Depends on dependencies of plot_GDM.Rmd
, until section preparation (needs maxsplines from given model)
#TODO : make independent from plot_GDM.Rmd
# run for each desired model. ####### # Here : EFdistance restab <- create_restab0() ####### # See script `GDM_multifun_thresholds.Rmd` for loading restab. # # for EFtrheshold weighted average : # model_name <- "EFturnover_WEIGTHED_mean_across_thresholds" model_name <- "EFnestedness_WEIGTHED_mean_across_thresholds" # # restab <- readRDS(paste(pathtodata, "/analysis/output_datasets/weighted_avg_over_all_thresholds_EFturnover.Rds", sep = "")) restab <- readRDS(paste(pathtodata, "/analysis/output_datasets/weighted_avg_over_all_thresholds_EFnestedness.Rds", sep = "")) setnames(restab, old = c("wres"), new = c("maxsplines")) #TODO : this line is maybe not needed any more restab <- merge(restab, nicenames[, .(names, correlated, lui_ground, lui_ground_nicenames, lui_component)], by = "names") ####### # create overviewbars # exclude correlated drivers ov_list <- create_overviewbar_restab(restab = restab[correlated != "yes", ]) # above- below df <- data.frame(ov_list$`above-below`) ov1 <- create_overview_above_below_abiotic_barplot() df <- data.frame(ov_list$`turnover-nestedness`) ov2 <- create_overview_turnover_nestedness_abiotic_barplot() plot_grid(NULL, ov1, ov2, NULL, nrow = 4, align = T, labels = model_name) # saved as : "sensitivity_small_extras_overviewbar_without_correlated_gdm_EFdistance_LUI.pdf" A5 # "sensitivity_small_extras_overviewbar_without_correlated_EFnestedness_WEIGTHED_mean_across_thresholds.pdf"
dependencies : needs maxsplines from given model, and variable called restab
. Run from script plot_GDM.Rmd
.
# create categories : biotic, LUI, abiotic restab[lui_component %in% c("nestedness", "turnover"), lui_component := "bio"] sumtab <- aggregate(maxsplines ~ lui_component, restab, sum) ggplot(sumtab, aes(x = lui_component, y = maxsplines)) + geom_bar(stat = "identity") + ggtitle("Summed contributions of EFnestedness0.7")
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