diff --git a/cran-comments.md b/cran-comments.md index 9f3c27e..7204630 100644 --- a/cran-comments.md +++ b/cran-comments.md @@ -8,15 +8,13 @@ ## R CMD check results -0 errors | 0 warnings | 1 note - -Maintainer: ‘Gorka Navarrete ’ - -Size of tarball: 6242682 bytes +0 errors | 0 warnings | 0 note ## New major version +Resending to reduce the tarball size below 5 MB as requestes by CRAN. Sorry! + This is a new version refactoring the way we calculate the PPV and NPV matrices. I also added parameters to control the scales, and multiple checks and tests. diff --git a/docs/articles/introduction.html b/docs/articles/introduction.html index d5fc1ae..c8d174b 100644 --- a/docs/articles/introduction.html +++ b/docs/articles/introduction.html @@ -79,7 +79,7 @@

Introduction to BayesianReasoning

Gorka Navarrete

-

2021-12-20

+

2021-12-21

Source: vignettes/introduction.Rmd
introduction.Rmd
@@ -88,8 +88,14 @@

2021-12-20

-

Bayesian reasoning in medical contexts +

Bayesian reasoning

+ +

CRAN status Codecov test coverage downloads Lifecycle: experimental DOI

+
+
+

Bayesian reasoning in medical contexts +

This package includes a few functions to plot and help understand Positive and Negative Predictive Values, and their relationship with Sensitivity, Specificity and Prevalence.

  • The Positive Predictive Value of a medical test is the probability that a positive result will mean having the disease. Formally p(Disease|+)
    • @@ -108,6 +114,7 @@

    Bayesian reasoning in medical co

    If you want to install the package can use: remotes::install_github("gorkang/BayesianReasoning"). Please report any problems you find in the Issues Github page.

    There is a shiny app implementation with most of the main features available.

    +

PPV_heatmap()

@@ -127,12 +134,9 @@

PPV_heatmap() PPV_heatmap(min_Prevalence = 1, max_Prevalence = 1000, - Sensitivity = 100, - Language = "en") -#> Warning in process_variables(min_Prevalence = min_Prevalence, max_Prevalence -#> = max_Prevalence, : * Specificity and limits_Specificity are NULL. Setting -#> Specificity = 95 and limits_Specificity = c(90, 100)

-

+ Sensitivity = 100, limits_Specificity = c(90, 100), + Language = "en")
+

NPV @@ -141,14 +145,10 @@

NPV 
 
 PPV_heatmap(PPV_NPV = "NPV",
-            min_Prevalence = 800,
-            max_Prevalence = 1000, 
-            Specificity = 95, 
-            Language = "en")
-#> Warning in process_variables(min_Prevalence = min_Prevalence, max_Prevalence
-#> = max_Prevalence, : * Sensitivity and limits_Specificity are NULL. Setting
-#> Sensitivity = 95 and limits_Sensitivity = c(90, 100)
-

+ min_Prevalence = 800, max_Prevalence = 1000, + Specificity = 95, limits_Sensitivity = c(90, 100), + Language = "en")

+

@@ -167,7 +167,7 @@

Area overlay= 4.8, overlay_prevalence_1 = 1, overlay_prevalence_2 = 68)

-

+

The area plot overlay can show more details about how the calculation of PPV/NPV is performed:

 
@@ -181,7 +181,7 @@ 
Area overlay= 4.8,
             overlay_prevalence_1 = 1,
             overlay_prevalence_2 = 68)
-

+

Line overlay @@ -198,7 +198,7 @@

Line overlay= c(6.5, 7, 8, 9, 12, 14, 14), overlay_prevalence_1 = c(1, 1, 1, 1, 1, 1, 1), overlay_prevalence_2 = c(22, 26, 29, 44, 69, 227, 1667))

-

+

Another example. In this case, the FP is constant across age:

@@ -211,7 +211,7 @@ 
Line overlay= c(4.8, 4.8, 4.8),
             overlay_prevalence_1 = c(1, 1, 1),
             overlay_prevalence_2 = c(68, 626, 1068))
-

+

@@ -226,7 +226,7 @@

PPV_diagnostic_vs_screening()= 100, prevalence_screening_group = 1000, prevalence_diagnostic_group = 2) -

+

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- * Bootstrap Table of Contents v0.4.1 (http://afeld.github.io/bootstrap-toc/) - * Copyright 2015 Aidan Feldman - * Licensed under MIT (https://github.com/afeld/bootstrap-toc/blob/gh-pages/LICENSE.md) */ -(function() { - 'use strict'; - - window.Toc = { - helpers: { - // return all matching elements in the set, or their descendants - findOrFilter: function($el, selector) { - // http://danielnouri.org/notes/2011/03/14/a-jquery-find-that-also-finds-the-root-element/ - // http://stackoverflow.com/a/12731439/358804 - var $descendants = $el.find(selector); - return $el.filter(selector).add($descendants).filter(':not([data-toc-skip])'); - }, - - generateUniqueIdBase: function(el) { - var text = $(el).text(); - var anchor = text.trim().toLowerCase().replace(/[^A-Za-z0-9]+/g, '-'); - return anchor || el.tagName.toLowerCase(); - }, - - generateUniqueId: function(el) { - var anchorBase = this.generateUniqueIdBase(el); - for (var i = 0; ; i++) { - var anchor = anchorBase; - if (i > 0) { - // add suffix - anchor += '-' + i; - } - // check if ID already exists - if (!document.getElementById(anchor)) { - return anchor; - } - } - }, - - generateAnchor: function(el) { - if (el.id) { - return el.id; - } else { - var anchor = this.generateUniqueId(el); - el.id = anchor; - return anchor; - } - }, - - createNavList: function() { - return $('
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- } // else use the current $context - - $context.append($newNav); - - $prevNav = $newNav; - }); - }, - - parseOps: function(arg) { - var opts; - if (arg.jquery) { - opts = { - $nav: arg - }; - } else { - opts = arg; - } - opts.$scope = opts.$scope || $(document.body); - return opts; - } - }, - - // accepts a jQuery object, or an options object - init: function(opts) { - opts = this.helpers.parseOps(opts); - - // ensure that the data attribute is in place for styling - opts.$nav.attr('data-toggle', 'toc'); - - var $topContext = this.helpers.createChildNavList(opts.$nav); - var topLevel = this.helpers.getTopLevel(opts.$scope); - var $headings = this.helpers.getHeadings(opts.$scope, topLevel); - this.helpers.populateNav($topContext, topLevel, $headings); - } - }; - - $(function() { - $('nav[data-toggle="toc"]').each(function(i, el) { - var $nav = $(el); - Toc.init($nav); - }); - }); -})(); diff --git a/docs/docsearch.css b/docs/docsearch.css deleted file mode 100644 index e5f1fe1..0000000 --- a/docs/docsearch.css +++ /dev/null @@ -1,148 +0,0 @@ -/* Docsearch -------------------------------------------------------------- */ -/* - Source: https://github.com/algolia/docsearch/ - License: MIT -*/ - -.algolia-autocomplete { - display: block; - -webkit-box-flex: 1; - -ms-flex: 1; - flex: 1 -} - -.algolia-autocomplete .ds-dropdown-menu { - width: 100%; - min-width: none; - max-width: none; - padding: .75rem 0; - background-color: #fff; - background-clip: padding-box; - border: 1px solid rgba(0, 0, 0, .1); - box-shadow: 0 .5rem 1rem rgba(0, 0, 0, .175); -} - -@media (min-width:768px) { - .algolia-autocomplete .ds-dropdown-menu { - width: 175% - } -} - -.algolia-autocomplete .ds-dropdown-menu::before { - display: none -} - -.algolia-autocomplete .ds-dropdown-menu [class^=ds-dataset-] { - padding: 0; - background-color: rgb(255,255,255); - border: 0; - max-height: 80vh; -} - -.algolia-autocomplete .ds-dropdown-menu .ds-suggestions { - margin-top: 0 -} - -.algolia-autocomplete .algolia-docsearch-suggestion { - padding: 0; - overflow: visible -} - -.algolia-autocomplete .algolia-docsearch-suggestion--category-header { - padding: .125rem 1rem; - margin-top: 0; - font-size: 1.3em; - font-weight: 500; - color: #00008B; - border-bottom: 0 -} - -.algolia-autocomplete .algolia-docsearch-suggestion--wrapper { - float: none; 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- margin-top: -.25rem; - font-size: 0.8em; - font-weight: 400; - line-height: 1.25 -} - -.algolia-autocomplete .algolia-docsearch-footer { - width: 110px; - height: 20px; - z-index: 3; - margin-top: 10.66667px; - float: right; - font-size: 0; - line-height: 0; -} - -.algolia-autocomplete .algolia-docsearch-footer--logo { - background-image: url("data:image/svg+xml;utf8,"); - background-repeat: no-repeat; - background-position: 50%; - background-size: 100%; - overflow: hidden; - text-indent: -9000px; - width: 100%; - height: 100%; - display: block; - transform: translate(-8px); -} - -.algolia-autocomplete .algolia-docsearch-suggestion--highlight { - color: #FF8C00; - background: rgba(232, 189, 54, 0.1) -} - - -.algolia-autocomplete .algolia-docsearch-suggestion--text .algolia-docsearch-suggestion--highlight { - box-shadow: inset 0 -2px 0 0 rgba(105, 105, 105, .5) -} - -.algolia-autocomplete .ds-suggestion.ds-cursor .algolia-docsearch-suggestion--content { - background-color: rgba(192, 192, 192, .15) -} diff --git a/docs/docsearch.js b/docs/docsearch.js deleted file mode 100644 index b35504c..0000000 --- a/docs/docsearch.js +++ /dev/null @@ -1,85 +0,0 @@ -$(function() { - - // register a handler to move the focus to the search bar - // upon pressing shift + "/" (i.e. "?") - $(document).on('keydown', function(e) { - if (e.shiftKey && e.keyCode == 191) { - e.preventDefault(); - $("#search-input").focus(); - } - }); - - $(document).ready(function() { - // do keyword highlighting - /* modified from https://jsfiddle.net/julmot/bL6bb5oo/ */ - var mark = function() { - - var referrer = document.URL ; - var paramKey = "q" ; - - if (referrer.indexOf("?") !== -1) { - var qs = referrer.substr(referrer.indexOf('?') + 1); - var qs_noanchor = qs.split('#')[0]; - var qsa = qs_noanchor.split('&'); - var keyword = ""; - - for (var i = 0; i < qsa.length; i++) { - var currentParam = qsa[i].split('='); - - if (currentParam.length !== 2) { - continue; - } - - if (currentParam[0] == paramKey) { - keyword = decodeURIComponent(currentParam[1].replace(/\+/g, "%20")); - } - } - - if (keyword !== "") { - $(".contents").unmark({ - done: function() { - $(".contents").mark(keyword); - } - }); - } - } - }; - - mark(); - }); -}); - -/* Search term highlighting ------------------------------*/ - -function matchedWords(hit) { - var words = []; - - var hierarchy = hit._highlightResult.hierarchy; - // loop to fetch from lvl0, lvl1, etc. - for (var idx in hierarchy) { - words = words.concat(hierarchy[idx].matchedWords); - } - - var content = hit._highlightResult.content; - if (content) { - words = words.concat(content.matchedWords); - } - - // return unique words - var words_uniq = [...new Set(words)]; - return words_uniq; -} - -function updateHitURL(hit) { - - var words = matchedWords(hit); - var url = ""; - - if (hit.anchor) { - url = hit.url_without_anchor + '?q=' + escape(words.join(" ")) + '#' + hit.anchor; - } else { - url = hit.url + '?q=' + escape(words.join(" ")); - } - - return url; -} diff --git a/docs/index.html b/docs/index.html index c66e5f6..e173d68 100644 --- a/docs/index.html +++ b/docs/index.html @@ -76,12 +76,12 @@
    -

    BayesianReasoning +

    Bayesian reasoning

    - +

    Bayesian reasoning in medical contexts

    @@ -100,8 +100,8 @@

    Bayesian reasoning in medical co min_possible_prevalence(): Calculates how high should the prevalence of a disease be to reach a desired PPV given certain test parameters.
    -

    You can install the stable (CRAN) version of the package with install.packages("BayesianReasoning") or development version with remotes::install_github("gorkang/BayesianReasoning@dev"). Please report any problems you find in the Issues Github page.

    -

    There is a shiny app implementation with most of the main features of the PPV_heatmap() function available.

    +

    If you want to install the package can use: remotes::install_github("gorkang/BayesianReasoning"). Please report any problems you find in the Issues Github page.

    +

    There is a shiny app implementation with most of the main features available.

    @@ -119,27 +119,21 @@

    PPV_heatmap()
    -
-PPV_heatmap(min_Prevalence = 1,
-            max_Prevalence = 1000, 
-            Sensitivity = 100, 
-            Language = "en")
-

    -

    +
    PPV_heatmap(min_Prevalence = 1,
+            max_Prevalence = 1000, 
+            Sensitivity = 100, limits_Specificity = c(90, 100),
+            Language = "en")
    +

    NPV

    You can also plot an NPV heatmap with PPV_NPV = “NPV”.

    -
    -
-PPV_heatmap(PPV_NPV = "NPV",
-            min_Prevalence = 800,
-            max_Prevalence = 1000, 
-            Specificity = 95, 
-            Language = "en")
    -

    +
    PPV_heatmap(PPV_NPV = "NPV",
+            min_Prevalence = 800, max_Prevalence = 1000, 
+            Specificity = 95, limits_Sensitivity = c(90, 100),
+            Language = "en")
    +

    @@ -147,50 +141,54 @@

    Area overlay
    -
-PPV_heatmap(min_Prevalence = 1, max_Prevalence = 1200, 
-            Sensitivity = 81, 
-            limits_Specificity = c(94, 100),
-            label_subtitle = "Prenatal screening for Down Syndrome by Age",
-            overlay = "area",
-            overlay_labels = "40 y.o.",
-            overlay_position_FP = 4.8,
-            overlay_prevalence_1 = 1,
-            overlay_prevalence_2 = 68)

    -

    +
    PPV_heatmap(min_Prevalence = 1, max_Prevalence = 1200, 
+            Sensitivity = 81, 
+            limits_Specificity = c(94, 100),
+            label_subtitle = "Prenatal screening for Down Syndrome by Age",
+            overlay = "area",
+            overlay_labels = "40 y.o.",
+            overlay_position_FP = 4.8,
+            overlay_prevalence_1 = 1,
+            overlay_prevalence_2 = 68)
    +

    +

    The area plot overlay can show more details about how the calculation of PPV/NPV is performed:

    +
    PPV_heatmap(min_Prevalence = 1, max_Prevalence = 1200, 
+            Sensitivity = 81, 
+            limits_Specificity = c(94, 100),
+            label_subtitle = "Prenatal screening for Down Syndrome by Age", 
+            overlay_extra_info = TRUE,
+            overlay = "area",
+            overlay_labels = "40 y.o.",
+            overlay_position_FP = 4.8,
+            overlay_prevalence_1 = 1,
+            overlay_prevalence_2 = 68)
    +

    Line overlay

    Also, you can add a line overlay highlighting a range of prevalences and FP. This is useful, for example, to show how the PPV of a test changes with age:

    -
    -
-PPV_heatmap(min_Prevalence = 1, max_Prevalence = 1800, 
-            Sensitivity = 90, 
-            limits_Specificity = c(84, 100),
-            label_subtitle = "PPV of Mammogram for Breast Cancer by Age",
-            overlay = "line", 
-            overlay_labels = c("80 y.o.", "70 y.o.", "60 y.o.", "50 y.o.", "40 y.o.", "30 y.o.", "20  y.o."),
-            overlay_position_FP = c(6.5, 7, 8, 9, 12, 14, 14),
-            overlay_prevalence_1 = c(1, 1, 1, 1, 1, 1, 1),
-            overlay_prevalence_2 = c(22, 26, 29, 44, 69, 227, 1667))
-
    -

    +
    PPV_heatmap(min_Prevalence = 1, max_Prevalence = 1800, 
+            Sensitivity = 90, 
+            limits_Specificity = c(84, 100),
+            label_subtitle = "PPV of Mammogram for Breast Cancer by Age",
+            overlay = "line", 
+            overlay_labels = c("80 y.o.", "70 y.o.", "60 y.o.", "50 y.o.", "40 y.o.", "30 y.o.", "20  y.o."),
+            overlay_position_FP = c(6.5, 7, 8, 9, 12, 14, 14),
+            overlay_prevalence_1 = c(1, 1, 1, 1, 1, 1, 1),
+            overlay_prevalence_2 = c(22, 26, 29, 44, 69, 227, 1667))
    +

    Another example. In this case, the FP is constant across age:

    -
    -
-PPV_heatmap(min_Prevalence = 1, max_Prevalence = 2000, Sensitivity = 81, 
-            limits_Specificity = c(94, 100),
-            label_subtitle = "Prenatal screening for Down Syndrome by Age",
-            overlay = "line",
-            overlay_labels = c("40 y.o.", "30 y.o.", "20 y.o."),
-            overlay_position_FP = c(4.8, 4.8, 4.8),
-            overlay_prevalence_1 = c(1, 1, 1),
-            overlay_prevalence_2 = c(68, 626, 1068))
-
    -

    +
    PPV_heatmap(min_Prevalence = 1, max_Prevalence = 2000, Sensitivity = 81, 
+            limits_Specificity = c(94, 100),
+            label_subtitle = "Prenatal screening for Down Syndrome by Age",
+            overlay = "line",
+            overlay_labels = c("40 y.o.", "30 y.o.", "20 y.o."),
+            overlay_position_FP = c(4.8, 4.8, 4.8),
+            overlay_prevalence_1 = c(1, 1, 1),
+            overlay_prevalence_2 = c(68, 626, 1068))
    +

    @@ -199,14 +197,11 @@

    PPV_diagnostic_vs_screening()
    -
-PPV_diagnostic_vs_screening(max_FP = 10, 
-                            Sensitivity = 100, 
-                            prevalence_screening_group = 1000, 
-                            prevalence_diagnostic_group = 2)
-

    -

    +
    PPV_diagnostic_vs_screening(max_FP = 10, 
+                            Sensitivity = 100, 
+                            prevalence_screening_group = 1000, 
+                            prevalence_diagnostic_group = 2)
    +

    @@ -214,19 +209,17 @@

    min_possible_prevalence()

    Imagine you would like to use a test in a population and want to have a 98% PPV. That is, IF a positive result comes out in the test, you would like a 98% certainty that it is a true positive.

    How high should the prevalence of the disease be in that group?

    -
    -min_possible_prevalence(Sensitivity = 100, 
-                        FP_test = 0.1, 
-                        min_PPV_desired = 98)
    +
    min_possible_prevalence(Sensitivity = 100, 
+                        FP_test = 0.1, 
+                        min_PPV_desired = 98)

    To reach a PPV of 98 when using a test with 100 % Sensitivity and 0.1 % False Positive Rate, you need a prevalence of at least 1 out of 21

    Another example, with a very good test, and lower expectations:

    -
    -min_possible_prevalence(Sensitivity = 99.9, 
-                        FP_test = .1, 
-                        min_PPV_desired = 70)
    +
    min_possible_prevalence(Sensitivity = 99.9, 
+                        FP_test = .1, 
+                        min_PPV_desired = 70)

    To reach a PPV of 70 when using a test with 99.9 % Sensitivity and 0.1 % False Positive Rate, you need a prevalence of at least 1 out of 429

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-} -.hanging-indent div.csl-entry { - margin-left:2em; - text-indent:-2em; -} -div.csl-left-margin { - min-width:2em; - float:left; -} -div.csl-right-inline { - margin-left:2em; - padding-left:1em; -} -div.csl-indent { - margin-left: 2em; -} diff --git a/docs/pkgdown.yml b/docs/pkgdown.yml index d2ca312..0f572ba 100644 --- a/docs/pkgdown.yml +++ b/docs/pkgdown.yml @@ -4,5 +4,5 @@ pkgdown_sha: ~ articles: PPV_NPV: PPV_NPV.html introduction: introduction.html -last_built: 2021-12-20T14:17Z +last_built: 2021-12-21T16:08Z diff --git a/docs/reference/PPV_heatmap.html b/docs/reference/PPV_heatmap.html index 147a74e..bda6557 100644 --- a/docs/reference/PPV_heatmap.html +++ b/docs/reference/PPV_heatmap.html @@ -80,7 +80,8 @@

    Usage folder = "", PPV_NPV = "PPV", steps_matrix = 100, - DEBUG = FALSE + DEBUG = FALSE, + ... )

    @@ -130,6 +131,8 @@

    Arguments

    Value

    diff --git a/docs/reference/dot-get_point_ppv_npv.html b/docs/reference/dot-get_point_ppv_npv.html index e062e5f..02bcc94 100644 --- a/docs/reference/dot-get_point_ppv_npv.html +++ b/docs/reference/dot-get_point_ppv_npv.html @@ -94,7 +94,7 @@

    ArgumentsArguments