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* Added `plot.SMCMallows()` method + example (#114) * Changed class of SMC outputs to SMCMallows (#114) This makes proper dispatching of possible, withou having any other apparent consequences. * Adapted SMC vignette and unit tests to new `plot()` method (#114) * Fixed CodeFactor issue Redundant blank line at the end of a code block should be deleted. * Gathererd SMC plot functions on same file (#114) This should help with future DRYing, perhaps both subfunctions should eventually be internalized? * Increment version number to 1.2.0.9004 * Updated NEWS.md * Updated build CI config file Using template from https://github.com/r-lib/actions/tree/v2-branch/examples#standard-ci-workflow * Added `plot.SMCMallows()` method (#263) * Added `plot.SMCMallows()` method + example (#114) * Changed class of SMC outputs to SMCMallows (#114) This makes proper dispatching of possible, withou having any other apparent consequences. * Adapted SMC vignette and unit tests to new `plot()` method (#114) * Fixed CodeFactor issue Redundant blank line at the end of a code block should be deleted. * Gathererd SMC plot functions on same file (#114) This should help with future DRYing, perhaps both subfunctions should eventually be internalized? * Increment version number to 1.2.0.9004 * Updated NEWS.md * Updated docs * Removed duplicated function As mentioned in the in-code comment, `scalefun()` was already defined in a different source file. * Increment version number to 1.2.1.9001 Co-authored-by: Øystein Sørensen <oystein_sorensen@hotmail.com> * Fix documentation for `plot.SMCMallows()` (#266) * Fixed links to other package functions Links were formatted wrongly assuming the package was setup to support markdown-formatted links. This fixes it. * Increment version number to 1.2.1.9002 * Reverting changes to the `compute_mallows()` docs * Reverting changes to NEWS.md Textual change is automatically introduced by `usethis::use_dev_version()`. * Deprecated `plot_*_posterior()` (#267) Following the conversation started [here](#263 (comment)), this commit moves the superseded subfunctions of `plot.SMCMallows()` into the `smc_mallows_deprecated.R` file for eventual removal. The subfunctions themselves were renamed and test units to test the deprecation warnings were written. * Matching SMC defaults to their MCMC counterparts (#269) * Updated documentation Some text were taken from the original implementation and make no longer sense. * Matched SMC defaults with their original counterparts (#114) * SMC metric defaults to footrule (#114) * SMC leap_size defaults to 1 (#114) * SMC alpha_prop_sd defaults to 0.5 (#114) * SMC alpha_max defaults to 1e6 (#114) * SMC lambda defautls to 0.1 (#114) * Update DESCRIPTION Incremented development version. Co-authored-by: Øystein Sørensen <oystein_sorensen@hotmail.com> * Added changes from PR #269 to NEWS.md Those are important to mention, since they technically break backwards compatibility. I forgot to do that on he PR itself, thankfully there's a second chance. :) Co-authored-by: Waldir Leoncio <w.l.netto@medisin.uio.no>
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
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| #' @title Plot SMC Posterior Distributions | ||
| #' @description Plot posterior distributions of SMC-Mallow parameters. | ||
| #' @param x An object of type \code{SMC-Mallows}, returned for example from | ||
| #' \code{\link{smc_mallows_new_users}}. | ||
| #' @param nmc Number of Monte Carlo samples | ||
| #' @param burnin A numeric value specifying the number of iterations | ||
| #' to discard as burn-in. Defaults to \code{model_fit$burnin}, and must be | ||
| #' provided if \code{model_fit$burnin} does not exist. See | ||
| #' \code{\link{assess_convergence}}. | ||
| #' @param parameter Character string defining the parameter to plot. Available | ||
| #' options are \code{"alpha"} and \code{"rho"}. | ||
| #' @param time Integer determining the update slice to plot | ||
| #' @param C Number of cluster | ||
| #' @param colnames A vector of item names. If NULL, generic names are generated | ||
| #' for the items in the ranking. | ||
| #' @param items Either a vector of item names, or a vector of indices. If NULL, | ||
| #' five items are selected randomly. | ||
| #' @param ... Other arguments passed to \code{\link[base]{plot}} (not used). | ||
| #' @return A plot of the posterior distributions | ||
| #' @author Waldir Leoncio | ||
| #' @export | ||
| #' @example /inst/examples/plot.SMCMallows_example.R | ||
| plot.SMCMallows <- function(x, nmc = nrow(x$rho_samples[, 1, ]), burnin = 0, | ||
| parameter = "alpha", time = ncol(x$rho_samples[, 1, ]), C = 1, | ||
| colnames = NULL, items = NULL, ...) { | ||
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| if (parameter == "alpha") { | ||
| output <- x$alpha_samples[, time] | ||
| plot_alpha_smc(output, nmc, burnin) | ||
| } else if (parameter == "rho") { | ||
| output <- x$rho_samples[, , time] | ||
| plot_rho_smc(output, nmc, burnin, C, colnames, items) | ||
| } else { | ||
| stop("parameter must be either 'alpha' or 'rho'.") | ||
| } | ||
| } | ||
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| plot_alpha_smc <- function(output, nmc, burnin) { | ||
| alpha_samples_table <- data.frame(iteration = 1:nmc, value = output) | ||
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| plot_posterior_alpha <- ggplot2::ggplot(alpha_samples_table, ggplot2::aes_(x = ~value)) + | ||
| ggplot2::geom_density() + | ||
| ggplot2::xlab(expression(alpha)) + | ||
| ggplot2::ylab("Posterior density") + | ||
| ggplot2::ggtitle(label = "Implemented SMC scheme") + | ||
| ggplot2::theme(plot.title = ggplot2::element_text(hjust = 0.5)) | ||
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| print(plot_posterior_alpha) | ||
| } | ||
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| plot_rho_smc <- function(output, nmc, burnin, C, colnames = NULL, items = NULL) { | ||
| n_items <- dim(output)[2] | ||
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| if (is.null(items) && n_items > 5) { | ||
| message("Items not provided by user or more than 5 items in a ranking. Picking 5 at random.") | ||
| items <- sample(seq_len(n_items), 5, replace = FALSE) | ||
| items <- sort(items) | ||
| } else if (is.null(items) && n_items <= 5) { | ||
| items <- seq_len(n_items) | ||
| items <- sort(items) | ||
| } | ||
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| # do smc processing here | ||
| smc_plot <- smc_processing(output = output, colnames = colnames) | ||
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| if (!is.character(items)) { | ||
| items <- unique(smc_plot$item)[items] | ||
| } | ||
|
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| iteration <- rep(seq_len(nmc), times = n_items) | ||
| df <- cbind(iteration, smc_plot) | ||
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| if (C == 1) { | ||
| df <- cbind(cluster = "Cluster 1", df) | ||
| } | ||
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| df <- df[df$iteration > burnin & df$item %in% items, , drop = FALSE] | ||
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| # Compute the density, rather than the count, since the latter | ||
| # depends on the number of Monte Carlo samples | ||
| df <- aggregate(list(n = df$iteration), | ||
| list(cluster = df$cluster, item = df$item, value = df$value), | ||
| FUN = length | ||
| ) | ||
| df$pct <- df$n / sum(df$n) | ||
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| df$item <- factor(df$item, levels = c(items)) | ||
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| # Finally create the plot | ||
| p <- ggplot2::ggplot(df, ggplot2::aes(x = .data$value, y = .data$pct)) + | ||
| ggplot2::geom_col() + | ||
| ggplot2::scale_x_continuous(labels = scalefun) + | ||
| ggplot2::xlab("rank") + | ||
| ggplot2::ylab("Posterior probability") | ||
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| if (C == 1) { | ||
| p <- p + ggplot2::facet_wrap(~ .data$item) | ||
| } else { | ||
| p <- p + ggplot2::facet_wrap(~ .data$cluster + .data$item) | ||
| } | ||
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| return(p) | ||
| } |
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