2019-02-11 10:27:10 +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 predict antimicrobial resistance • 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 > <!-- Font Awesome icons --> < link rel = "stylesheet" href = "https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.11.1/css/all.min.css" integrity = "sha256-PbSmjxuVAzJ6FPvNYsrXygfGhNJYyZ2GktDbkMBqQZg=" crossorigin = "anonymous" >
< link rel = "stylesheet" href = "https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.11.1/css/v4-shims.min.css" integrity = "sha256-A6jcAdwFD48VMjlI3GDxUd+eCQa7/KWy6G9oe/ovaPA=" crossorigin = "anonymous" >
2019-10-13 09:31:58 +02:00
<!-- clipboard.js --> < script src = "https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.4/clipboard.min.js" integrity = "sha256-FiZwavyI2V6+EXO1U+xzLG3IKldpiTFf3153ea9zikQ=" crossorigin = "anonymous" > < / script > <!-- headroom.js --> < script src = "https://cdnjs.cloudflare.com/ajax/libs/headroom/0.9.4/headroom.min.js" integrity = "sha256-DJFC1kqIhelURkuza0AvYal5RxMtpzLjFhsnVIeuk+U=" crossorigin = "anonymous" > < / script > < script src = "https://cdnjs.cloudflare.com/ajax/libs/headroom/0.9.4/jQuery.headroom.min.js" integrity = "sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin = "anonymous" > < / script > <!-- pkgdown --> < link href = "../pkgdown.css" rel = "stylesheet" >
2020-02-23 20:56:11 +01:00
< script src = "../pkgdown.js" > < / script > < link href = "../extra.css" rel = "stylesheet" >
2019-02-11 10:27:10 +01:00
< script src = "../extra.js" > < / script > < meta property = "og:title" content = "How to predict antimicrobial resistance" >
2020-04-13 21:09:56 +02:00
< meta property = "og:description" content = "AMR" >
< meta property = "og:image" content = "/logo.svg" >
2019-02-11 10:27:10 +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 >
< body >
< 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 >
2020-04-13 21:09:56 +02:00
< span class = "version label label-default" data-toggle = "tooltip" data-placement = "bottom" title = "Latest development version" > 1.0.1.9004< / span >
2019-02-11 10:27:10 +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 >
2020-04-13 21:09:56 +02:00
< li >
< a href = "../articles/PCA.html" >
< span class = "fa fa-compress" > < / span >
Conduct principal component analysis for AMR
< / a >
< / li >
2019-05-23 16:58:59 +02:00
< li >
< a href = "../articles/MDR.html" >
< span class = "fa fa-skull-crossbones" > < / span >
Determine multi-drug resistance (MDR)
< / a >
< / li >
2019-02-11 10:27:10 +01:00
< li >
< a href = "../articles/WHONET.html" >
< span class = "fa fa-globe-americas" > < / span >
Work with WHONET data
< / a >
< / li >
2019-02-14 15:18:17 +01:00
< li >
< a href = "../articles/SPSS.html" >
< span class = "fa fa-file-upload" > < / span >
Import data from SPSS/SAS/Stata
< / a >
< / li >
2019-02-11 10:27:10 +01:00
< 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 >
2019-05-10 16:44:59 +02:00
< a href = "../reference/ab_property.html" >
2019-02-11 10:27:10 +01:00
< 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 >
< a href = "../reference/" >
< 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 >
< a href = "../news/" >
< span class = "far fa far fa-newspaper" > < / span >
Changelog
< / a >
< / li >
< / ul >
< ul class = "nav navbar-nav navbar-right" >
< li >
< a href = "https://gitlab.com/msberends/AMR" >
< span class = "fab fa fab fa-gitlab" > < / span >
Source Code
< / a >
< / li >
< li >
< a href = "../LICENSE-text.html" >
< span class = "fa fa-book" > < / span >
Licence
< / a >
< / li >
< / ul >
< / div >
<!-- /.nav - collapse -->
< / div >
<!-- /.container -->
< / div >
<!-- /.navbar -->
2019-10-13 09:31:58 +02:00
2019-02-11 10:27:10 +01:00
< / header > < div class = "row" >
< div class = "col-md-9 contents" >
< div class = "page-header toc-ignore" >
< h1 > How to predict antimicrobial resistance< / h1 >
< h4 class = "author" > Matthijs S. Berends< / h4 >
2020-04-13 21:09:56 +02:00
< h4 class = "date" > 17 March 2020< / h4 >
2019-02-11 10:27:10 +01:00
< div class = "hidden name" > < code > resistance_predict.Rmd< / code > < / div >
< / div >
< div id = "needed-r-packages" class = "section level2" >
< h2 class = "hasAnchor" >
< a href = "#needed-r-packages" class = "anchor" > < / a > Needed R packages< / h2 >
2019-06-02 19:23:19 +02:00
< p > As with many uses in R, we need some additional packages for AMR analysis. Our package works closely together with the < a href = "https://www.tidyverse.org" > tidyverse packages< / a > < a href = "https://dplyr.tidyverse.org/" > < code > dplyr< / code > < / a > and < a href = "https://ggplot2.tidyverse.org" > < code > ggplot2< / code > < / a > by Dr Hadley Wickham. The tidyverse tremendously improves the way we conduct data science - it allows for a very natural way of writing syntaxes and creating beautiful plots in R.< / p >
2019-02-11 10:27:10 +01:00
< p > Our < code > AMR< / code > package depends on these packages and even extends their use and functions.< / p >
2020-02-23 20:56:11 +01:00
< div class = "sourceCode" id = "cb1" > < pre class = "sourceCode r" > < code class = "sourceCode r" > < span id = "cb1-1" > < a href = "#cb1-1" > < / a > < span class = "kw" > < a href = "https://rdrr.io/r/base/library.html" > library< / a > < / span > (dplyr)< / span >
< span id = "cb1-2" > < a href = "#cb1-2" > < / a > < span class = "kw" > < a href = "https://rdrr.io/r/base/library.html" > library< / a > < / span > (ggplot2)< / span >
< span id = "cb1-3" > < a href = "#cb1-3" > < / a > < span class = "kw" > < a href = "https://rdrr.io/r/base/library.html" > library< / a > < / span > (AMR)< / span >
< span id = "cb1-4" > < a href = "#cb1-4" > < / a > < / span >
< span id = "cb1-5" > < a href = "#cb1-5" > < / a > < span class = "co" > # (if not yet installed, install with:)< / span > < / span >
< span id = "cb1-6" > < a href = "#cb1-6" > < / a > < span class = "co" > # install.packages(c("tidyverse", "AMR"))< / span > < / span > < / code > < / pre > < / div >
2019-02-11 10:27:10 +01:00
< / div >
< div id = "prediction-analysis" class = "section level2" >
< h2 class = "hasAnchor" >
< a href = "#prediction-analysis" class = "anchor" > < / a > Prediction analysis< / h2 >
< p > Our package contains a function < code > < a href = "../reference/resistance_predict.html" > resistance_predict()< / a > < / code > , which takes the same input as functions for < a href = "./AMR.html" > other AMR analysis< / a > . Based on a date column, it calculates cases per year and uses a regression model to predict antimicrobial resistance.< / p >
< p > It is basically as easy as:< / p >
2020-02-23 20:56:11 +01:00
< div class = "sourceCode" id = "cb2" > < pre class = "sourceCode r" > < code class = "sourceCode r" > < span id = "cb2-1" > < a href = "#cb2-1" > < / a > < span class = "co" > # resistance prediction of piperacillin/tazobactam (TZP):< / span > < / span >
< span id = "cb2-2" > < a href = "#cb2-2" > < / a > < span class = "kw" > < a href = "../reference/resistance_predict.html" > resistance_predict< / a > < / span > (< span class = "dt" > tbl =< / span > example_isolates, < span class = "dt" > col_date =< / span > < span class = "st" > "date"< / span > , < span class = "dt" > col_ab =< / span > < span class = "st" > "TZP"< / span > , < span class = "dt" > model =< / span > < span class = "st" > "binomial"< / span > )< / span >
< span id = "cb2-3" > < a href = "#cb2-3" > < / a > < / span >
< span id = "cb2-4" > < a href = "#cb2-4" > < / a > < span class = "co" > # or:< / span > < / span >
< span id = "cb2-5" > < a href = "#cb2-5" > < / a > example_isolates < span class = "op" > %> %< / span > < span class = "st" > < / span > < / span >
< span id = "cb2-6" > < a href = "#cb2-6" > < / a > < span class = "st" > < / span > < span class = "kw" > < a href = "../reference/resistance_predict.html" > resistance_predict< / a > < / span > (< span class = "dt" > col_ab =< / span > < span class = "st" > "TZP"< / span > ,< / span >
< span id = "cb2-7" > < a href = "#cb2-7" > < / a > model < span class = "st" > "binomial"< / span > )< / span >
< span id = "cb2-8" > < a href = "#cb2-8" > < / a > < / span >
< span id = "cb2-9" > < a href = "#cb2-9" > < / a > < span class = "co" > # to bind it to object 'predict_TZP' for example:< / span > < / span >
< span id = "cb2-10" > < a href = "#cb2-10" > < / a > predict_TZP < -< span class = "st" > < / span > example_isolates < span class = "op" > %> %< / span > < span class = "st" > < / span > < / span >
< span id = "cb2-11" > < a href = "#cb2-11" > < / a > < span class = "st" > < / span > < span class = "kw" > < a href = "../reference/resistance_predict.html" > resistance_predict< / a > < / span > (< span class = "dt" > col_ab =< / span > < span class = "st" > "TZP"< / span > ,< / span >
< span id = "cb2-12" > < a href = "#cb2-12" > < / a > < span class = "dt" > model =< / span > < span class = "st" > "binomial"< / span > )< / span > < / code > < / pre > < / div >
2019-02-11 10:27:10 +01:00
< p > The function will look for a date column itself if < code > col_date< / code > is not set.< / p >
< p > When running any of these commands, a summary of the regression model will be printed unless using < code > < a href = "../reference/resistance_predict.html" > resistance_predict(..., info = FALSE)< / a > < / code > .< / p >
2020-02-23 20:56:11 +01:00
< pre > < code > # NOTE: Using column `date` as input for `col_date`.< / code > < / pre >
2019-02-11 10:27:10 +01:00
< p > This text is only a printed summary - the actual result (output) of the function is a < code > data.frame< / code > containing for each year: the number of observations, the actual observed resistance, the estimated resistance and the standard error below and above the estimation:< / p >
2020-02-23 20:56:11 +01:00
< div class = "sourceCode" id = "cb4" > < pre class = "sourceCode r" > < code class = "sourceCode r" > < span id = "cb4-1" > < a href = "#cb4-1" > < / a > predict_TZP< / span >
< span id = "cb4-2" > < a href = "#cb4-2" > < / a > < span class = "co" > # year value se_min se_max observations observed estimated< / span > < / span >
< span id = "cb4-3" > < a href = "#cb4-3" > < / a > < span class = "co" > # 1 2003 0.06250000 NA NA 32 0.06250000 0.05486389< / span > < / span >
< span id = "cb4-4" > < a href = "#cb4-4" > < / a > < span class = "co" > # 2 2004 0.08536585 NA NA 82 0.08536585 0.06089002< / span > < / span >
< span id = "cb4-5" > < a href = "#cb4-5" > < / a > < span class = "co" > # 3 2005 0.05000000 NA NA 60 0.05000000 0.06753075< / span > < / span >
< span id = "cb4-6" > < a href = "#cb4-6" > < / a > < span class = "co" > # 4 2006 0.05084746 NA NA 59 0.05084746 0.07483801< / span > < / span >
< span id = "cb4-7" > < a href = "#cb4-7" > < / a > < span class = "co" > # 5 2007 0.12121212 NA NA 66 0.12121212 0.08286570< / span > < / span >
< span id = "cb4-8" > < a href = "#cb4-8" > < / a > < span class = "co" > # 6 2008 0.04166667 NA NA 72 0.04166667 0.09166918< / span > < / span >
< span id = "cb4-9" > < a href = "#cb4-9" > < / a > < span class = "co" > # 7 2009 0.01639344 NA NA 61 0.01639344 0.10130461< / span > < / span >
< span id = "cb4-10" > < a href = "#cb4-10" > < / a > < span class = "co" > # 8 2010 0.05660377 NA NA 53 0.05660377 0.11182814< / span > < / span >
< span id = "cb4-11" > < a href = "#cb4-11" > < / a > < span class = "co" > # 9 2011 0.18279570 NA NA 93 0.18279570 0.12329488< / span > < / span >
< span id = "cb4-12" > < a href = "#cb4-12" > < / a > < span class = "co" > # 10 2012 0.30769231 NA NA 65 0.30769231 0.13575768< / span > < / span >
< span id = "cb4-13" > < a href = "#cb4-13" > < / a > < span class = "co" > # 11 2013 0.06896552 NA NA 58 0.06896552 0.14926576< / span > < / span >
< span id = "cb4-14" > < a href = "#cb4-14" > < / a > < span class = "co" > # 12 2014 0.10000000 NA NA 60 0.10000000 0.16386307< / span > < / span >
< span id = "cb4-15" > < a href = "#cb4-15" > < / a > < span class = "co" > # 13 2015 0.23636364 NA NA 55 0.23636364 0.17958657< / span > < / span >
< span id = "cb4-16" > < a href = "#cb4-16" > < / a > < span class = "co" > # 14 2016 0.22619048 NA NA 84 0.22619048 0.19646431< / span > < / span >
< span id = "cb4-17" > < a href = "#cb4-17" > < / a > < span class = "co" > # 15 2017 0.16279070 NA NA 86 0.16279070 0.21451350< / span > < / span >
< span id = "cb4-18" > < a href = "#cb4-18" > < / a > < span class = "co" > # 16 2018 0.23373852 0.2021578 0.2653193 NA NA 0.23373852< / span > < / span >
< span id = "cb4-19" > < a href = "#cb4-19" > < / a > < span class = "co" > # 17 2019 0.25412909 0.2168525 0.2914057 NA NA 0.25412909< / span > < / span >
< span id = "cb4-20" > < a href = "#cb4-20" > < / a > < span class = "co" > # 18 2020 0.27565854 0.2321869 0.3191302 NA NA 0.27565854< / span > < / span >
< span id = "cb4-21" > < a href = "#cb4-21" > < / a > < span class = "co" > # 19 2021 0.29828252 0.2481942 0.3483709 NA NA 0.29828252< / span > < / span >
< span id = "cb4-22" > < a href = "#cb4-22" > < / a > < span class = "co" > # 20 2022 0.32193804 0.2649008 0.3789753 NA NA 0.32193804< / span > < / span >
< span id = "cb4-23" > < a href = "#cb4-23" > < / a > < span class = "co" > # 21 2023 0.34654311 0.2823269 0.4107593 NA NA 0.34654311< / span > < / span >
< span id = "cb4-24" > < a href = "#cb4-24" > < / a > < span class = "co" > # 22 2024 0.37199700 0.3004860 0.4435080 NA NA 0.37199700< / span > < / span >
< span id = "cb4-25" > < a href = "#cb4-25" > < / a > < span class = "co" > # 23 2025 0.39818127 0.3193839 0.4769787 NA NA 0.39818127< / span > < / span >
< span id = "cb4-26" > < a href = "#cb4-26" > < / a > < span class = "co" > # 24 2026 0.42496142 0.3390173 0.5109056 NA NA 0.42496142< / span > < / span >
< span id = "cb4-27" > < a href = "#cb4-27" > < / a > < span class = "co" > # 25 2027 0.45218939 0.3593720 0.5450068 NA NA 0.45218939< / span > < / span >
< span id = "cb4-28" > < a href = "#cb4-28" > < / a > < span class = "co" > # 26 2028 0.47970658 0.3804212 0.5789920 NA NA 0.47970658< / span > < / span >
< span id = "cb4-29" > < a href = "#cb4-29" > < / a > < span class = "co" > # 27 2029 0.50734745 0.4021241 0.6125708 NA NA 0.50734745< / span > < / span >
< span id = "cb4-30" > < a href = "#cb4-30" > < / a > < span class = "co" > # 28 2030 0.53494347 0.4244247 0.6454622 NA NA 0.53494347< / span > < / span > < / code > < / pre > < / div >
2019-02-11 10:27:10 +01:00
< p > The function < code > plot< / code > is available in base R, and can be extended by other packages to depend the output based on the type of input. We extended its function to cope with resistance predictions:< / p >
2020-02-23 20:56:11 +01:00
< div class = "sourceCode" id = "cb5" > < pre class = "sourceCode r" > < code class = "sourceCode r" > < span id = "cb5-1" > < a href = "#cb5-1" > < / a > < span class = "kw" > < a href = "https://rdrr.io/r/graphics/plot.html" > plot< / a > < / span > (predict_TZP)< / span > < / code > < / pre > < / div >
2019-02-11 10:27:10 +01:00
< p > < img src = "resistance_predict_files/figure-html/unnamed-chunk-4-1.png" width = "720" > < / p >
< p > This is the fastest way to plot the result. It automatically adds the right axes, error bars, titles, number of available observations and type of model.< / p >
< p > We also support the < code > ggplot2< / code > package with our custom function < code > < a href = "../reference/resistance_predict.html" > ggplot_rsi_predict()< / a > < / code > to create more appealing plots:< / p >
2020-02-23 20:56:11 +01:00
< div class = "sourceCode" id = "cb6" > < pre class = "sourceCode r" > < code class = "sourceCode r" > < span id = "cb6-1" > < a href = "#cb6-1" > < / a > < span class = "kw" > < a href = "../reference/resistance_predict.html" > ggplot_rsi_predict< / a > < / span > (predict_TZP)< / span > < / code > < / pre > < / div >
2019-02-11 10:27:10 +01:00
< p > < img src = "resistance_predict_files/figure-html/unnamed-chunk-5-1.png" width = "720" > < / p >
2020-02-23 20:56:11 +01:00
< div class = "sourceCode" id = "cb7" > < pre class = "sourceCode r" > < code class = "sourceCode r" > < span id = "cb7-1" > < a href = "#cb7-1" > < / a > < / span >
< span id = "cb7-2" > < a href = "#cb7-2" > < / a > < span class = "co" > # choose for error bars instead of a ribbon< / span > < / span >
< span id = "cb7-3" > < a href = "#cb7-3" > < / a > < span class = "kw" > < a href = "../reference/resistance_predict.html" > ggplot_rsi_predict< / a > < / span > (predict_TZP, < span class = "dt" > ribbon =< / span > < span class = "ot" > FALSE< / span > )< / span > < / code > < / pre > < / div >
2019-02-11 10:27:10 +01:00
< p > < img src = "resistance_predict_files/figure-html/unnamed-chunk-5-2.png" width = "720" > < / p >
< div id = "choosing-the-right-model" class = "section level3" >
< h3 class = "hasAnchor" >
< a href = "#choosing-the-right-model" class = "anchor" > < / a > Choosing the right model< / h3 >
< p > Resistance is not easily predicted; if we look at vancomycin resistance in Gram positives, the spread (i.e. < / p >
2020-02-23 20:56:11 +01:00
< div class = "sourceCode" id = "cb8" > < pre class = "sourceCode r" > < code class = "sourceCode r" > < span id = "cb8-1" > < a href = "#cb8-1" > < / a > example_isolates < span class = "op" > %> %< / span > < / span >
< span id = "cb8-2" > < a href = "#cb8-2" > < / a > < span class = "st" > < / span > < span class = "kw" > < a href = "https://dplyr.tidyverse.org/reference/filter.html" > filter< / a > < / span > (< span class = "kw" > < a href = "../reference/mo_property.html" > mo_gramstain< / a > < / span > (mo, < span class = "dt" > language =< / span > < span class = "ot" > NULL< / span > ) < span class = "op" > ==< / span > < span class = "st" > "Gram-positive"< / span > ) < span class = "op" > %> %< / span > < / span >
< span id = "cb8-3" > < a href = "#cb8-3" > < / a > < span class = "st" > < / span > < span class = "kw" > < a href = "../reference/resistance_predict.html" > resistance_predict< / a > < / span > (< span class = "dt" > col_ab =< / span > < span class = "st" > "VAN"< / span > , < span class = "dt" > year_min =< / span > < span class = "dv" > 2010< / span > , < span class = "dt" > info =< / span > < span class = "ot" > FALSE< / span > , < span class = "dt" > model =< / span > < span class = "st" > "binomial"< / span > ) < span class = "op" > %> %< / span > < span class = "st" > < / span > < / span >
< span id = "cb8-4" > < a href = "#cb8-4" > < / a > < span class = "st" > < / span > < span class = "kw" > < a href = "../reference/resistance_predict.html" > ggplot_rsi_predict< / a > < / span > ()< / span >
< span id = "cb8-5" > < a href = "#cb8-5" > < / a > < span class = "co" > # < / span > < span class = "al" > NOTE< / span > < span class = "co" > : Using column `date` as input for `col_date`.< / span > < / span > < / code > < / pre > < / div >
2019-02-11 10:27:10 +01:00
< p > < img src = "resistance_predict_files/figure-html/unnamed-chunk-6-1.png" width = "720" > < / p >
< p > Vancomycin resistance could be 100% in ten years, but might also stay around 0%.< / p >
2019-08-08 15:52:07 +02:00
< p > You can define the model with the < code > model< / code > parameter. The model chosen above is a generalised linear regression model using a binomial distribution, assuming that a period of zero resistance was followed by a period of increasing resistance leading slowly to more and more resistance.< / p >
2019-02-11 10:27:10 +01:00
< p > Valid values are:< / p >
< table class = "table" >
< colgroup >
< col width = "32%" >
< col width = "25%" >
< col width = "42%" >
< / colgroup >
< thead > < tr class = "header" >
< th > Input values< / th >
< th > Function used by R< / th >
< th > Type of model< / th >
< / tr > < / thead >
< tbody >
< tr class = "odd" >
< td >
< code > "binomial"< / code > or < code > "binom"< / code > or < code > "logit"< / code >
< / td >
2019-10-13 09:31:58 +02:00
< td > < code > < a href = "https://rdrr.io/r/stats/glm.html" > glm(..., family = binomial)< / a > < / code > < / td >
2019-02-11 10:27:10 +01:00
< td > Generalised linear model with binomial distribution< / td >
< / tr >
< tr class = "even" >
< td >
< code > "loglin"< / code > or < code > "poisson"< / code >
< / td >
2019-10-13 09:31:58 +02:00
< td > < code > < a href = "https://rdrr.io/r/stats/glm.html" > glm(..., family = poisson)< / a > < / code > < / td >
2019-02-11 10:27:10 +01:00
< td > Generalised linear model with poisson distribution< / td >
< / tr >
< tr class = "odd" >
< td >
< code > "lin"< / code > or < code > "linear"< / code >
< / td >
2019-10-13 09:31:58 +02:00
< td > < code > < a href = "https://rdrr.io/r/stats/lm.html" > lm()< / a > < / code > < / td >
2019-02-11 10:27:10 +01:00
< td > Linear model< / td >
< / tr >
< / tbody >
< / table >
< p > For the vancomycin resistance in Gram positive bacteria, a linear model might be more appropriate since no (left half of a) binomial distribution is to be expected based on the observed years:< / p >
2020-02-23 20:56:11 +01:00
< div class = "sourceCode" id = "cb9" > < pre class = "sourceCode r" > < code class = "sourceCode r" > < span id = "cb9-1" > < a href = "#cb9-1" > < / a > example_isolates < span class = "op" > %> %< / span > < / span >
< span id = "cb9-2" > < a href = "#cb9-2" > < / a > < span class = "st" > < / span > < span class = "kw" > < a href = "https://dplyr.tidyverse.org/reference/filter.html" > filter< / a > < / span > (< span class = "kw" > < a href = "../reference/mo_property.html" > mo_gramstain< / a > < / span > (mo, < span class = "dt" > language =< / span > < span class = "ot" > NULL< / span > ) < span class = "op" > ==< / span > < span class = "st" > "Gram-positive"< / span > ) < span class = "op" > %> %< / span > < / span >
< span id = "cb9-3" > < a href = "#cb9-3" > < / a > < span class = "st" > < / span > < span class = "kw" > < a href = "../reference/resistance_predict.html" > resistance_predict< / a > < / span > (< span class = "dt" > col_ab =< / span > < span class = "st" > "VAN"< / span > , < span class = "dt" > year_min =< / span > < span class = "dv" > 2010< / span > , < span class = "dt" > info =< / span > < span class = "ot" > FALSE< / span > , < span class = "dt" > model =< / span > < span class = "st" > "linear"< / span > ) < span class = "op" > %> %< / span > < span class = "st" > < / span > < / span >
< span id = "cb9-4" > < a href = "#cb9-4" > < / a > < span class = "st" > < / span > < span class = "kw" > < a href = "../reference/resistance_predict.html" > ggplot_rsi_predict< / a > < / span > ()< / span >
< span id = "cb9-5" > < a href = "#cb9-5" > < / a > < span class = "co" > # < / span > < span class = "al" > NOTE< / span > < span class = "co" > : Using column `date` as input for `col_date`.< / span > < / span > < / code > < / pre > < / div >
2019-02-11 10:27:10 +01:00
< p > < img src = "resistance_predict_files/figure-html/unnamed-chunk-7-1.png" width = "720" > < / p >
< p > This seems more likely, doesn’ < / p >
< p > The model itself is also available from the object, as an < code > attribute< / code > :< / p >
2020-02-23 20:56:11 +01:00
< div class = "sourceCode" id = "cb10" > < pre class = "sourceCode r" > < code class = "sourceCode r" > < span id = "cb10-1" > < a href = "#cb10-1" > < / a > model < -< span class = "st" > < / span > < span class = "kw" > < a href = "https://rdrr.io/r/base/attributes.html" > attributes< / a > < / span > (predict_TZP)< span class = "op" > $< / span > model< / span >
< span id = "cb10-2" > < a href = "#cb10-2" > < / a > < / span >
< span id = "cb10-3" > < a href = "#cb10-3" > < / a > < span class = "kw" > < a href = "https://rdrr.io/r/base/summary.html" > summary< / a > < / span > (model)< span class = "op" > $< / span > family< / span >
< span id = "cb10-4" > < a href = "#cb10-4" > < / a > < span class = "co" > # < / span > < / span >
< span id = "cb10-5" > < a href = "#cb10-5" > < / a > < span class = "co" > # Family: binomial < / span > < / span >
< span id = "cb10-6" > < a href = "#cb10-6" > < / a > < span class = "co" > # Link function: logit< / span > < / span >
< span id = "cb10-7" > < a href = "#cb10-7" > < / a > < / span >
< span id = "cb10-8" > < a href = "#cb10-8" > < / a > < span class = "kw" > < a href = "https://rdrr.io/r/base/summary.html" > summary< / a > < / span > (model)< span class = "op" > $< / span > coefficients< / span >
< span id = "cb10-9" > < a href = "#cb10-9" > < / a > < span class = "co" > # Estimate Std. Error z value Pr(> |z|)< / span > < / span >
< span id = "cb10-10" > < a href = "#cb10-10" > < / a > < span class = "co" > # (Intercept) -224.3987194 48.0335384 -4.671709 2.987038e-06< / span > < / span >
< span id = "cb10-11" > < a href = "#cb10-11" > < / a > < span class = "co" > # year 0.1106102 0.0238753 4.632831 3.606990e-06< / span > < / span > < / code > < / pre > < / div >
2019-02-11 10:27:10 +01:00
< / div >
< / div >
< / div >
2020-04-13 21:09:56 +02:00
< div class = "col-md-3 hidden-xs hidden-sm" id = "pkgdown-sidebar" >
2019-10-13 09:31:58 +02:00
2019-02-11 10:27:10 +01:00
< div id = "tocnav" >
< h2 class = "hasAnchor" >
< a href = "#tocnav" class = "anchor" > < / a > Contents< / h2 >
< ul class = "nav nav-pills nav-stacked" >
< li > < a href = "#needed-r-packages" > Needed R packages< / a > < / li >
< li > < a href = "#prediction-analysis" > Prediction analysis< / a > < / li >
< / ul >
< / div >
< / div >
< / div >
2019-10-13 09:31:58 +02:00
2019-02-11 10:27:10 +01:00
< footer > < div class = "copyright" >
2020-01-26 20:38:54 +01:00
< 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 >
2019-02-11 10:27:10 +01:00
< / div >
< div class = "pkgdown" >
2020-04-13 21:09:56 +02:00
< p > Site built with < a href = "https://pkgdown.r-lib.org/" > pkgdown< / a > 1.4.1.9000.< / p >
2019-02-11 10:27:10 +01:00
< / div >
2019-10-13 09:31:58 +02:00
2019-02-11 10:27:10 +01:00
< / footer >
< / div >
2020-02-23 20:56:11 +01:00
< / body >
2019-02-11 10:27:10 +01:00
< / html >
