## ----include = FALSE---------------------------------------------------------- knitr::opts_chunk$set( collapse = TRUE, comment = "#>", fig.width = 12, fig.height = 8, fig.asp = 0.8, out.width = "80%", dev.args = list(png = list(type = "cairo")) ) ## ----setup-------------------------------------------------------------------- weights <- TAD::AB[, 5:102] weights_factor <- TAD::AB[, c("Year", "Plot", "Treatment", "Bloc")] trait_data <- log(TAD::trait[["SLA"]][seq_len(ncol(weights))]) aggregation_factor_name <- c("Year", "Bloc") statistics_factor_name <- c("Treatment") regenerate_abundance_df <- TRUE regenerate_weighted_moments_df <- TRUE regenerate_stat_per_obs_df <- TRUE regenerate_stat_per_rand_df <- TRUE regenerate_stat_skr_df <- TRUE randomization_number <- 100 seed <- 1312 significativity_threshold <- c(0.025, 0.975) lin_mod <- "lm" slope_distance <- TAD:::CONSTANTS$SKEW_UNIFORM_SLOPE_DISTANCE intercept_distance <- TAD:::CONSTANTS$SKEW_UNIFORM_INTERCEPT_DISTANCE future::plan(future::multisession) results <- TAD::launch_analysis_tad( weights = weights, weights_factor = weights_factor, trait_data = trait_data, randomization_number = randomization_number, aggregation_factor_name = aggregation_factor_name, statistics_factor_name = statistics_factor_name, seed = seed, regenerate_abundance_df = TRUE, regenerate_weighted_moments_df = TRUE, regenerate_stat_per_obs_df = TRUE, regenerate_stat_per_rand_df = TRUE, regenerate_stat_skr_df = TRUE, significativity_threshold = significativity_threshold, lin_mod = lin_mod, slope_distance = slope_distance, intercept_distance = intercept_distance ) future::plan(future::sequential) ## ----------------------------------------------------------------------------- str(results$weighted_moments) str(results$statistics_per_observation) moments_graph <- TAD::moments_graph( moments_df = results$weighted_moments, statistics_per_observation = results$statistics_per_observation, statistics_factor_name = statistics_factor_name, statistics_factor_name_breaks = c("Mown_Unfertilized", "Mown_NPK"), statistics_factor_name_col = c("#1A85FF", "#D41159") ) moments_graph ## ----------------------------------------------------------------------------- str(results$weighted_moments) skr_graph <- TAD::skr_graph( moments_df = results$weighted_moments, statistics_factor_name = statistics_factor_name, statistics_factor_name_breaks = c("Mown_Unfertilized", "Mown_NPK"), statistics_factor_name_col = c("#1A85FF", "#D41159"), slope_distance = slope_distance, intercept_distance = intercept_distance ) skr_graph ## ----------------------------------------------------------------------------- str(results$ses_skr) skr_param_graph <- TAD::skr_param_graph( skr_param = results$ses_skr, statistics_factor_name = statistics_factor_name, statistics_factor_name_breaks = c("Mown_Unfertilized", "Mown_NPK"), statistics_factor_name_col = c("#1A85FF", "#D41159"), slope_distance = slope_distance, intercept_distance = intercept_distance ) skr_param_graph ## ----------------------------------------------------------------------------- results <- TAD::launch_analysis_tad( weights = weights, weights_factor = weights_factor, trait_data = trait_data, randomization_number = randomization_number, aggregation_factor_name = aggregation_factor_name, statistics_factor_name = statistics_factor_name, seed = seed, regenerate_abundance_df = TRUE, regenerate_weighted_moments_df = TRUE, regenerate_stat_per_obs_df = TRUE, regenerate_stat_per_rand_df = TRUE, regenerate_stat_skr_df = TRUE, significativity_threshold = significativity_threshold, lin_mod = lin_mod, slope_distance = slope_distance, intercept_distance = (intercept_distance <- 1.90) ) str(results$ses_skr) skr_param_graph <- TAD::skr_param_graph( skr_param = results$ses_skr, statistics_factor_name = statistics_factor_name, statistics_factor_name_breaks = c("Mown_Unfertilized", "Mown_NPK"), statistics_factor_name_col = c("#1A85FF", "#D41159"), slope_distance = 1, intercept_distance = intercept_distance ) skr_param_graph