2020-03-07 21:48:21 +01:00
<!DOCTYPE html>
<!-- Generated by pkgdown: do not edit by hand --> < html lang = "en" >
< head >
< meta http-equiv = "Content-Type" content = "text/html; charset=UTF-8" >
< meta charset = "utf-8" >
< meta http-equiv = "X-UA-Compatible" content = "IE=edge" >
< meta name = "viewport" content = "width=device-width, initial-scale=1.0" >
< title > How to conduct principal component analysis (PCA) for AMR • AMR (for R)< / title >
<!-- favicons --> < link rel = "icon" type = "image/png" sizes = "16x16" href = "../favicon-16x16.png" >
< link rel = "icon" type = "image/png" sizes = "32x32" href = "../favicon-32x32.png" >
< link rel = "apple-touch-icon" type = "image/png" sizes = "180x180" href = "../apple-touch-icon.png" >
< link rel = "apple-touch-icon" type = "image/png" sizes = "120x120" href = "../apple-touch-icon-120x120.png" >
< link rel = "apple-touch-icon" type = "image/png" sizes = "76x76" href = "../apple-touch-icon-76x76.png" >
< link rel = "apple-touch-icon" type = "image/png" sizes = "60x60" href = "../apple-touch-icon-60x60.png" >
2020-04-13 21:09:56 +02:00
<!-- jquery --> < script src = "https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity = "sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin = "anonymous" > < / script > <!-- Bootstrap --> < link href = "https://cdnjs.cloudflare.com/ajax/libs/bootswatch/3.4.0/flatly/bootstrap.min.css" rel = "stylesheet" crossorigin = "anonymous" >
< script src = "https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity = "sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin = "anonymous" > < / script > <!-- bootstrap - toc --> < link rel = "stylesheet" href = "../bootstrap-toc.css" >
< script src = "../bootstrap-toc.js" > < / script > <!-- Font Awesome icons --> < link rel = "stylesheet" href = "https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity = "sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin = "anonymous" >
< link rel = "stylesheet" href = "https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity = "sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin = "anonymous" >
<!-- clipboard.js --> < script src = "https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity = "sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin = "anonymous" > < / script > <!-- headroom.js --> < script src = "https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity = "sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin = "anonymous" > < / script > < script src = "https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity = "sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin = "anonymous" > < / script > <!-- pkgdown --> < link href = "../pkgdown.css" rel = "stylesheet" >
2020-03-07 21:48:21 +01:00
< script src = "../pkgdown.js" > < / script > < link href = "../extra.css" rel = "stylesheet" >
< script src = "../extra.js" > < / script > < meta property = "og:title" content = "How to conduct principal component analysis (PCA) for AMR" >
2020-04-13 21:09:56 +02:00
< meta property = "og:description" content = "AMR" >
2020-07-09 14:12:11 +02:00
< meta property = "og:image" content = "https://msberends.github.io/AMR/logo.svg" >
2020-03-07 21:48:21 +01:00
<!-- mathjax --> < script src = "https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity = "sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin = "anonymous" > < / script > < script src = "https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity = "sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin = "anonymous" > < / script > <!-- [if lt IE 9]>
< script src = "https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js" > < / script >
< script src = "https://oss.maxcdn.com/respond/1.4.2/respond.min.js" > < / script >
<![endif]-->
< / head >
2020-04-13 21:09:56 +02:00
< body data-spy = "scroll" data-target = "#toc" >
2020-03-07 21:48:21 +01:00
< div class = "container template-article" >
< header > < div class = "navbar navbar-default navbar-fixed-top" role = "navigation" >
< div class = "container" >
< div class = "navbar-header" >
< button type = "button" class = "navbar-toggle collapsed" data-toggle = "collapse" data-target = "#navbar" aria-expanded = "false" >
< span class = "sr-only" > Toggle navigation< / span >
< span class = "icon-bar" > < / span >
< span class = "icon-bar" > < / span >
< span class = "icon-bar" > < / span >
< / button >
< span class = "navbar-brand" >
< a class = "navbar-link" href = "../index.html" > AMR (for R)< / a >
2020-09-03 20:59:21 +02:00
< span class = "version label label-default" data-toggle = "tooltip" data-placement = "bottom" title = "Latest development version" > 1.3.0.9015< / span >
2020-03-07 21:48:21 +01:00
< / span >
< / div >
< div id = "navbar" class = "navbar-collapse collapse" >
< ul class = "nav navbar-nav" >
< li >
< a href = "../index.html" >
< span class = "fa fa-home" > < / span >
Home
< / a >
< / li >
< li class = "dropdown" >
< a href = "#" class = "dropdown-toggle" data-toggle = "dropdown" role = "button" aria-expanded = "false" >
< span class = "fa fa-question-circle" > < / span >
How to
< span class = "caret" > < / span >
< / a >
< ul class = "dropdown-menu" role = "menu" >
< li >
< a href = "../articles/AMR.html" >
< span class = "fa fa-directions" > < / span >
Conduct AMR analysis
< / a >
< / li >
< li >
< a href = "../articles/resistance_predict.html" >
< span class = "fa fa-dice" > < / span >
Predict antimicrobial resistance
< / a >
< / li >
2020-08-17 21:49:58 +02:00
< li >
< a href = "../articles/datasets.html" >
< span class = "fa fa-database" > < / span >
2020-09-03 20:59:21 +02:00
Data sets for download / own use
2020-08-17 21:49:58 +02:00
< / a >
< / li >
2020-03-07 21:48:21 +01:00
< li >
< a href = "../articles/PCA.html" >
< span class = "fa fa-compress" > < / span >
Conduct principal component analysis for AMR
< / a >
< / li >
< li >
< a href = "../articles/MDR.html" >
< span class = "fa fa-skull-crossbones" > < / span >
Determine multi-drug resistance (MDR)
< / a >
< / li >
< li >
< a href = "../articles/WHONET.html" >
< span class = "fa fa-globe-americas" > < / span >
Work with WHONET data
< / a >
< / li >
< li >
< a href = "../articles/SPSS.html" >
< span class = "fa fa-file-upload" > < / span >
Import data from SPSS/SAS/Stata
< / a >
< / li >
< li >
< a href = "../articles/EUCAST.html" >
< span class = "fa fa-exchange-alt" > < / span >
Apply EUCAST rules
< / a >
< / li >
< li >
< a href = "../reference/mo_property.html" >
< span class = "fa fa-bug" > < / span >
Get properties of a microorganism
< / a >
< / li >
< li >
< a href = "../reference/ab_property.html" >
< span class = "fa fa-capsules" > < / span >
Get properties of an antibiotic
< / a >
< / li >
< li >
< a href = "../articles/benchmarks.html" >
< span class = "fa fa-shipping-fast" > < / span >
Other: benchmarks
< / a >
< / li >
< / ul >
< / li >
< li >
2020-07-28 18:39:57 +02:00
< a href = "../reference/index.html" >
2020-03-07 21:48:21 +01:00
< span class = "fa fa-book-open" > < / span >
Manual
< / a >
< / li >
< li >
< a href = "../authors.html" >
< span class = "fa fa-users" > < / span >
Authors
< / a >
< / li >
< li >
2020-07-28 18:39:57 +02:00
< a href = "../news/index.html" >
2020-03-07 21:48:21 +01:00
< span class = "far fa far fa-newspaper" > < / span >
Changelog
< / a >
< / li >
< / ul >
< ul class = "nav navbar-nav navbar-right" >
< li >
2020-07-09 14:12:11 +02:00
< a href = "https://github.com/msberends/AMR" >
< span class = "fab fa fab fa-github" > < / span >
2020-03-07 21:48:21 +01:00
Source Code
< / a >
< / li >
< li >
2020-07-28 18:39:57 +02:00
< a href = "../survey.html" >
< span class = "fa fa-clipboard-list" > < / span >
2020-03-07 21:48:21 +01:00
2020-07-28 18:39:57 +02:00
Survey
2020-03-07 21:48:21 +01:00
< / a >
< / li >
< / ul >
< / div >
<!-- /.nav - collapse -->
< / div >
<!-- /.container -->
< / div >
<!-- /.navbar -->
2020-07-09 14:12:11 +02:00
< / header > < script src = "PCA_files/accessible-code-block-0.0.1/empty-anchor.js" > < / script > < div class = "row" >
2020-03-07 21:48:21 +01:00
< div class = "col-md-9 contents" >
< div class = "page-header toc-ignore" >
2020-04-13 21:09:56 +02:00
< h1 data-toc-skip > How to conduct principal component analysis (PCA) for AMR< / h1 >
2020-03-07 21:48:21 +01:00
2020-07-09 14:12:11 +02:00
< small class = "dont-index" > Source: < a href = "https://github.com/msberends/AMR/blob/master/vignettes/PCA.Rmd" > < code > vignettes/PCA.Rmd< / code > < / a > < / small >
2020-03-07 21:48:21 +01:00
< div class = "hidden name" > < code > PCA.Rmd< / code > < / div >
< / div >
< p > < strong > NOTE: This page will be updated soon, as the pca() function is currently being developed.< / strong > < / p >
< div id = "introduction" class = "section level1" >
< h1 class = "hasAnchor" >
< a href = "#introduction" class = "anchor" > < / a > Introduction< / h1 >
< / div >
< div id = "transforming" class = "section level1" >
< h1 class = "hasAnchor" >
< a href = "#transforming" class = "anchor" > < / a > Transforming< / h1 >
< p > For PCA, we need to transform our AMR data first. This is what the < code > example_isolates< / code > data set in this package looks like:< / p >
2020-08-10 12:46:03 +02:00
< div class = "sourceCode" id = "cb1" > < pre class = "downlit" >
< span class = "fu" > < a href = "https://rdrr.io/r/base/library.html" > library< / a > < / span > (< span class = "kw" > < a href = "https://msberends.github.io/AMR" > AMR< / a > < / span > )
< span class = "fu" > < a href = "https://rdrr.io/r/base/library.html" > library< / a > < / span > (< span class = "kw" > < a href = "https://dplyr.tidyverse.org" > dplyr< / a > < / span > )
< span class = "fu" > < a href = "https://tibble.tidyverse.org/reference/glimpse.html" > glimpse< / a > < / span > (< span class = "kw" > example_isolates< / span > )
2020-04-13 21:09:56 +02:00
< span class = "co" > # Rows: 2,000< / span >
< span class = "co" > # Columns: 49< / span >
< span class = "co" > # $ date < date> 2002-01-02, 2002-01-03, 2002-01-07, 2002-01-07, 2002…< / span >
< span class = "co" > # $ hospital_id < fct> D, D, B, B, B, B, D, D, B, B, D, D, D, D, D, B, B, B,…< / span >
< span class = "co" > # $ ward_icu < lgl> FALSE, FALSE, TRUE, TRUE, TRUE, TRUE, FALSE, FALSE, T…< / span >
< span class = "co" > # $ ward_clinical < lgl> TRUE, TRUE, FALSE, FALSE, FALSE, FALSE, TRUE, TRUE, F…< / span >
< span class = "co" > # $ ward_outpatient < lgl> FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALS…< / span >
< span class = "co" > # $ age < dbl> 65, 65, 45, 45, 45, 45, 78, 78, 45, 79, 67, 67, 71, 7…< / span >
< span class = "co" > # $ gender < chr> "F", "F", "F", "F", "F", "F", "M", "M", "F", "F", "M"…< / span >
< span class = "co" > # $ patient_id < chr> "A77334", "A77334", "067927", "067927", "067927", "06…< / span >
2020-05-25 01:01:14 +02:00
< span class = "co" > # $ mo < mo> "B_ESCHR_COLI", "B_ESCHR_COLI", "B_STPHY_EPDR", "B_STP…< / span >
2020-09-03 20:59:21 +02:00
< span class = "co" > # $ PEN < rsi> R, R, R, R, R, R, R, R, R, R, R, R, R, R, R, R, R, R,…< / span >
< span class = "co" > # $ OXA < rsi> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…< / span >
< span class = "co" > # $ FLC < rsi> NA, NA, R, R, R, R, S, S, R, S, S, S, NA, NA, NA, NA,…< / span >
< span class = "co" > # $ AMX < rsi> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…< / span >
< span class = "co" > # $ AMC < rsi> I, I, NA, NA, NA, NA, S, S, NA, NA, S, S, I, I, R, I,…< / span >
< span class = "co" > # $ AMP < rsi> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…< / span >
< span class = "co" > # $ TZP < rsi> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…< / span >
< span class = "co" > # $ CZO < rsi> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…< / span >
< span class = "co" > # $ FEP < rsi> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…< / span >
< span class = "co" > # $ CXM < rsi> I, I, R, R, R, R, S, S, R, S, S, S, S, S, NA, S, S, R…< / span >
< span class = "co" > # $ FOX < rsi> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…< / span >
< span class = "co" > # $ CTX < rsi> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, S, S,…< / span >
< span class = "co" > # $ CAZ < rsi> NA, NA, R, R, R, R, R, R, R, R, R, R, NA, NA, NA, S, …< / span >
< span class = "co" > # $ CRO < rsi> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, S, S,…< / span >
< span class = "co" > # $ GEN < rsi> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…< / span >
< span class = "co" > # $ TOB < rsi> NA, NA, NA, NA, NA, NA, S, S, NA, NA, NA, NA, S, S, N…< / span >
< span class = "co" > # $ AMK < rsi> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…< / span >
< span class = "co" > # $ KAN < rsi> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…< / span >
< span class = "co" > # $ TMP < rsi> R, R, S, S, R, R, R, R, S, S, NA, NA, S, S, S, S, S, …< / span >
< span class = "co" > # $ SXT < rsi> R, R, S, S, NA, NA, NA, NA, S, S, NA, NA, S, S, S, S,…< / span >
< span class = "co" > # $ NIT < rsi> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…< / span >
< span class = "co" > # $ FOS < rsi> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…< / span >
< span class = "co" > # $ LNZ < rsi> R, R, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, R, R, R…< / span >
< span class = "co" > # $ CIP < rsi> NA, NA, NA, NA, NA, NA, NA, NA, S, S, NA, NA, NA, NA,…< / span >
< span class = "co" > # $ MFX < rsi> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…< / span >
< span class = "co" > # $ VAN < rsi> R, R, S, S, S, S, S, S, S, S, NA, NA, R, R, R, R, R, …< / span >
< span class = "co" > # $ TEC < rsi> R, R, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, R, R, R…< / span >
< span class = "co" > # $ TCY < rsi> R, R, S, S, S, S, S, S, S, I, S, S, NA, NA, I, R, R, …< / span >
< span class = "co" > # $ TGC < rsi> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…< / span >
< span class = "co" > # $ DOX < rsi> NA, NA, S, S, S, S, S, S, S, NA, S, S, NA, NA, NA, R,…< / span >
< span class = "co" > # $ ERY < rsi> R, R, R, R, R, R, S, S, R, S, S, S, R, R, R, R, R, R,…< / span >
< span class = "co" > # $ CLI < rsi> NA, NA, NA, NA, NA, R, NA, NA, NA, NA, NA, NA, NA, NA…< / span >
< span class = "co" > # $ AZM < rsi> R, R, R, R, R, R, S, S, R, S, S, S, R, R, R, R, R, R,…< / span >
< span class = "co" > # $ IPM < rsi> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, S, S,…< / span >
< span class = "co" > # $ MEM < rsi> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…< / span >
< span class = "co" > # $ MTR < rsi> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…< / span >
< span class = "co" > # $ CHL < rsi> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…< / span >
< span class = "co" > # $ COL < rsi> NA, NA, R, R, R, R, R, R, R, R, R, R, NA, NA, NA, R, …< / span >
< span class = "co" > # $ MUP < rsi> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…< / span >
< span class = "co" > # $ RIF < rsi> R, R, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, R, R, R…< / span >
2020-08-10 12:46:03 +02:00
< / pre > < / div >
2020-03-07 21:48:21 +01:00
< p > Now to transform this to a data set with only resistance percentages per taxonomic order and genus:< / p >
2020-08-10 12:46:03 +02:00
< div class = "sourceCode" id = "cb2" > < pre class = "downlit" >
< span class = "kw" > resistance_data< / span > < span class = "op" > < -< / span > < span class = "kw" > example_isolates< / span > < span class = "op" > %> %< / span >
< span class = "fu" > < a href = "https://dplyr.tidyverse.org/reference/group_by.html" > group_by< / a > < / span > (order = < span class = "fu" > < a href = "../reference/mo_property.html" > mo_order< / a > < / span > (< span class = "kw" > mo< / span > ), < span class = "co" > # group on anything, like order< / span >
genus = < span class = "fu" > < a href = "../reference/mo_property.html" > mo_genus< / a > < / span > (< span class = "kw" > mo< / span > )) < span class = "op" > %> %< / span > < span class = "co" > # and genus as we do here< / span >
< span class = "fu" > < a href = "https://dplyr.tidyverse.org/reference/summarise_all.html" > summarise_if< / a > < / span > (< span class = "kw" > is.rsi< / span > , < span class = "kw" > resistance< / span > ) < span class = "op" > %> %< / span > < span class = "co" > # then get resistance of all drugs< / span >
< span class = "fu" > < a href = "https://dplyr.tidyverse.org/reference/select.html" > select< / a > < / span > (< span class = "kw" > order< / span > , < span class = "kw" > genus< / span > , < span class = "kw" > AMC< / span > , < span class = "kw" > CXM< / span > , < span class = "kw" > CTX< / span > ,
< span class = "kw" > CAZ< / span > , < span class = "kw" > GEN< / span > , < span class = "kw" > TOB< / span > , < span class = "kw" > TMP< / span > , < span class = "kw" > SXT< / span > ) < span class = "co" > # and select only relevant columns< / span >
2020-04-13 21:09:56 +02:00
2020-08-10 12:46:03 +02:00
< span class = "fu" > < a href = "https://rdrr.io/r/utils/head.html" > head< / a > < / span > (< span class = "kw" > resistance_data< / span > )
2020-04-13 21:09:56 +02:00
< span class = "co" > # # A tibble: 6 x 10< / span >
< span class = "co" > # # Groups: order [2]< / span >
2020-05-28 10:51:56 +02:00
< span class = "co" > # order genus AMC CXM CTX CAZ GEN TOB TMP SXT< / span >
< span class = "co" > # < chr> < chr> < dbl> < dbl> < dbl> < dbl> < dbl> < dbl> < dbl> < dbl> < / span >
< span class = "co" > # 1 (unknown order) (unknown genu… NA NA NA NA NA NA NA NA< / span >
< span class = "co" > # 2 Actinomycetales Corynebacteri… NA NA NA NA NA NA NA NA< / span >
< span class = "co" > # 3 Actinomycetales Cutibacterium NA NA NA NA NA NA NA NA< / span >
< span class = "co" > # 4 Actinomycetales Dermabacter NA NA NA NA NA NA NA NA< / span >
< span class = "co" > # 5 Actinomycetales Micrococcus NA NA NA NA NA NA NA NA< / span >
2020-08-10 12:46:03 +02:00
< span class = "co" > # 6 Actinomycetales Rothia NA NA NA NA NA NA NA NA< / span >
< / pre > < / div >
2020-03-07 21:48:21 +01:00
< / div >
< div id = "perform-principal-component-analysis" class = "section level1" >
< h1 class = "hasAnchor" >
< a href = "#perform-principal-component-analysis" class = "anchor" > < / a > Perform principal component analysis< / h1 >
< p > The new < code > < a href = "../reference/pca.html" > pca()< / a > < / code > function will automatically filter on rows that contain numeric values in all selected variables, so we now only need to do:< / p >
2020-08-10 12:46:03 +02:00
< div class = "sourceCode" id = "cb3" > < pre class = "downlit" >
< span class = "kw" > pca_result< / span > < span class = "op" > < -< / span > < span class = "fu" > < a href = "../reference/pca.html" > pca< / a > < / span > (< span class = "kw" > resistance_data< / span > )
2020-05-25 01:01:14 +02:00
< span class = "co" > # NOTE: Columns selected for PCA: AMC CXM CTX CAZ GEN TOB TMP SXT.< / span >
2020-08-10 12:46:03 +02:00
< span class = "co" > # Total observations available: 7.< / span >
< / pre > < / div >
2020-03-07 21:48:21 +01:00
< p > The result can be reviewed with the good old < code > < a href = "https://rdrr.io/r/base/summary.html" > summary()< / a > < / code > function:< / p >
2020-08-10 12:46:03 +02:00
< div class = "sourceCode" id = "cb4" > < pre class = "downlit" >
< span class = "fu" > < a href = "https://rdrr.io/r/base/summary.html" > summary< / a > < / span > (< span class = "kw" > pca_result< / span > )
2020-04-13 21:09:56 +02:00
< span class = "co" > # Importance of components:< / span >
2020-05-28 10:51:56 +02:00
< span class = "co" > # PC1 PC2 PC3 PC4 PC5 PC6 PC7< / span >
< span class = "co" > # Standard deviation 2.154 1.6809 0.61305 0.33882 0.20755 0.03137 1.602e-16< / span >
< span class = "co" > # Proportion of Variance 0.580 0.3532 0.04698 0.01435 0.00538 0.00012 0.000e+00< / span >
2020-08-10 12:46:03 +02:00
< span class = "co" > # Cumulative Proportion 0.580 0.9332 0.98014 0.99449 0.99988 1.00000 1.000e+00< / span >
< / pre > < / div >
2020-05-28 10:51:56 +02:00
< p > Good news. The first two components explain a total of 93.3% of the variance (see the PC1 and PC2 values of the < em > Proportion of Variance< / em > . We can create a so-called biplot with the base R < code > < a href = "https://rdrr.io/r/stats/biplot.html" > biplot()< / a > < / code > function, to see which antimicrobial resistance per drug explain the difference per microorganism.< / p >
2020-03-07 21:48:21 +01:00
< / div >
< div id = "plotting-the-results" class = "section level1" >
< h1 class = "hasAnchor" >
< a href = "#plotting-the-results" class = "anchor" > < / a > Plotting the results< / h1 >
2020-08-10 12:46:03 +02:00
< div class = "sourceCode" id = "cb5" > < pre class = "downlit" >
< span class = "fu" > < a href = "https://rdrr.io/r/stats/biplot.html" > biplot< / a > < / span > (< span class = "kw" > pca_result< / span > )
< / pre > < / div >
2020-03-07 21:48:21 +01:00
< p > < img src = "PCA_files/figure-html/unnamed-chunk-5-1.png" width = "750" > < / p >
2020-04-13 21:09:56 +02:00
< p > But we can’ < code > < a href = "../reference/ggplot_pca.html" > ggplot_pca()< / a > < / code > function, that automatically adds the right labels and even groups:< / p >
2020-08-10 12:46:03 +02:00
< div class = "sourceCode" id = "cb6" > < pre class = "downlit" >
< span class = "fu" > < a href = "../reference/ggplot_pca.html" > ggplot_pca< / a > < / span > (< span class = "kw" > pca_result< / span > )
< / pre > < / div >
2020-03-07 21:48:21 +01:00
< p > < img src = "PCA_files/figure-html/unnamed-chunk-6-1.png" width = "750" > < / p >
< p > You can also print an ellipse per group, and edit the appearance:< / p >
2020-08-10 12:46:03 +02:00
< div class = "sourceCode" id = "cb7" > < pre class = "downlit" >
< span class = "fu" > < a href = "../reference/ggplot_pca.html" > ggplot_pca< / a > < / span > (< span class = "kw" > pca_result< / span > , ellipse = < span class = "fl" > TRUE< / span > ) < span class = "op" > +< / span >
< span class = "kw" > ggplot2< / span > ::< span class = "fu" > < a href = "https://ggplot2.tidyverse.org/reference/labs.html" > labs< / a > < / span > (title = < span class = "st" > "An AMR/PCA biplot!"< / span > )
< / pre > < / div >
2020-03-07 21:48:21 +01:00
< p > < img src = "PCA_files/figure-html/unnamed-chunk-7-1.png" width = "750" > < / p >
< / div >
< / div >
2020-04-13 21:09:56 +02:00
< div class = "col-md-3 hidden-xs hidden-sm" id = "pkgdown-sidebar" >
2020-03-07 21:48:21 +01:00
2020-04-13 21:09:56 +02:00
< nav id = "toc" data-toggle = "toc" > < h2 data-toc-skip > Contents< / h2 >
< / nav >
2020-03-07 21:48:21 +01:00
< / div >
< / div >
< footer > < div class = "copyright" >
< p > Developed by < a href = "https://www.rug.nl/staff/m.s.berends/" > Matthijs S. Berends< / a > , < a href = "https://www.rug.nl/staff/c.f.luz/" > Christian F. Luz< / a > , < a href = "https://www.rug.nl/staff/a.w.friedrich/" > Alexander W. Friedrich< / a > , < a href = "https://www.rug.nl/staff/b.sinha/" > Bhanu N. M. Sinha< / a > , < a href = "https://www.rug.nl/staff/c.j.albers/" > Casper J. Albers< / a > , < a href = "https://www.rug.nl/staff/c.glasner/" > Corinna Glasner< / a > .< / p >
< / div >
< div class = "pkgdown" >
2020-08-10 12:46:03 +02:00
< p > Site built with < a href = "https://pkgdown.r-lib.org/" > pkgdown< / a > 1.5.1.9000.< / p >
2020-03-07 21:48:21 +01:00
< / div >
< / footer >
< / div >
< / body >
< / html >
