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added ks in Description as package requirement
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Paper/MeasurementVariance/DistributionFittingTables.qmd
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -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") | ||
| ``` | ||
|
|
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