{r snpi, cache = TRUE, fig.dim = c(10,20), results='hide',fig.keep='all'} # read in model outputs snpi_outputs_global <- setDT(import_model_outputs(res_dir, "snpi", 'global', 'final')) node_names <- unique(snpi_outputs_global %>% .[ , get(config$spatial_setup$nodenames)]) @@ -224,8 +226,9 @@ snpi_plots <- lapply(node_names, } + theme_bw(base_size = 10) + theme(axis.text.x = element_text(angle = 60, hjust = 1, size = 6), - text = element_text(size = 8)) + - guides(color = guide_legend(override.aes = list(size = 0.5)))+ + text = element_text(size = 8), + legend.key.size = unit(0.2, "cm")) + + # guides(color = guide_legend(override.aes = list(size = 0.5)))+ scale_color_viridis_c(option = "B", name = "log\nlikelihood") + labs(x = "parameter", title = i) + theme_small @@ -252,8 +255,8 @@ snpi_plots <- lapply(node_names, } + theme_bw(base_size = 10) + theme(axis.text.x = element_text(angle = 60, hjust = 1, size = 6), - text = element_text(size = 8)) + - guides(color = guide_legend(override.aes = list(size = 0.5)))+ + text = element_text(size = 8), + legend.key.size = unit(0.2, "cm")) + scale_color_viridis_c(option = "B", name = "log\nlikelihood") + labs(x = "parameter") + theme_small } @@ -265,15 +268,15 @@ print(do.call("grid.arrange", c(snpi_plots, ncol=4))) ``` -#Outcome model: HOSP model output +# Outcome model: HOSP model output +Here are the results from your outcomes model. If you ran more than one simulation, here's a randomly sampled simulation, and if you ran more, here are the quantiles of all your simulations. -## Daily hosp {.tabset} -### Single trajectories {.tabset} -```{r hosp_daily_single_slot, results='asis', cache = TRUE, fig.dim = c(8,8)} +## Daily hosp single trajectories {.tabset} +```{r hosp_daily_single_slot, results='asis', cache = TRUE, fig.dim = c(10,10)} ## add something so that if it doesn't exist, it prints some 'no output' message # get all outcome variables @@ -300,8 +303,10 @@ cat("\n\n") ## plot ONE sample trajectory for sanity check (can modify) for(i in 1:length(outcome_vars_)){ - cat(paste0("#### ",outcome_vars_[i]," \n")) + cat(paste0("### ",outcome_vars_[i]," {.tabset} \n")) + cat(paste0("#### Incident \n")) + ## Incident print( hosp_outputs_global %>% @@ -324,6 +329,10 @@ for(i in 1:length(outcome_vars_)){ theme_classic() + theme_small ) + cat("\n\n") + + cat(paste0("#### Cumulative \n")) + ## Cumulative print( hosp_outputs_global %>% @@ -353,8 +362,8 @@ for(i in 1:length(outcome_vars_)){ ``` -### Quantiles {.tabset} -```{r hosp_daily_quantiles, results='asis', cache = TRUE, fig.dim = c(8,8)} +## Quantiles {.tabset} +```{r hosp_daily_quantiles, results='asis', cache = TRUE, fig.dim = c(10,10)} # ```{r hosp_daily_quantiles, fig.dim = c(8,8), results='hide',fig.keep='all'} if(length(unique(hosp_outputs_global$slot)) > 1){ @@ -364,10 +373,11 @@ if(length(unique(hosp_outputs_global$slot)) > 1){ ## plot quantiles (if more than one slot) for(i in 1:length(outcome_vars_)){ - cat(paste0("#### ",outcome_vars_[i]," \n")) + cat(paste0("### ",outcome_vars_[i]," {.tabset} \n")) ## plot quantiles (if more than one slot) # for(i in 1:length(outcome_vars_)){ + cat(paste0("#### Incident \n")) # incident print( hosp_outputs_global %>% @@ -391,6 +401,10 @@ if(length(unique(hosp_outputs_global$slot)) > 1){ theme_classic()+ theme_small ) + cat("\n\n") + + cat(paste0("#### Cumulative \n")) + # cumulative print( hosp_outputs_global %>% @@ -416,8 +430,9 @@ if(length(unique(hosp_outputs_global$slot)) > 1){ theme_classic() + theme_small ) + cat("\n\n") + } - cat("\n\n") } @@ -427,8 +442,9 @@ if(length(unique(hosp_outputs_global$slot)) > 1){ # Outcome model: HNPI model output +This shows the parameters associated with your outcomes model, for all subpopulations. If inference is run, points are coloured by the associated likelihoods. -```{r hnpi, cache = TRUE, fig.dim = c(8,20), results='hide',fig.keep='all'} +```{r hnpi, cache = TRUE, fig.dim = c(10,20), results='hide',fig.keep='all'} # read in model outputs hnpi_outputs_global <- setDT(import_model_outputs(res_dir, "hnpi", 'global', 'final')) node_names <- unique(hnpi_outputs_global %>% .[ , get(config$spatial_setup$nodenames)]) @@ -454,9 +470,8 @@ hnpi_plots <- lapply(node_names, geom_jitter(aes(group = npi_name), size = 0.6, height = 0, width = 0.2, alpha = 1) } + facet_wrap(~geoid, scales = 'free') + - guides(color = guide_legend(override.aes = list(size = 0.5))) + scale_color_viridis_c(option = "B", name = "log\nlikelihood") + - theme_classic()+ theme_small + theme_classic()+ theme_small+ theme(legend.key.size = unit(0.2, "cm")) } ) print(do.call("grid.arrange", c(hnpi_plots, ncol=4))) @@ -464,15 +479,19 @@ print(do.call("grid.arrange", c(hnpi_plots, ncol=4))) ``` # Inference: analyses -## Likelihood +If you ran inference, here are some analyses that might be helpful! + +## Likelihood (TO ADD: some acceptance stuff) ```{r llik_acceptances} ``` -## Inference specific outcomes: aggregated {.tabset} -### Single trajectories (aggregated by fitting) {.tabset} -```{r hosp_trajectories_inference_aggregate, fig.dim = c(8,20), results='hide',fig.keep='all'} +## Inference specific outcomes: aggregated single trajectories {.tabset} + +In your inference method you specified that your model be fit to `r names(config$inference$statistics)`, with some aggregation over period: `r unlist(config$inference$statistics)[which(str_detect(names(unlist(config$inference$statistics)), "period"))]`. + +```{r hosp_trajectories_inference_aggregate, fig.dim = c(10,10), results='asis'} if(inference){ # get all outcome variables scns <- config$outcomes$scenarios @@ -485,7 +504,7 @@ if(inference){ cat("\n\n") for(i in 1:length(fit_stats)){ - cat(paste0("#### ",fit_stats[i]," \n")) + cat(paste0("### ",fit_stats[i]," {.tabset} \n")) statistics <- purrr::flatten(config$inference$statistics[i]) cols_sim <- c("date", statistics$sim_var, "geoid","slot") @@ -526,6 +545,7 @@ if(inference){ # )) %>% dplyr::mutate(geoid = x)) %>% dplyr::bind_rows() ## Incident + cat(paste0("#### Incident \n")) print( df_data %>% setDT() %>% @@ -544,8 +564,10 @@ if(inference){ labs(x = 'date', y = statistics$name, title = "Incidence") + theme_classic() + theme_small ) - + cat("\n\n") + ## Cumulative + cat(paste0("#### Cumulative \n")) print( df_data %>% setDT() %>% @@ -573,18 +595,19 @@ if(inference){ ``` -### Quantiles(aggregated by fitting) {.tabset} -```{r hosp_aggregate_quantiles, fig.dim = c(8,20), results='hide',fig.keep='all'} +## Inference specific outcomes: aggregated quantiles {.tabset} +```{r hosp_aggregate_quantiles, fig.dim = c(10,10), results='asis'} if(length(unique(hosp_outputs_global$slot)) > 1 & inference){ cat("\n\n") for(i in 1:length(fit_stats)){ - cat(paste0("#### ",fit_stats[i]," \n")) + cat(paste0("### ",fit_stats[i]," {.tabset} \n")) statistics <- purrr::flatten(config$inference$statistics[i]) # Incident + cat(paste0("#### Incident \n")) print( df_data %>% setDT() %>% @@ -605,6 +628,9 @@ if(length(unique(hosp_outputs_global$slot)) > 1 & inference){ ) ## Cumulative + cat("\n\n") + cat(paste0("#### Cumulative \n")) + print( df_data %>% setDT() %>% @@ -640,7 +666,7 @@ if(length(unique(hosp_outputs_global$slot)) > 1 & inference){ Trajectories of the 5 and bottom 5 log likelihoods for each subpopulation. -```{r hosp_trajectories_by_likelihood, fig.dim = c(8,20), results='hide',fig.keep='all'} +```{r hosp_trajectories_by_likelihood, fig.dim = c(10,20), results='hide',fig.keep='all'} if(inference){ @@ -682,10 +708,10 @@ if(inference){ aes(lubridate::as_date(date), value), color = 'firebrick', alpha = 0.1) } + facet_wrap(~geoid, scales = 'free') + - guides(color = guide_legend(override.aes = list(size = 0.5)), - linetype = 'none') + + guides(linetype = 'none') + labs(x = 'date', y = fit_stats[i]) + #, title = paste0("top 5, bottom 5 lliks, ", statistics$sim_var)) + - theme_classic() + theme_small + theme_classic() + theme_small + + theme(legend.key.size = unit(0.2, "cm")) } )