#' @param include_unknown logical to determine whether 'unknown' microorganisms should be included too, i.e. microbial code `"UNKNOWN"`, which defaults to `FALSE`. For WHONET users, this means that all records with organism code `"con"` (*contamination*) will be excluded at default. Isolates with a microbial ID of `NA` will always be excluded as first isolate.
#' @param ... parameters passed on to the [first_isolate()] function
#' @details **WHY THIS IS SO IMPORTANT** \cr
#' To conduct an analysis of antimicrobial resistance, you should only include the first isolate of every patient per episode [[1]](https://www.ncbi.nlm.nih.gov/pubmed/17304462). If you would not do this, you could easily get an overestimate or underestimate of the resistance of an antibiotic. Imagine that a patient was admitted with an MRSA and that it was found in 5 different blood cultures the following week. The resistance percentage of oxacillin of all *S. aureus* isolates would be overestimated, because you included this MRSA more than once. It would be [selection bias](https://en.wikipedia.org/wiki/Selection_bias).
#' To conduct an analysis of antimicrobial resistance, you should only include the first isolate of every patient per episode [(ref)](https://www.ncbi.nlm.nih.gov/pubmed/17304462). If you would not do this, you could easily get an overestimate or underestimate of the resistance of an antibiotic. Imagine that a patient was admitted with an MRSA and that it was found in 5 different blood cultures the following week. The resistance percentage of oxacillin of all *S. aureus* isolates would be overestimated, because you included this MRSA more than once. It would be [selection bias](https://en.wikipedia.org/wiki/Selection_bias).
#'
#' All isolates with a microbial ID of `NA` will be excluded as first isolate.
#' [g.test()] performs chi-squared contingency table tests and goodness-of-fit tests, just like [chisq.test()] but is more reliable [1]. A *G*-test can be used to see whether the number of observations in each category fits a theoretical expectation (called a ***G*-test of goodness-of-fit**), or to see whether the proportions of one variable are different for different values of the other variable (called a ***G*-test of independence**).
#' [g.test()] performs chi-squared contingency table tests and goodness-of-fit tests, just like [chisq.test()] but is more reliable (1). A *G*-test can be used to see whether the number of observations in each category fits a theoretical expectation (called a ***G*-test of goodness-of-fit**), or to see whether the proportions of one variable are different for different values of the other variable (called a ***G*-test of independence**).
#' @inherit stats::chisq.test params return
#' @details If `x` is a matrix with one row or column, or if `x` is a vector and `y` is not given, then a *goodness-of-fit test* is performed (`x` is treated as a one-dimensional contingency table). The entries of `x` must be non-negative integers. In this case, the hypothesis tested is whether the population probabilities equal those in `p`, or are all equal if `p` is not given.
#'
@ -64,7 +64,7 @@
#'
#' If there are more than two categories and you want to find out which ones are significantly different from their null expectation, you can use the same method of testing each category vs. the sum of all categories, with the Bonferroni correction. You use *G*-tests for each category, of course.
#' @seealso [chisq.test()]
#' @references [1] McDonald, J.H. 2014. **Handbook of Biological Statistics (3rd ed.)**. Sparky House Publishing, Baltimore, Maryland. <http://www.biostathandbook.com/gtestgof.html>.
#' @references 1. McDonald, J.H. 2014. **Handbook of Biological Statistics (3rd ed.)**. Sparky House Publishing, Baltimore, Maryland. <http://www.biostathandbook.com/gtestgof.html>.
#' @source The code for this function is identical to that of [chisq.test()], except that:
#' - The calculation of the statistic was changed to \eqn{2 * sum(x * log(x / E))}
#' - Yates' continuity correction was removed as it does not apply to a *G*-test
#' @param by a variable to join by - if left empty will search for a column with class [`mo`] (created with [as.mo()]) or will be `"mo"` if that column name exists in `x`, could otherwise be a column name of `x` with values that exist in `microorganisms$mo` (like `by = "bacteria_id"`), or another column in [microorganisms] (but then it should be named, like `by = c("my_genus_species" = "fullname")`)
#' @param suffix if there are non-joined duplicate variables in `x` and `y`, these suffixes will be added to the output to disambiguate them. Should be a character vector of length 2.
#' @param ... other parameters to pass on to [dplyr::join()]
#' @details **Note:** As opposed to the [dplyr::join()] functions of `dplyr`, [`characters`] vectors are supported and at default existing columns will get a suffix `"2"` and the newly joined columns will not get a suffix. See [dplyr::join()] for more information.
#' @details **Note:** As opposed to the [dplyr::join()] functions of `dplyr`, [`character`] vectors are supported and at default existing columns will get a suffix `"2"` and the newly joined columns will not get a suffix. See [dplyr::join()] for more information.
#' The German national guideline - Mueller et al. (2015) Antimicrobial Resistance and Infection Control 4:7. DOI: 10.1186/s13756-015-0047-6
#' - `guideline = "BRMO"`\cr
#' The Dutch national guideline - Rijksinstituut voor Volksgezondheid en Milieu "WIP-richtlijn BRMO (Bijzonder Resistente Micro-Organismen) [ZKH]" ([link](https://www.rivm.nl/Documenten_en_publicaties/Professioneel_Praktisch/Richtlijnen/Infectieziekten/WIP_Richtlijnen/WIP_Richtlijnen/Ziekenhuizen/WIP_richtlijn_BRMO_Bijzonder_Resistente_Micro_Organismen_ZKH))
#' The Dutch national guideline - Rijksinstituut voor Volksgezondheid en Milieu "WIP-richtlijn BRMO (Bijzonder Resistente Micro-Organismen) (ZKH)" ([link](https://www.rivm.nl/Documenten_en_publicaties/Professioneel_Praktisch/Richtlijnen/Infectieziekten/WIP_Richtlijnen/WIP_Richtlijnen/Ziekenhuizen/WIP_richtlijn_BRMO_Bijzonder_Resistente_Micro_Organismen_ZKH))
#'
#' Please suggest your own (country-specific) guidelines by letting us know: <https://gitlab.com/msberends/AMR/issues/new>.
#' This AMR package honours this new insight. Use [susceptibility()] (equal to [proportion_SI()]) to determine antimicrobial susceptibility and [count_susceptible()] (equal to [count_SI()]) to count susceptible isolates.
<li>Added ~5,000 more old taxonomic names to the <code>microorganisms.old</code> data set, which leads to better results finding when using the <code><ahref="../reference/as.mo.html">as.mo()</a></code> function</li>
<li>This package now honours the new EUCAST insight (2019) that S and I are but classified as susceptible, where I is defined as ‘increased exposure’ and not ‘intermediate’ anymore. For functions like <code><ahref="../reference/AMR-deprecated.html">portion_df()</a></code> and <code><ahref="../reference/count.html">count_df()</a></code> this means that their new parameter <code>combine_SI</code> is TRUE at default. Our plotting function <code><ahref="../reference/ggplot_rsi.html">ggplot_rsi()</a></code> also reflects this change since it uses <code><ahref="../reference/count.html">count_df()</a></code> internally.</li>
<li>The <code><ahref="../reference/age.html">age()</a></code> function gained a new parameter <code>exact</code> to determine ages with decimals</li>
<td><p>vector of values (for class <code>mic</code>: an MIC value in mg/L, for class <code><ahref='as.disk.html'>disk</a></code>: a disk diffusion radius in millimeters)</p></td>
<td><p>vector of values (for class <code><ahref='as.mic.html'>mic</a></code>: an MIC value in mg/L, for class <code><ahref='as.disk.html'>disk</a></code>: a disk diffusion radius in millimeters)</p></td>
To conduct an analysis of antimicrobial resistance, you should only include the first isolate of every patient per episode [1](https://www.ncbi.nlm.nih.gov/pubmed/17304462). If you would not do this, you could easily get an overestimate or underestimate of the resistance of an antibiotic. Imagine that a patient was admitted with an MRSA and that it was found in 5 different blood cultures the following week. The resistance percentage of oxacillin of all <em>S. aureus</em> isolates would be overestimated, because you included this MRSA more than once. It would be <ahref='https://en.wikipedia.org/wiki/Selection_bias'>selection bias</a>.</p>
To conduct an analysis of antimicrobial resistance, you should only include the first isolate of every patient per episode <ahref='https://www.ncbi.nlm.nih.gov/pubmed/17304462'>(ref)</a>. If you would not do this, you could easily get an overestimate or underestimate of the resistance of an antibiotic. Imagine that a patient was admitted with an MRSA and that it was found in 5 different blood cultures the following week. The resistance percentage of oxacillin of all <em>S. aureus</em> isolates would be overestimated, because you included this MRSA more than once. It would be <ahref='https://en.wikipedia.org/wiki/Selection_bias'>selection bias</a>.</p>
<p>All isolates with a microbial ID of <code>NA</code> will be excluded as first isolate.</p>
<p>The functions <code>filter_first_isolate()</code> and <code>filter_first_weighted_isolate()</code> are helper functions to quickly filter on first isolates. The function <code>filter_first_isolate()</code> is essentially equal to:</p><pre> x %>%
<metaproperty="og:title"content="<em>G</em>-test for Count Data — g.test"/>
<metaproperty="og:description"content="g.test() performs chi-squared contingency table tests and goodness-of-fit tests, just like chisq.test() but is more reliable 1. A G-test can be used to see whether the number of observations in each category fits a theoretical expectation (called a G-test of goodness-of-fit), or to see whether the proportions of one variable are different for different values of the other variable (called a G-test of independence)."/>
<metaproperty="og:description"content="g.test() performs chi-squared contingency table tests and goodness-of-fit tests, just like chisq.test() but is more reliable (1). A G-test can be used to see whether the number of observations in each category fits a theoretical expectation (called a G-test of goodness-of-fit), or to see whether the proportions of one variable are different for different values of the other variable (called a G-test of independence)."/>
<spanclass="version label label-default"data-toggle="tooltip"data-placement="bottom"title="Latest development version">0.8.0.9036</span>
<spanclass="version label label-default"data-toggle="tooltip"data-placement="bottom"title="Latest development version">0.8.0.9037</span>
</span>
</div>
@ -234,7 +234,7 @@
</div>
<divclass="ref-description">
<p><code>g.test()</code> performs chi-squared contingency table tests and goodness-of-fit tests, just like <code><ahref='https://rdrr.io/r/stats/chisq.test.html'>chisq.test()</a></code> but is more reliable 1. A <em>G</em>-test can be used to see whether the number of observations in each category fits a theoretical expectation (called a <strong><em>G</em>-test of goodness-of-fit</strong>), or to see whether the proportions of one variable are different for different values of the other variable (called a <strong><em>G</em>-test of independence</strong>).</p>
<p><code>g.test()</code> performs chi-squared contingency table tests and goodness-of-fit tests, just like <code><ahref='https://rdrr.io/r/stats/chisq.test.html'>chisq.test()</a></code> but is more reliable (1). A <em>G</em>-test can be used to see whether the number of observations in each category fits a theoretical expectation (called a <strong><em>G</em>-test of goodness-of-fit</strong>), or to see whether the proportions of one variable are different for different values of the other variable (called a <strong><em>G</em>-test of independence</strong>).</p>
<p>On our website <ahref='https://msberends.gitlab.io/AMR'>https://msberends.gitlab.io/AMR</a> you can find <ahref='https://msberends.gitlab.io/AMR/articles/AMR.html'>a tutorial</a> about how to conduct AMR analysis, the <ahref='https://msberends.gitlab.io/AMR/reference'>complete documentation of all functions</a> (which reads a lot easier than here in R) and <ahref='https://msberends.gitlab.io/AMR/articles/WHONET.html'>an example analysis using WHONET data</a>.</p>
<p><strong>Note:</strong> As opposed to the <code><ahref='https://dplyr.tidyverse.org/reference/join.html'>dplyr::join()</a></code> functions of <code>dplyr</code>, <code>characters</code> vectors are supported and at default existing columns will get a suffix <code>"2"</code> and the newly joined columns will not get a suffix. See <code><ahref='https://dplyr.tidyverse.org/reference/join.html'>dplyr::join()</a></code> for more information.</p>
<p><strong>Note:</strong> As opposed to the <code><ahref='https://dplyr.tidyverse.org/reference/join.html'>dplyr::join()</a></code> functions of <code>dplyr</code>, <code><ahref='https://rdrr.io/r/base/character.html'>character</a></code> vectors are supported and at default existing columns will get a suffix <code>"2"</code> and the newly joined columns will not get a suffix. See <code><ahref='https://dplyr.tidyverse.org/reference/join.html'>dplyr::join()</a></code> for more information.</p>
<h2class="hasAnchor"id="read-more-on-our-website-"><aclass="anchor"href="#read-more-on-our-website-"></a>Read more on our website!</h2>
<td><p>a vector of values, a <code><ahref='https://rdrr.io/r/base/matrix.html'>matrix</a></code> or a <code>dataframe</code></p></td>
<td><p>a vector of values, a <code><ahref='https://rdrr.io/r/base/matrix.html'>matrix</a></code> or a <code><ahref='https://rdrr.io/r/base/data.frame.html'>data.frame</a></code></p></td>
<spanclass="version label label-default"data-toggle="tooltip"data-placement="bottom"title="Latest development version">0.8.0.9036</span>
<spanclass="version label label-default"data-toggle="tooltip"data-placement="bottom"title="Latest development version">0.8.0.9037</span>
</span>
</div>
@ -325,7 +325,7 @@ The international guideline for multi-drug resistant tuberculosis - World Health
<li><p><code>guideline = "MRGN"</code><br/>
The German national guideline - Mueller et al. (2015) Antimicrobial Resistance and Infection Control 4:7. DOI: 10.1186/s13756-015-0047-6</p></li>
<li><p><code>guideline = "BRMO"</code><br/>
The Dutch national guideline - Rijksinstituut voor Volksgezondheid en Milieu "WIP-richtlijn BRMO (Bijzonder Resistente Micro-Organismen) ZKH" (<ahref='https://www.rivm.nl/Documenten_en_publicaties/Professioneel_Praktisch/Richtlijnen/Infectieziekten/WIP_Richtlijnen/WIP_Richtlijnen/Ziekenhuizen/WIP_richtlijn_BRMO_Bijzonder_Resistente_Micro_Organismen_ZKH'>link</a>)</p></li>
The Dutch national guideline - Rijksinstituut voor Volksgezondheid en Milieu "WIP-richtlijn BRMO (Bijzonder Resistente Micro-Organismen) (ZKH)" (<ahref='https://www.rivm.nl/Documenten_en_publicaties/Professioneel_Praktisch/Richtlijnen/Infectieziekten/WIP_Richtlijnen/WIP_Richtlijnen/Ziekenhuizen/WIP_richtlijn_BRMO_Bijzonder_Resistente_Micro_Organismen_ZKH'>link</a>)</p></li>
</ul>
<p>Please suggest your own (country-specific) guidelines by letting us know: <ahref='https://gitlab.com/msberends/AMR/issues/new'>https://gitlab.com/msberends/AMR/issues/new</a>.</p>
<spanclass="version label label-default"data-toggle="tooltip"data-placement="bottom"title="Latest development version">0.8.0.9036</span>
<spanclass="version label label-default"data-toggle="tooltip"data-placement="bottom"title="Latest development version">0.8.0.9037</span>
</span>
</div>
@ -261,7 +261,7 @@
<p>Manually added were:</p><ul>
<li><p>11 entries of <em>Streptococcus</em> (beta-haemolytic: groups A, B, C, D, F, G, H, K and unspecified; other: viridans, milleri)</p></li>
<li><p>2 entries of <em>Staphylococcus</em> (coagulase-negative CoNS and coagulase-positive CoPS)</p></li>
<li><p>2 entries of <em>Staphylococcus</em> (coagulase-negative (CoNS) and coagulase-positive (CoPS))</p></li>
<li><p>3 entries of <em>Trichomonas</em> (<em>Trichomonas vaginalis</em>, and its family and genus)</p></li>
<li><p>1 entry of <em>Blastocystis</em> (<em>Blastocystis hominis</em>), although it officially does not exist (Noel <em>et al.</em> 2005, PMID 15634993)</p></li>
<li><p>5 other 'undefined' entries (unknown, unknown Gram negatives, unknown Gram positives, unknown yeast and unknown fungus)</p></li>
@ -94,7 +94,7 @@ Determine first (weighted) isolates of all microorganisms of every patient per e
}
\details{
\strong{WHY THIS IS SO IMPORTANT} \cr
To conduct an analysis of antimicrobial resistance, you should only include the first isolate of every patient per episode [\link{1}](https://www.ncbi.nlm.nih.gov/pubmed/17304462). If you would not do this, you could easily get an overestimate or underestimate of the resistance of an antibiotic. Imagine that a patient was admitted with an MRSA and that it was found in 5 different blood cultures the following week. The resistance percentage of oxacillin of all \emph{S. aureus} isolates would be overestimated, because you included this MRSA more than once. It would be \href{https://en.wikipedia.org/wiki/Selection_bias}{selection bias}.
To conduct an analysis of antimicrobial resistance, you should only include the first isolate of every patient per episode \href{https://www.ncbi.nlm.nih.gov/pubmed/17304462}{(ref)}. If you would not do this, you could easily get an overestimate or underestimate of the resistance of an antibiotic. Imagine that a patient was admitted with an MRSA and that it was found in 5 different blood cultures the following week. The resistance percentage of oxacillin of all \emph{S. aureus} isolates would be overestimated, because you included this MRSA more than once. It would be \href{https://en.wikipedia.org/wiki/Selection_bias}{selection bias}.
All isolates with a microbial ID of \code{NA} will be excluded as first isolate.
@ -49,7 +49,7 @@ A list with class \code{"htest"} containing the following
section 2.4.5 for the case where \code{x} is a matrix, \code{n * p * (1 - p)} otherwise).}
}
\description{
\code{\link[=g.test]{g.test()}} performs chi-squared contingency table tests and goodness-of-fit tests, just like \code{\link[=chisq.test]{chisq.test()}} but is more reliable \link{1}. A \emph{G}-test can be used to see whether the number of observations in each category fits a theoretical expectation (called a \strong{\emph{G}-test of goodness-of-fit}), or to see whether the proportions of one variable are different for different values of the other variable (called a \strong{\emph{G}-test of independence}).
\code{\link[=g.test]{g.test()}} performs chi-squared contingency table tests and goodness-of-fit tests, just like \code{\link[=chisq.test]{chisq.test()}} but is more reliable (1). A \emph{G}-test can be used to see whether the number of observations in each category fits a theoretical expectation (called a \strong{\emph{G}-test of goodness-of-fit}), or to see whether the proportions of one variable are different for different values of the other variable (called a \strong{\emph{G}-test of independence}).
}
\details{
If \code{x} is a matrix with one row or column, or if \code{x} is a vector and \code{y} is not given, then a \emph{goodness-of-fit test} is performed (\code{x} is treated as a one-dimensional contingency table). The entries of \code{x} must be non-negative integers. In this case, the hypothesis tested is whether the population probabilities equal those in \code{p}, or are all equal if \code{p} is not given.
@ -137,7 +137,9 @@ g.test(x)
}
\references{
\link{1} McDonald, J.H. 2014. \strong{Handbook of Biological Statistics (3rd ed.)}. Sparky House Publishing, Baltimore, Maryland. \url{http://www.biostathandbook.com/gtestgof.html}.
\enumerate{
\item McDonald, J.H. 2014. \strong{Handbook of Biological Statistics (3rd ed.)}. Sparky House Publishing, Baltimore, Maryland. \url{http://www.biostathandbook.com/gtestgof.html}.
@ -36,7 +36,7 @@ anti_join_microorganisms(x, by = NULL, ...)
Join the data set \link{microorganisms} easily to an existing table or character vector.
}
\details{
\strong{Note:} As opposed to the \code{\link[dplyr:join]{dplyr::join()}} functions of \code{dplyr}, \code{\link{characters}} vectors are supported and at default existing columns will get a suffix \code{"2"} and the newly joined columns will not get a suffix. See \code{\link[dplyr:join]{dplyr::join()}} for more information.
\strong{Note:} As opposed to the \code{\link[dplyr:join]{dplyr::join()}} functions of \code{dplyr}, \code{\link{character}} vectors are supported and at default existing columns will get a suffix \code{"2"} and the newly joined columns will not get a suffix. See \code{\link[dplyr:join]{dplyr::join()}} for more information.
@ -85,7 +85,7 @@ The international guideline for multi-drug resistant tuberculosis - World Health
\item \code{guideline = "MRGN"}\cr
The German national guideline - Mueller et al. (2015) Antimicrobial Resistance and Infection Control 4:7. DOI: 10.1186/s13756-015-0047-6
\item \code{guideline = "BRMO"}\cr
The Dutch national guideline - Rijksinstituut voor Volksgezondheid en Milieu "WIP-richtlijn BRMO (Bijzonder Resistente Micro-Organismen) \link{ZKH}" (\href{https://www.rivm.nl/Documenten_en_publicaties/Professioneel_Praktisch/Richtlijnen/Infectieziekten/WIP_Richtlijnen/WIP_Richtlijnen/Ziekenhuizen/WIP_richtlijn_BRMO_Bijzonder_Resistente_Micro_Organismen_ZKH}{link})
The Dutch national guideline - Rijksinstituut voor Volksgezondheid en Milieu "WIP-richtlijn BRMO (Bijzonder Resistente Micro-Organismen) (ZKH)" (\href{https://www.rivm.nl/Documenten_en_publicaties/Professioneel_Praktisch/Richtlijnen/Infectieziekten/WIP_Richtlijnen/WIP_Richtlijnen/Ziekenhuizen/WIP_richtlijn_BRMO_Bijzonder_Resistente_Micro_Organismen_ZKH}{link})
}
Please suggest your own (country-specific) guidelines by letting us know: \url{https://gitlab.com/msberends/AMR/issues/new}.
