From c5b292bbe6ccd6c30974e1a2087fe99ce76996b2 Mon Sep 17 00:00:00 2001 From: Konrad1991 Date: Thu, 23 Jan 2025 07:48:23 +0100 Subject: [PATCH] added ks in Description as package requirement --- .../MeasurementVariance/DistributionFitting.R | 41 ++++-- .../DistributionFittingTables.qmd | 139 ++++++++++++++++++ .../LocationEstimation.png | Bin 485570 -> 487443 bytes tsf/DESCRIPTION | 3 +- 4 files changed, 172 insertions(+), 11 deletions(-) create mode 100644 Paper/MeasurementVariance/DistributionFittingTables.qmd diff --git a/Paper/MeasurementVariance/DistributionFitting.R b/Paper/MeasurementVariance/DistributionFitting.R index 56430da..c3b16ee 100644 --- a/Paper/MeasurementVariance/DistributionFitting.R +++ b/Paper/MeasurementVariance/DistributionFitting.R @@ -1,3 +1,4 @@ +setwd("/home/konrad/Documents/GitHub/RProjects/Thermosimfit/Paper/MeasurementVariance/") library(ggplot2) library(ks) library(cowplot) @@ -187,6 +188,12 @@ fit_distri <- function(data, distri) { ) } +# Median IQR +# ======================================== +median_iqr <- function(x) { + c(median(x), quantile(x, 0.25), quantile(x, 0.75)) +} + # Bootstrap method for calculating CI for the median # ======================================== calc_location_ci_bootstrap <- function( @@ -352,6 +359,7 @@ plotting <- function(data, idx, distri, density_data, kd4_m_ci) { data[, idx] <- transform(data[, idx], distri) bins <- hist(data[, idx], plot = FALSE)$breaks median_ci <- calc_location_ci_bootstrap(median, data[, idx]) + median_iqr <- median_iqr(data[, idx]) mean_ci <- calc_location_ci_bootstrap(mean, data[, idx]) kde_ci <- kde(data[, idx]) fd <- fit_distri(data[, idx], distri) @@ -363,18 +371,18 @@ plotting <- function(data, idx, distri, density_data, kd4_m_ci) { y <- c(3, 3, 1.4, 2.1)[idx] location_error <- data.frame( x = c( - mean_ci$location, median_ci$location, kde_ci$mode, kd4_m_ci[1] + mean_ci$location, median_ci$location, kde_ci$mode, kd4_m_ci[1], median_iqr[1] ), xmin = c( - mean_ci$lower_ci, median_ci$lower_ci, kde_ci$lower_ci, kd4_m_ci[2] + mean_ci$lower_ci, median_ci$lower_ci, kde_ci$lower_ci, kd4_m_ci[2], median_iqr[2] ), xmax = c( - mean_ci$upper_ci, median_ci$upper_ci, kde_ci$upper_ci, kd4_m_ci[3] + mean_ci$upper_ci, median_ci$upper_ci, kde_ci$upper_ci, kd4_m_ci[3], median_iqr[3] ), type = c( - "Mean", "Median", "Mode (Kernel density)", "Mode (joint Kernel density)" + "Mean", "Median", "Mode (Kernel density)", "Mode (joint Kernel density)", "Median IQR" ), - y = c(0.5, 0.6, 0.7, 0.8) + y = c(0.5, 0.6, 0.7, 0.8, 0.9) ) p <- ggplot() + @@ -407,7 +415,7 @@ plotting <- function(data, idx, distri, density_data, kd4_m_ci) { ) + labs(x = names(data)[idx], y = "Density") - colors <- RColorBrewer::brewer.pal(4, "Dark2") + colors <- RColorBrewer::brewer.pal(5, "Dark2") p <- p + geom_errorbarh( data = location_error, @@ -425,7 +433,8 @@ plotting <- function(data, idx, distri, density_data, kd4_m_ci) { "Mean" = colors[1], "Median" = colors[2], "Mode (Kernel density)" = colors[3], - "Mode (joint Kernel density)" = colors[4] + "Mode (joint Kernel density)" = colors[4], + "Median IQR" = colors[5] ) ) + scale_linetype_manual( @@ -502,7 +511,7 @@ calc_values <- function(df, distris) { l <- back_transform(res$lower_ci[idx], distris[idx], min, max) u <- back_transform(res$upper_ci[idx], distris[idx], min, max) df_temp <- data.frame( - values = c(mode, l, u), + values = sprintf("%.3e", c(mode, l, u)), type = c("mode", "lower", "upper") ) names(df_temp)[1] <- names(df)[idx] @@ -517,6 +526,18 @@ calc_values <- function(df, distris) { row.names(res) <- NULL return(res) } -calc_values(p_dba, distris) -calc_values(p_ida, distris) +dba_res <- calc_values(p_dba, distris) +dba_res +ida_res <- calc_values(p_ida, distris) +ida_res calc_values(p_gda, distris) + + +# Calc IQR +calc_iqr <- function(df) { + df <- df[, 1:4] + lapply(1:4, function(x) { + median_iqr(df[, x]) + }) +} +calc_iqr(p_dba) diff --git a/Paper/MeasurementVariance/DistributionFittingTables.qmd b/Paper/MeasurementVariance/DistributionFittingTables.qmd new file mode 100644 index 0000000..b5d6353 --- /dev/null +++ b/Paper/MeasurementVariance/DistributionFittingTables.qmd @@ -0,0 +1,139 @@ +--- +format: + html: + code-fold: true +--- + +```{r} +#| warning: false +#| echo: false + +setwd("/home/konrad/Documents/GitHub/RProjects/Thermosimfit/Paper/MeasurementVariance/") +library(ks) + +# Load the results +# ======================================== +extract_best_runs <- function(res) { + states <- res$states + params <- res$params + errors <- res$metrices + errors <- Reduce(rbind, errors) + states <- Reduce(rbind, states) + params <- Reduce(rbind, params) + params <- lapply(unique(errors$dataset), function(x) { + params_subset <- params[params$dataset == x, ] + errors_subset <- errors[errors$dataset == x, ] + errors_subset <- errors_subset[order(errors_subset$MeanSquareError), ][1:50, ] + res <- params_subset[params_subset$repetition %in% errors_subset$repetition, ] + res <- res[, 1:4] + res$error <- errors_subset$MeanSquareError + return(res) + }) + params <- Reduce(rbind, params) + return(params) +} + +load_params <- function(path) { + load(path) + extract_best_runs(res[[1]]) +} + +p_dba <- load_params("dba_100Runs.RData") +p_ida <- load_params("ida_100.RData") +p_gda <- load_params("gda_100.RData") + +# Transform the data +# ======================================== +transform <- function(data, distri) { + data <- (data - min(data)) / (max(data) - min(data)) + data <- ifelse(data < 1e-6, 1e-6, data) # Avoid log(0) + if (distri != "norm" && distri != "exp") { + if (min(data) < 1e-2) { + data <- data + 0.01 + } + } + return(data) +} + +kde4d_intern <- function(df) { + mins <- apply(df, 2, min) + maxs <- apply(df, 2, max) + res <- ks::kde(df, xmin = mins, xmax = maxs) + grid_points <- expand.grid(res$eval.points) + joint_densities <- as.vector(res$estimate) + density_data <- cbind(grid_points, joint_density = joint_densities) + return(density_data) +} +kde4d_with_smr <- function(df, distris, prob = 0.95) { + df <- df[, 1:4] + df <- mapply(transform, df, distris, SIMPLIFY = FALSE) + df <- as.data.frame(df) + res <- ks::kde(df) + level <- paste0(prob * 100, "%") + density_threshold <- res$cont[level] + grid_points <- expand.grid(res$eval.points) + densities <- as.vector(res$estimate) + significant_points <- grid_points[densities >= density_threshold, ] + mode_index <- which.max(densities) + mode <- grid_points[mode_index, ] + mode <- ifelse(mode < 0, 0, mode) |> as.numeric() + CIs <- apply(significant_points, 2, range) + lc <- CIs[1, ] + lc <- ifelse(lc < 0, 0, lc) + uc <- CIs[2, ] + uc <- ifelse(uc < 0, 0, uc) + res <- kde4d_intern(df) + res[, 5] <- transform(res[, 5], "norm") + df <- lapply(1:4, function(x) { + i <- parent.frame()$i[] + data.frame(x = res[, i], y = res[, 5]) + }) + return(list( + mode = mode, + lower_ci = lc, + upper_ci = uc, + df = df + )) +} + +# Calculate results +distis <- rep("norm", 4) +back_transform <- function(transformed_data, distri, + original_min, original_max) { + original_data <- transformed_data * + (original_max - original_min) + original_min + return(original_data) +} +calc_values <- function(df, distris) { + res <- kde4d_with_smr(df, distris) + res$mode + res$lower_ci + res$upper_ci + res <- lapply(1:4, function(idx) { + max <- max(df[, idx]) + min <- min(df[, idx]) + mode <- back_transform(res$mode[idx], distris[idx], min, max) + l <- back_transform(res$lower_ci[idx], distris[idx], min, max) + u <- back_transform(res$upper_ci[idx], distris[idx], min, max) + df_temp <- data.frame( + values = c(mode, l, u), + type = c("mode", "lower", "upper") + ) + names(df_temp)[1] <- names(df)[idx] + return(df_temp) + }) + res <- lapply(res, function(x) { + x[, 1] + }) + res <- Reduce(rbind, res) |> as.data.frame() + res <- cbind(names(df)[1:4], res) + names(res) <- c("Parameter", "mode", "lower", "upper") + row.names(res) <- NULL + return(res) +} +dba_res <- calc_values(p_dba, distris) +ida_res <- calc_values(p_ida, distris) +gda_res <- calc_values(p_gda, distris) +knitr::kable(dba_res, format = "latex") +``` + diff --git a/Paper/MeasurementVariance/LocationEstimation.png b/Paper/MeasurementVariance/LocationEstimation.png index 4b585457ca6123080593fd3a93ebabf958863a86..2a0487514b07ce75380595041904b79133bc76d3 100644 GIT binary patch literal 487443 zcmdq~cRZGF{|Am=O>GUOA-o$%LRwa6Xdo-4A}XX|NA_sQJBiFIS*3&uNg0{(wo*h& zva(n9-rwhO4RwEhzkj~}ey{s+e?HgS@`n#nrpSLDr-pmuH}g%7oW4e(2yP_*onaLweVsz# zrW`%EPt7Xuca^PApoKllFUyNv0k*q`+KpzezF{(7H!g|$(m(Syd^z*@Xe`Go*9_0& zF9$Egnni{M+q8*sEM|-H8=Pmzc7r?9W@o6uC!^g<=g(O2==Hi4N8T8C9eZYg#(v&h5d$!HA6w znccNn1qB7k$;qti#n~)Y{d-Da)yo$z7UUKd7Dh)$&+C(s;@h@uy_lKq2G1V}#@eN) zxow^v_7D`D?9ebZ4&m0TOf=oM)ZE;>e_%j1XwHU{r^(0v46k>7s_oFB0G8el5h_pR zD?ElhUZXLtN#8! zYj*Bk{`WgPSo!h)=7|%#F8ufJtLnAn`%VU;&r^I9c!qwL$LZv{@3kG=wEXUNog6iFb)_Kj6S)i# zcH&;w80PNo9x~p*>mk{jw?awMbhx|rhGM{O!=H=yRGL&-lzk48*i*lTwun40d`d%; 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