From bfa9174c46733df974aa3812881cd34e9e958742 Mon Sep 17 00:00:00 2001 From: Pat Johnson Date: Sun, 27 Oct 2024 14:30:52 -0500 Subject: [PATCH] feat: add dx_npv_prevalence and dx_ppv_prevalence --- .Rbuildignore | 2 + .gitignore | 3 + NAMESPACE | 2 + R/dx_constructor.R | 5 +- R/dx_measure.R | 6 ++ R/dx_metrics.R | 118 ++++++++++++++++++++++++++++++ README.Rmd | 2 +- README.md | 36 ++++----- man/dx.Rd | 4 + man/dx_npv_prevalence.Rd | 53 ++++++++++++++ man/dx_ppv_prevalence.Rd | 53 ++++++++++++++ man/figures/logo.png | Bin 0 -> 552356 bytes tests/testthat/test-constructor.R | 15 ---- tests/testthat/test-metrics.R | 42 +++++++++++ 14 files changed, 307 insertions(+), 34 deletions(-) create mode 100644 man/dx_npv_prevalence.Rd create mode 100644 man/dx_ppv_prevalence.Rd create mode 100644 man/figures/logo.png diff --git a/.Rbuildignore b/.Rbuildignore index f062d3c..9fbfba7 100755 --- a/.Rbuildignore +++ b/.Rbuildignore @@ -1,3 +1,5 @@ +^renv$ +^renv\.lock$ ^diagnosticSummary\.Rproj$ ^\.Rproj\.user$ ^LICENSE\.md$ diff --git a/.gitignore b/.gitignore index d411513..9ea7520 100755 --- a/.gitignore +++ b/.gitignore @@ -9,3 +9,6 @@ docs/ inst/WORDLIST docs test_cases +renv/ +.Rprofile +renv.lock diff --git a/NAMESPACE b/NAMESPACE index 3df570b..f97740c 100755 --- a/NAMESPACE +++ b/NAMESPACE @@ -34,6 +34,7 @@ export(dx_mcc) export(dx_mcnemars) export(dx_nir) export(dx_npv) +export(dx_npv_prevalence) export(dx_odds_ratio) export(dx_plot_calibration) export(dx_plot_cap) @@ -52,6 +53,7 @@ export(dx_plot_rocs) export(dx_plot_thresholds) export(dx_plot_youden_j) export(dx_ppv) +export(dx_ppv_prevalence) export(dx_prevalence) export(dx_sensitivity) export(dx_specificity) diff --git a/R/dx_constructor.R b/R/dx_constructor.R index 0a3dee4..9f5329b 100755 --- a/R/dx_constructor.R +++ b/R/dx_constructor.R @@ -15,6 +15,8 @@ #' @param outcome_label Label for outcome (string) #' @param grouping_variables Character vector of variable names to #' be summarized by. Variables are converted to factors if not already one. +#' @param prevalence Numeric value between 0 and 1, representing a target +#' prevalence for additional NPV and PPV calculations. #' @param citype Confidence interval type. #' @param bootreps Number of bootstrap samples used to generate F1 score CI #' @param bootseed Seed value to be used when calculating bootsraped CI's @@ -27,7 +29,7 @@ dx <- function(data, classlabels = c("Negative", "Positive"), threshold_range = NA, outcome_label = NA, pred_varname, true_varname, - setthreshold = .5, poslabel = 1, grouping_variables = NA, + setthreshold = .5, poslabel = 1, grouping_variables = NA, prevalence = NA, citype = "exact", bootreps = 2000, bootseed = 20191015, doboot = FALSE, direction = "auto", ...) { @@ -84,6 +86,7 @@ dx <- function(data, setthreshold = setthreshold, poslabel = poslabel, grouping_variables = grouping_variables, + prevalence = prevalence, citype = citype, bootreps = bootreps, bootseed = bootseed, diff --git a/R/dx_measure.R b/R/dx_measure.R index c30865f..f66d04e 100755 --- a/R/dx_measure.R +++ b/R/dx_measure.R @@ -197,6 +197,12 @@ dx_measure <- function(data, threshold, options, var = "Overall", # Apply the generic function to all metrics and combine results metrics_list <- lapply(metric_calculations, call_metric) + if (!is.na(options$prevalence)) { + metrics_list$npv_prevalence <- dx_npv_prevalence(cm, prevalence = options$prevalence) + metrics_list$ppv_prevalence <- dx_ppv_prevalence(cm, prevalence = options$prevalence) + } + + # Combine all metric results into one data frame results <- do.call(rbind, metrics_list) diff --git a/R/dx_metrics.R b/R/dx_metrics.R index 1c82eeb..6d3c7e0 100644 --- a/R/dx_metrics.R +++ b/R/dx_metrics.R @@ -136,6 +136,124 @@ dx_npv <- function(cm, detail = "full", ...) { metric_binomial(cm$tn, cm$testneg, name = "Negative Predictive Value", detail = detail, ...) } +metric_npv_ppv_prevalence <- function(est, logit, varlogit, ...) { + cc <- stats::qnorm(1 - (1 - 0.95) / 2) + lci_logit <- logit - cc * sqrt(varlogit) + uci_logit <- logit + cc * sqrt(varlogit) + + lower <- exp(lci_logit) / ( 1 + exp(lci_logit)) + upper <- exp(uci_logit) / ( 1 + exp(uci_logit)) + + formatted_estimate <- conf_int( + est, + lower, + upper, + percent = TRUE + ) + + measure_df( + estimate = formatted_estimate, + estimate_raw = est, + lci_raw = lower, + uci_raw = upper, + ci_type = "Simple Logit (Mercaldo et al, 2007)", + ... + ) +} + +#' Calculate Negative Predictive Value (NPV) at Target Prevalence +#' +#' Computes the Negative Predictive Value (NPV) adjusted to a specified prevalence +#' level. This function is useful for understanding classifier performance in settings +#' where the actual prevalence of the condition may differ from that in the initial data. +#' +#' @inherit metrics-params +#' +#' @param prevalence Numeric value between 0 and 1, representing the target prevalence +#' for adjusting the NPV calculation. +#' +#' @details +#' This function calculates the NPV using the formula: +#' \deqn{NPV = \frac{Specificity \times (1 - Prevalence)}{(1 - Sensitivity) \times Prevalence + Specificity \times (1 - Prevalence)}} +#' where `Specificity` is the true negative rate and `Sensitivity` is the true positive rate. +#' Adjusting NPV for prevalence allows better estimation of the classifier’s performance +#' in different population settings. +#' +#' Confidence intervales are calucated using a simple logit (Mercaldo et al, 2007) +#' +#' @examples +#' cm <- dx_cm(dx_heart_failure$predicted, dx_heart_failure$truth, +#' threshold = 0.5, poslabel = 1 +#' ) +#' # Calculate NPV at a prevalence of 0.1 +#' dx_npv_prevalence(cm, prevalence = 0.1) +#' +#' @seealso [dx_cm()], [dx_npv()] for the non-prevalence adjusted NPV. +#' @export +#' @concept metrics +dx_npv_prevalence <- function(cm, prevalence, detail = "full", ...) { + spec <- dx_specificity(cm, detail = "simple") + sens <- dx_sensitivity(cm, detail = "simple") + npv <- (spec * (1 - prevalence)) / ((1 - sens) * prevalence + spec * (1 - prevalence)) + if (detail == "simple") return(npv) + logit <- log((spec * (1 - prevalence)) / ((1 - sens) * prevalence)) + varlogit <- (sens / (1 - sens)) * 1 / cm$dispos + ((1 - spec) / spec ) * 1 / cm$disneg + metric_npv_ppv_prevalence( + est = npv, + logit, + varlogit, + measure = "Negative Predictive Value (Target Prevalence)", + notes = paste0("Prevalence value: ", prevalence) + ) +} + +#' Calculate Positive Predictive Value (PPV) at Target Prevalence +#' +#' Computes the Positive Predictive Value (PPV) adjusted to a specified prevalence +#' level. This function is useful for understanding classifier performance in settings +#' where the actual prevalence of the condition may differ from that in the initial data. +#' +#' @inherit metrics-params +#' +#' @param prevalence Numeric value between 0 and 1, representing the target prevalence +#' for adjusting the PPV calculation. +#' @details +#' This function calculates the PPV using the formula: +#' \deqn{PPV = \frac{Sensitivity \times Prevalence}{(Sensitivity \times Prevalence) + (1 - Specificity) \times (1 - Prevalence)}} +#' where `Sensitivity` is the true positive rate and `Specificity` is the true negative rate. +#' Adjusting PPV for prevalence allows a more accurate assessment of the classifier’s performance +#' in different population settings. +#' +#' Confidence intervales are calucated using a simple logit (Mercaldo et al, 2007) +#' +#' @examples +#' cm <- dx_cm(dx_heart_failure$predicted, dx_heart_failure$truth, +#' threshold = 0.5, poslabel = 1 +#' ) +#' # Calculate PPV at a prevalence of 0.1 +#' dx_ppv_prevalence(cm, prevalence = 0.1) +#' +#' @seealso [dx_cm()], [dx_ppv()] for the non-prevalence adjusted PPV. +#' @export +#' @concept metrics +dx_ppv_prevalence <- function(cm, prevalence, detail = "full", ...) { + spec <- dx_specificity(cm, detail = "simple") + sens <- dx_sensitivity(cm, detail = "simple") + ppv <- (sens * prevalence) / ((sens * prevalence) + (1-spec) * (1 - prevalence)) + + if (detail == "simple") return(ppv) + + logit <- log((sens * prevalence) / ((1 - spec) * (1 - prevalence))) + varlogit <- ((1 - sens) / sens ) * 1 / cm$dispos + (spec / (1 - spec) ) * 1 / cm$disneg + metric_npv_ppv_prevalence( + est = ppv, + logit, + varlogit, + measure = "Positive Predictive Value (Target Prevalence)", + notes = paste0("Prevalence value: ", prevalence) + ) +} + #' Calculate False Negative Rate (FNR) #' #' Calculates the False Negative Rate (FNR), which is the proportion of actual positives diff --git a/README.Rmd b/README.Rmd index ddcd26a..031f9fd 100755 --- a/README.Rmd +++ b/README.Rmd @@ -13,7 +13,7 @@ knitr::opts_chunk$set( ) ``` -# diagnosticSummary +# diagnosticSummary [![Lifecycle: experimental](https://img.shields.io/badge/lifecycle-experimental-orange.svg)](https://www.tidyverse.org/lifecycle/#experimental) diff --git a/README.md b/README.md index f4fce6d..c662bbf 100755 --- a/README.md +++ b/README.md @@ -1,7 +1,7 @@ -# diagnosticSummary +# diagnosticSummary @@ -30,13 +30,16 @@ library(diagnosticSummary) # Load sample data data("dx_heart_failure") head(dx_heart_failure) -#> AgeGroup Sex truth predicted AgeSex -#> 1 (20,50] Male 0 0.016164112 (20,50] - Male -#> 2 (20,50] Male 0 0.074193671 (20,50] - Male -#> 3 (20,50] Female 0 0.004677979 (20,50] - Female -#> 4 (20,50] Female 0 0.017567313 (20,50] - Female -#> 5 (20,50] Female 0 0.017517025 (20,50] - Female -#> 6 (20,50] Male 0 0.051570734 (20,50] - Male +#> AgeGroup Sex truth predicted AgeSex predicted_rf +#> 1 (20,50] Male 0 0.016164112 (20,50] - Male 0.19774011 +#> 2 (20,50] Male 0 0.074193671 (20,50] - Male 0.04624277 +#> 3 (20,50] Female 0 0.004677979 (20,50] - Female 0.22448980 +#> 4 (20,50] Female 0 0.017567313 (20,50] - Female 0.09326425 +#> 5 (20,50] Female 0 0.017517025 (20,50] - Female 0.04878049 +#> 6 (20,50] Male 0 0.051570734 (20,50] - Male 0.10982659 +``` + +``` r # Create dx object dx_obj <- dx( @@ -67,23 +70,22 @@ summary(dx_obj, variable = "Overall", show_var = F, show_label = F) | LRT+ | 3.54 (2.66, 4.71) | | LRT- | 0.20 (0.13, 0.32) | | Odds Ratio | 17.59 (9.12, 33.94) | -| F1 Score | 75.5% (68.3%, 81.5%) | -| F2 Score | 80.7% (74.0%, 86.4%) | +| F1 Score | 75.5% (68.6%, 81.2%) | +| F2 Score | 80.7% (73.9%, 86.3%) | | Prevalence | 37.5% (31.7%, 43.7%) | | False Negative Rate | 15.3% (8.8%, 24.0%) | | False Positive Rate | 23.9% (17.6%, 31.2%) | | False Discovery Rate | 32.0% (23.8%, 41.0%) | | AUC PR | 0.87 | | Cohen’s Kappa | 0.58 (0.48, 0.68) | -| Matthews Correlation Coefficient | 59.0% (48.9%, 68.4%) | -| Balanced Accuracy | 80.4% (75.3%, 85.1%) | -| Informedness | 60.8% (50.9%, 70.7%) | -| Markedness | 57.2% (47.4%, 67.2%) | -| G-mean | 80.3% (75.1%, 84.8%) | -| Fowlkes-Mallows Index | 75.9% (69.6%, 81.4%) | +| Matthews Correlation Coefficient | 59.0% (49.6%, 68.1%) | +| Balanced Accuracy | 80.4% (75.5%, 85.5%) | +| Informedness | 60.8% (51.2%, 69.9%) | +| Markedness | 57.2% (47.2%, 66.7%) | +| G-mean | 80.3% (75.5%, 85.0%) | +| Fowlkes-Mallows Index | 75.9% (69.7%, 81.7%) | | Brier Score | 0.11 | | Pearson’s Chi-squared | p\<0.01 | -| Pearson’s Chi-squared | p\<0.01 | | Fisher’s Exact | p\<0.01 | | G-Test | p\<0.01 | diff --git a/man/dx.Rd b/man/dx.Rd index dbb0508..e073414 100644 --- a/man/dx.Rd +++ b/man/dx.Rd @@ -14,6 +14,7 @@ dx( setthreshold = 0.5, poslabel = 1, grouping_variables = NA, + prevalence = NA, citype = "exact", bootreps = 2000, bootseed = 20191015, @@ -44,6 +45,9 @@ with 1 being the event} \item{grouping_variables}{Character vector of variable names to be summarized by. Variables are converted to factors if not already one.} +\item{prevalence}{Numeric value between 0 and 1, representing a target +prevalence for additional NPV and PPV calculations.} + \item{citype}{Confidence interval type.} \item{bootreps}{Number of bootstrap samples used to generate F1 score CI} diff --git a/man/dx_npv_prevalence.Rd b/man/dx_npv_prevalence.Rd new file mode 100644 index 0000000..eb46a7a --- /dev/null +++ b/man/dx_npv_prevalence.Rd @@ -0,0 +1,53 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/dx_metrics.R +\name{dx_npv_prevalence} +\alias{dx_npv_prevalence} +\title{Calculate Negative Predictive Value (NPV) at Target Prevalence} +\usage{ +dx_npv_prevalence(cm, prevalence, detail = "full", ...) +} +\arguments{ +\item{cm}{A dx_cm object created by \code{\link[=dx_cm]{dx_cm()}}.} + +\item{prevalence}{Numeric value between 0 and 1, representing the target prevalence +for adjusting the NPV calculation.} + +\item{detail}{Character specifying the level of detail in the output: +"simple" for raw estimate, "full" for detailed estimate +including 95\% confidence intervals.} + +\item{...}{Additional arguments to pass to metric_binomial function, such as +\code{citype} for type of confidence interval method.} +} +\value{ +Depending on the \code{detail} parameter, returns a numeric value +representing the calculated metric or a data frame/tibble with +detailed diagnostics including confidence intervals and possibly other +metrics relevant to understanding the metric. +} +\description{ +Computes the Negative Predictive Value (NPV) adjusted to a specified prevalence +level. This function is useful for understanding classifier performance in settings +where the actual prevalence of the condition may differ from that in the initial data. +} +\details{ +This function calculates the NPV using the formula: +\deqn{NPV = \frac{Specificity \times (1 - Prevalence)}{(1 - Sensitivity) \times Prevalence + Specificity \times (1 - Prevalence)}} +where \code{Specificity} is the true negative rate and \code{Sensitivity} is the true positive rate. +Adjusting NPV for prevalence allows better estimation of the classifier’s performance +in different population settings. + +Confidence intervales are calucated using a simple logit (Mercaldo et al, 2007) +} +\examples{ +cm <- dx_cm(dx_heart_failure$predicted, dx_heart_failure$truth, + threshold = 0.5, poslabel = 1 +) +# Calculate NPV at a prevalence of 0.1 +dx_npv_prevalence(cm, prevalence = 0.1) + +} +\seealso{ +\code{\link[=dx_cm]{dx_cm()}}, \code{\link[=dx_npv]{dx_npv()}} for the non-prevalence adjusted NPV. +} +\concept{metrics} diff --git a/man/dx_ppv_prevalence.Rd b/man/dx_ppv_prevalence.Rd new file mode 100644 index 0000000..3a6fa73 --- /dev/null +++ b/man/dx_ppv_prevalence.Rd @@ -0,0 +1,53 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/dx_metrics.R +\name{dx_ppv_prevalence} +\alias{dx_ppv_prevalence} +\title{Calculate Positive Predictive Value (PPV) at Target Prevalence} +\usage{ +dx_ppv_prevalence(cm, prevalence, detail = "full", ...) +} +\arguments{ +\item{cm}{A dx_cm object created by \code{\link[=dx_cm]{dx_cm()}}.} + +\item{prevalence}{Numeric value between 0 and 1, representing the target prevalence +for adjusting the PPV calculation.} + +\item{detail}{Character specifying the level of detail in the output: +"simple" for raw estimate, "full" for detailed estimate +including 95\% confidence intervals.} + +\item{...}{Additional arguments to pass to metric_binomial function, such as +\code{citype} for type of confidence interval method.} +} +\value{ +Depending on the \code{detail} parameter, returns a numeric value +representing the calculated metric or a data frame/tibble with +detailed diagnostics including confidence intervals and possibly other +metrics relevant to understanding the metric. +} +\description{ +Computes the Positive Predictive Value (PPV) adjusted to a specified prevalence +level. This function is useful for understanding classifier performance in settings +where the actual prevalence of the condition may differ from that in the initial data. +} +\details{ +This function calculates the PPV using the formula: +\deqn{PPV = \frac{Sensitivity \times Prevalence}{(Sensitivity \times Prevalence) + (1 - Specificity) \times (1 - Prevalence)}} +where \code{Sensitivity} is the true positive rate and \code{Specificity} is the true negative rate. +Adjusting PPV for prevalence allows a more accurate assessment of the classifier’s performance +in different population settings. + +Confidence intervales are calucated using a simple logit (Mercaldo et al, 2007) +} +\examples{ +cm <- dx_cm(dx_heart_failure$predicted, dx_heart_failure$truth, + threshold = 0.5, poslabel = 1 +) +# Calculate PPV at a prevalence of 0.1 +dx_ppv_prevalence(cm, prevalence = 0.1) + +} +\seealso{ +\code{\link[=dx_cm]{dx_cm()}}, \code{\link[=dx_ppv]{dx_ppv()}} for the non-prevalence adjusted PPV. +} 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