mirror of https://github.com/msberends/AMR.git
(v0.7.1.9021) CI fix for resistance_predict
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@ -118,7 +118,7 @@ coverage:
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# install missing and outdated packages
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- Rscript -e 'source(".gitlab-ci.R"); gl_update_pkg_all(repos = "https://cran.rstudio.com", quiet = TRUE, install_pkgdown = FALSE)'
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# codecov token is set in https://gitlab.com/msberends/AMR/settings/ci_cd
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- Rscript -e "cc <- covr::package_coverage(); covr::codecov(coverage = cc, token = '$codecov', exclusions = c('R/atc_online.R', 'R/mo_source.R', 'R/resistance_predict.R')); cat('Code coverage:', covr::percent_coverage(cc))"
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- Rscript -e "cc <- covr::package_coverage()); covr::codecov(coverage = cc, token = '$codecov'); cat('Code coverage:', covr::percent_coverage(cc))"
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coverage: '/Code coverage: \d+\.\d+/'
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pages:
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@ -1,6 +1,6 @@
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Package: AMR
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Version: 0.7.1.9020
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Date: 2019-08-03
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Version: 0.7.1.9021
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Date: 2019-08-04
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Title: Antimicrobial Resistance Analysis
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Authors@R: c(
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person(
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2
NEWS.md
2
NEWS.md
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@ -1,4 +1,4 @@
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# AMR 0.7.1.9020
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# AMR 0.7.1.9021
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### Breaking
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* Function `freq()` has moved to a new package, [`clean`](https://github.com/msberends/clean) ([CRAN link](https://cran.r-project.org/package=clean)). Creating frequency tables is actually not the scope of this package (never was) and this function has matured a lot over the last two years. We decided to create a new package for data cleaning and checking and it perfectly fits the `freq()` function. The [`clean`](https://github.com/msberends/clean) package is available on CRAN and will be installed automatically when updating the `AMR` package, that now imports it. In a later stage, the `skewness()` and `kurtosis()` functions will be moved to the `clean` package too.
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@ -67,7 +67,10 @@ get_locale <- function() {
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}
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lang <- Sys.getlocale("LC_COLLATE")
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# grepl with case = FALSE is faster than like
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# Check the locale settings for a start with one of these languages:
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# grepl() with ignore.case = FALSE is faster than %like%
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if (grepl("^(English|en_|EN_)", lang, ignore.case = FALSE)) {
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# as first option to optimise speed
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"en"
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@ -28,8 +28,8 @@
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#' @param year_max highest year to use in the prediction model, defaults to 10 years after today
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#' @param year_every unit of sequence between lowest year found in the data and \code{year_max}
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#' @param minimum minimal amount of available isolates per year to include. Years containing less observations will be estimated by the model.
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#' @param model the statistical model of choice. Defaults to a generalised linear regression model with binomial distribution, assuming that a period of zero resistance was followed by a period of increasing resistance leading slowly to more and more resistance. See Details for valid options.
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#' @param I_as_S a logical to indicate whether values \code{I} should be treated as \code{S}
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#' @param model the statistical model of choice. Defaults to a generalised linear regression model with binomial distribution (i.e. using \code{\link{glm}(..., family = \link{binomial})}), assuming that a period of zero resistance was followed by a period of increasing resistance leading slowly to more and more resistance. See Details for valid options.
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#' @param I_as_S a logical to indicate whether values \code{I} should be treated as \code{S} (will otherwise be treated as \code{R})
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#' @param preserve_measurements a logical to indicate whether predictions of years that are actually available in the data should be overwritten by the original data. The standard errors of those years will be \code{NA}.
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#' @param info a logical to indicate whether textual analysis should be printed with the name and \code{\link{summary}} of the statistical model.
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#' @param main title of the plot
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@ -162,18 +162,19 @@ resistance_predict <- function(x,
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as.integer(format(as.Date(x), '%Y'))
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}
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}
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df <- x %>%
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mutate_at(col_ab, as.rsi) %>%
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mutate_at(col_ab, droplevels) %>%
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mutate_at(col_ab, ~(
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if (I_as_S == TRUE) {
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gsub("I", "S", .)
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} else {
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# then I as R
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gsub("I", "R", .)
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}
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)) %>%
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mutate_at(col_ab, droplevels)
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if (I_as_S == TRUE) {
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df <- df %>%
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mutate_at(col_ab, ~gsub("I", "S", .))
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} else {
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# then I as R
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df <- df %>%
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mutate_at(col_ab, ~gsub("I", "R", .))
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}
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df <- df %>%
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filter_at(col_ab, all_vars(!is.na(.))) %>%
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mutate(year = pull(., col_date) %>% year()) %>%
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group_by_at(c('year', col_ab)) %>%
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@ -18,4 +18,3 @@ coverage:
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ignore:
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- "R/atc_online.R"
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- "R/mo_source.R"
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- "R/resistance_predict.R"
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@ -78,7 +78,7 @@
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</button>
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<span class="navbar-brand">
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<a class="navbar-link" href="index.html">AMR (for R)</a>
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<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">0.7.1.9020</span>
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<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">0.7.1.9021</span>
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</span>
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</div>
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@ -78,7 +78,7 @@
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</button>
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<span class="navbar-brand">
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<a class="navbar-link" href="../index.html">AMR (for R)</a>
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<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">0.7.1.9020</span>
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<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">0.7.1.9021</span>
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</span>
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</div>
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@ -78,7 +78,7 @@
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</button>
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<span class="navbar-brand">
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<a class="navbar-link" href="index.html">AMR (for R)</a>
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<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">0.7.1.9020</span>
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<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">0.7.1.9021</span>
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</span>
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</div>
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@ -42,7 +42,7 @@
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</button>
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<span class="navbar-brand">
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<a class="navbar-link" href="index.html">AMR (for R)</a>
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<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">0.7.1.9020</span>
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<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">0.7.1.9021</span>
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</span>
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</div>
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@ -78,7 +78,7 @@
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</button>
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<span class="navbar-brand">
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<a class="navbar-link" href="../index.html">AMR (for R)</a>
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<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">0.7.1.9020</span>
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<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">0.7.1.9021</span>
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</span>
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</div>
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@ -225,9 +225,9 @@
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</div>
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<div id="amr-0-7-1-9020" class="section level1">
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<div id="amr-0-7-1-9021" class="section level1">
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<h1 class="page-header">
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<a href="#amr-0-7-1-9020" class="anchor"></a>AMR 0.7.1.9020<small> Unreleased </small>
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<a href="#amr-0-7-1-9021" class="anchor"></a>AMR 0.7.1.9021<small> Unreleased </small>
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</h1>
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<div id="breaking" class="section level3">
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<h3 class="hasAnchor">
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@ -1198,7 +1198,7 @@ Using <code><a href="../reference/as.mo.html">as.mo(..., allow_uncertain = 3)</a
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<div id="tocnav">
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<h2>Contents</h2>
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<ul class="nav nav-pills nav-stacked">
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<li><a href="#amr-0-7-1-9020">0.7.1.9020</a></li>
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<li><a href="#amr-0-7-1-9021">0.7.1.9021</a></li>
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<li><a href="#amr-0-7-1">0.7.1</a></li>
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<li><a href="#amr-0-7-0">0.7.0</a></li>
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<li><a href="#amr-0-6-1">0.6.1</a></li>
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@ -78,7 +78,7 @@
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</button>
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<span class="navbar-brand">
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<a class="navbar-link" href="../index.html">AMR (for R)</a>
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<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">0.7.1.9020</span>
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<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">0.7.1.9021</span>
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</span>
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</div>
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@ -80,7 +80,7 @@
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</button>
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<span class="navbar-brand">
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<a class="navbar-link" href="../index.html">AMR (for R)</a>
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<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">0.7.1.9015</span>
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<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">0.7.1.9021</span>
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</span>
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</div>
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@ -284,11 +284,11 @@
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</tr>
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<tr>
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<th>model</th>
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<td><p>the statistical model of choice. Defaults to a generalised linear regression model with binomial distribution, assuming that a period of zero resistance was followed by a period of increasing resistance leading slowly to more and more resistance. See Details for valid options.</p></td>
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<td><p>the statistical model of choice. Defaults to a generalised linear regression model with binomial distribution (i.e. using <code><a href='https://www.rdocumentation.org/packages/stats/topics/glm'>glm</a>(..., family = <a href='https://www.rdocumentation.org/packages/stats/topics/family'>binomial</a>)</code>), assuming that a period of zero resistance was followed by a period of increasing resistance leading slowly to more and more resistance. See Details for valid options.</p></td>
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</tr>
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<tr>
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<th>I_as_S</th>
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<td><p>a logical to indicate whether values <code>I</code> should be treated as <code>S</code></p></td>
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<td><p>a logical to indicate whether values <code>I</code> should be treated as <code>S</code> (will otherwise be treated as <code>R</code>)</p></td>
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</tr>
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<tr>
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<th>preserve_measurements</th>
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@ -38,9 +38,9 @@ ggplot_rsi_predict(x, main = paste("Resistance Prediction of", x_name),
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\item{minimum}{minimal amount of available isolates per year to include. Years containing less observations will be estimated by the model.}
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\item{model}{the statistical model of choice. Defaults to a generalised linear regression model with binomial distribution, assuming that a period of zero resistance was followed by a period of increasing resistance leading slowly to more and more resistance. See Details for valid options.}
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\item{model}{the statistical model of choice. Defaults to a generalised linear regression model with binomial distribution (i.e. using \code{\link{glm}(..., family = \link{binomial})}), assuming that a period of zero resistance was followed by a period of increasing resistance leading slowly to more and more resistance. See Details for valid options.}
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\item{I_as_S}{a logical to indicate whether values \code{I} should be treated as \code{S}}
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\item{I_as_S}{a logical to indicate whether values \code{I} should be treated as \code{S} (will otherwise be treated as \code{R})}
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\item{preserve_measurements}{a logical to indicate whether predictions of years that are actually available in the data should be overwritten by the original data. The standard errors of those years will be \code{NA}.}
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@ -0,0 +1,29 @@
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# ==================================================================== #
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# TITLE #
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# Antimicrobial Resistance (AMR) Analysis #
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# #
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# SOURCE #
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# https://gitlab.com/msberends/AMR #
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# #
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# LICENCE #
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# (c) 2019 Berends MS (m.s.berends@umcg.nl), Luz CF (c.f.luz@umcg.nl) #
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# #
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# This R package is free software; you can freely use and distribute #
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# it for both personal and commercial purposes under the terms of the #
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# GNU General Public License version 2.0 (GNU GPL-2), as published by #
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# the Free Software Foundation. #
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# #
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# This R package was created for academic research and was publicly #
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# released in the hope that it will be useful, but it comes WITHOUT #
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# ANY WARRANTY OR LIABILITY. #
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# Visit our website for more info: https://msberends.gitlab.io/AMR. #
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# ==================================================================== #
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context("extended.R")
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test_that("extensions work", {
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expect_identical(scale_type.mo(), "discrete")
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expect_identical(scale_type.ab(), "discrete")
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})
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@ -24,8 +24,10 @@ context("freq.R")
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test_that("frequency table works", {
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library(clean)
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# mo
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expect_output(print(freq(septic_patients$mo)))
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expect_true(is.freq(freq(septic_patients$mo)))
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# rsi
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expect_output(print(freq(septic_patients$AMX)))
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expect_true(is.freq(freq(septic_patients$AMX)))
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library(dplyr)
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expect_true(is.freq(septic_patients %>% freq(mo)))
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})
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