P281424/AMR
1
0
Fork 0
mirror of https://github.com/msberends/AMR.git synced 2024-12-25 18:46:11 +01:00
Code Releases Activity

documentation fix

Browse Source
This commit is contained in:
dr. M.S. (Matthijs) Berends 2019-05-13 16:35:48 +02:00
parent cc403169c6
commit 2ea0c93e44
4 changed files with 68 additions and 50 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

63
R/resistance_predict.R
View File

@ -22,20 +22,21 @@
#' Predict antimicrobial resistance
#'
#' Create a prediction model to predict antimicrobial resistance for the next years on statistical solid ground. Standard errors (SE) will be returned as columns \code{se_min} and \code{se_max}. See Examples for a real live example.
#' @inheritParams first_isolate
#' @inheritParams graphics::plot
#' @param col_ab column name of \code{tbl} with antimicrobial interpretations (\code{R}, \code{I} and \code{S})
#' @param col_ab column name of \code{x} with antimicrobial interpretations (\code{R}, \code{I} and \code{S})
#' @param col_date column name of the date, will be used to calculate years if this column doesn't consist of years already, defaults to the first column of with a date class
#' @param year_min lowest year to use in the prediction model, dafaults to the lowest year in \code{col_date}
#' @param year_max highest year to use in the prediction model, defaults to 10 years after today
#' @param year_every unit of sequence between lowest year found in the data and \code{year_max}
#' @param minimum minimal amount of available isolates per year to include. Years containing less observations will be estimated by the model.
#' @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.
#' @param I_as_R a logical to indicate whether values \code{I} should be treated as \code{R}
#' @param I_as_S a logical to indicate whether values \code{I} should be treated as \code{S}
#' @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}.
#' @param info a logical to indicate whether textual analysis should be printed with the name and \code{\link{summary}} of the statistical model.
#' @param main title of the plot
#' @param ribbon a logical to indicate whether a ribbon should be shown (default) or error bars
#' @param ... parameters passed on to functions
#' @inheritParams first_isolate
#' @inheritParams graphics::plot
#' @details Valid options for the statistical model are:
#' \itemize{
#' \item{\code{"binomial"} or \code{"binom"} or \code{"logit"}: a generalised linear regression model with binomial distribution}
@ -104,7 +105,7 @@
#' x = "Year") +
#' theme_minimal(base_size = 13)
#' }
resistance_predict <- function(tbl,
resistance_predict <- function(x,
col_ab,
col_date = NULL,
year_min = NULL,
@ -112,33 +113,46 @@ resistance_predict <- function(tbl,
year_every = 1,
minimum = 30,
model = 'binomial',
I_as_R = TRUE,
I_as_S = TRUE,
preserve_measurements = TRUE,
info = TRUE) {
info = TRUE,
...) {
if (nrow(tbl) == 0) {
if (nrow(x) == 0) {
stop('This table does not contain any observations.')
}
if (!col_ab %in% colnames(tbl)) {
if (!col_ab %in% colnames(x)) {
stop('Column ', col_ab, ' not found.')
}
dots <- unlist(list(...))
if (length(dots) != 0) {
# backwards compatibility with old parameters
dots.names <- dots %>% names()
if ('tbl' %in% dots.names) {
x <- dots[which(dots.names == 'tbl')]
}
if ('I_as_R' %in% dots.names) {
warning("`I_as_R is deprecated - use I_as_S instead.", call. = FALSE)
}
}
# -- date
if (is.null(col_date)) {
col_date <- search_type_in_df(tbl = tbl, type = "date")
col_date <- search_type_in_df(tbl = x, type = "date")
}
if (is.null(col_date)) {
stop("`col_date` must be set.", call. = FALSE)
}
if (!col_date %in% colnames(tbl)) {
if (!col_date %in% colnames(x)) {
stop('Column ', col_date, ' not found.')
}
if (n_groups(tbl) > 1) {
if (n_groups(x) > 1) {
# no grouped tibbles please, mutate will throw errors
tbl <- base::as.data.frame(tbl, stringsAsFactors = FALSE)
x <- base::as.data.frame(x, stringsAsFactors = FALSE)
}
year <- function(x) {
@ -149,14 +163,15 @@ resistance_predict <- function(tbl,
}
}
df <- tbl %>%
df <- x %>%
mutate_at(col_ab, as.rsi) %>%
mutate_at(col_ab, droplevels) %>%
mutate_at(col_ab, funs(
if (I_as_R == TRUE) {
gsub("I", "R", .)
} else {
if (I_as_S == TRUE) {
gsub("I", "S", .)
} else {
# then I as R
gsub("I", "R", .)
}
)) %>%
filter_at(col_ab, all_vars(!is.na(.))) %>%
@ -289,7 +304,7 @@ resistance_predict <- function(tbl,
structure(
.Data = df_prediction,
class = c("resistance_predict", "data.frame"),
I_as_R = I_as_R,
I_as_S = I_as_S,
model_title = model,
model = model_lm,
ab = col_ab
@ -306,10 +321,10 @@ rsi_predict <- resistance_predict
#' @importFrom graphics plot axis arrows points
#' @rdname resistance_predict
plot.resistance_predict <- function(x, main = paste("Resistance prediction of", attributes(x)$ab), ...) {
if (attributes(x)$I_as_R == TRUE) {
ylab <- "%IR"
} else {
if (attributes(x)$I_as_S == TRUE) {
ylab <- "%R"
} else {
ylab <- "%IR"
}
plot(x = x$year,
y = x$value,
@ -352,10 +367,10 @@ ggplot_rsi_predict <- function(x,
stop("`x` must be a resistance prediction model created with resistance_predict().")
}
if (attributes(x)$I_as_R == TRUE) {
ylab <- "%IR"
} else {
if (attributes(x)$I_as_S == TRUE) {
ylab <- "%R"
} else {
ylab <- "%IR"
}
p <- ggplot2::ggplot(x, ggplot2::aes(x = year, y = value)) +

3
codecov.yml
View File

@ -14,3 +14,6 @@ coverage:
project: no
patch: no
changes: no
ignore:
- "R/atc_online.R"

30
docs/reference/resistance_predict.html
View File

@ -241,15 +241,15 @@
</div>
<pre class="usage"><span class='fu'>resistance_predict</span>(<span class='no'>tbl</span>, <span class='no'>col_ab</span>, <span class='kw'>col_date</span> <span class='kw'>=</span> <span class='kw'>NULL</span>, <span class='kw'>year_min</span> <span class='kw'>=</span> <span class='kw'>NULL</span>,
<pre class="usage"><span class='fu'>resistance_predict</span>(<span class='no'>x</span>, <span class='no'>col_ab</span>, <span class='kw'>col_date</span> <span class='kw'>=</span> <span class='kw'>NULL</span>, <span class='kw'>year_min</span> <span class='kw'>=</span> <span class='kw'>NULL</span>,
<span class='kw'>year_max</span> <span class='kw'>=</span> <span class='kw'>NULL</span>, <span class='kw'>year_every</span> <span class='kw'>=</span> <span class='fl'>1</span>, <span class='kw'>minimum</span> <span class='kw'>=</span> <span class='fl'>30</span>,
<span class='kw'>model</span> <span class='kw'>=</span> <span class='st'>"binomial"</span>, <span class='kw'>I_as_R</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>, <span class='kw'>preserve_measurements</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>,
<span class='kw'>info</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>)
<span class='kw'>model</span> <span class='kw'>=</span> <span class='st'>"binomial"</span>, <span class='kw'>I_as_S</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>, <span class='kw'>preserve_measurements</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>,
<span class='kw'>info</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>, <span class='no'>...</span>)
<span class='fu'>rsi_predict</span>(<span class='no'>tbl</span>, <span class='no'>col_ab</span>, <span class='kw'>col_date</span> <span class='kw'>=</span> <span class='kw'>NULL</span>, <span class='kw'>year_min</span> <span class='kw'>=</span> <span class='kw'>NULL</span>,
<span class='fu'>rsi_predict</span>(<span class='no'>x</span>, <span class='no'>col_ab</span>, <span class='kw'>col_date</span> <span class='kw'>=</span> <span class='kw'>NULL</span>, <span class='kw'>year_min</span> <span class='kw'>=</span> <span class='kw'>NULL</span>,
<span class='kw'>year_max</span> <span class='kw'>=</span> <span class='kw'>NULL</span>, <span class='kw'>year_every</span> <span class='kw'>=</span> <span class='fl'>1</span>, <span class='kw'>minimum</span> <span class='kw'>=</span> <span class='fl'>30</span>,
<span class='kw'>model</span> <span class='kw'>=</span> <span class='st'>"binomial"</span>, <span class='kw'>I_as_R</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>, <span class='kw'>preserve_measurements</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>,
<span class='kw'>info</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>)
<span class='kw'>model</span> <span class='kw'>=</span> <span class='st'>"binomial"</span>, <span class='kw'>I_as_S</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>, <span class='kw'>preserve_measurements</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>,
<span class='kw'>info</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>, <span class='no'>...</span>)
<span class='co'># S3 method for resistance_predict</span>
<span class='fu'><a href='https://www.rdocumentation.org/packages/graphics/topics/plot'>plot</a></span>(<span class='no'>x</span>,
@ -261,9 +261,13 @@
<h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
<table class="ref-arguments">
<colgroup><col class="name" /><col class="desc" /></colgroup>
<tr>
<th>x</th>
<td><p>a <code>data.frame</code> containing isolates.</p></td>
</tr>
<tr>
<th>col_ab</th>
<td><p>column name of <code>tbl</code> with antimicrobial interpretations (<code>R</code>, <code>I</code> and <code>S</code>)</p></td>
<td><p>column name of <code>x</code> with antimicrobial interpretations (<code>R</code>, <code>I</code> and <code>S</code>)</p></td>
</tr>
<tr>
<th>col_date</th>
@ -290,8 +294,8 @@
<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>
</tr>
<tr>
<th>I_as_R</th>
<td><p>a logical to indicate whether values <code>I</code> should be treated as <code>R</code></p></td>
<th>I_as_S</th>
<td><p>a logical to indicate whether values <code>I</code> should be treated as <code>S</code></p></td>
</tr>
<tr>
<th>preserve_measurements</th>
@ -302,17 +306,13 @@
<td><p>a logical to indicate whether textual analysis should be printed with the name and <code><a href='https://www.rdocumentation.org/packages/base/topics/summary'>summary</a></code> of the statistical model.</p></td>
</tr>
<tr>
<th>x</th>
<td><p>a <code>data.frame</code> containing isolates.</p></td>
<th>...</th>
<td><p>parameters passed on to functions</p></td>
</tr>
<tr>
<th>main</th>
<td><p>title of the plot</p></td>
</tr>
<tr>
<th>...</th>
<td><p>parameters passed on to the <code>first_isolate</code> function</p></td>
</tr>
<tr>
<th>ribbon</th>
<td><p>a logical to indicate whether a ribbon should be shown (default) or error bars</p></td>

22
man/resistance_predict.Rd
View File

@ -7,15 +7,15 @@
\alias{ggplot_rsi_predict}
\title{Predict antimicrobial resistance}
\usage{
resistance_predict(tbl, col_ab, col_date = NULL, year_min = NULL,
resistance_predict(x, col_ab, col_date = NULL, year_min = NULL,
year_max = NULL, year_every = 1, minimum = 30,
model = "binomial", I_as_R = TRUE, preserve_measurements = TRUE,
info = TRUE)
model = "binomial", I_as_S = TRUE, preserve_measurements = TRUE,
info = TRUE, ...)
rsi_predict(tbl, col_ab, col_date = NULL, year_min = NULL,
rsi_predict(x, col_ab, col_date = NULL, year_min = NULL,
year_max = NULL, year_every = 1, minimum = 30,
model = "binomial", I_as_R = TRUE, preserve_measurements = TRUE,
info = TRUE)
model = "binomial", I_as_S = TRUE, preserve_measurements = TRUE,
info = TRUE, ...)
\method{plot}{resistance_predict}(x,
main = paste("Resistance prediction of", attributes(x)$ab), ...)
@ -24,7 +24,9 @@ ggplot_rsi_predict(x, main = paste("Resistance prediction of",
attributes(x)$ab), ribbon = TRUE, ...)
}
\arguments{
\item{col_ab}{column name of \code{tbl} with antimicrobial interpretations (\code{R}, \code{I} and \code{S})}
\item{x}{a \code{data.frame} containing isolates.}
\item{col_ab}{column name of \code{x} with antimicrobial interpretations (\code{R}, \code{I} and \code{S})}
\item{col_date}{column name of the date, will be used to calculate years if this column doesn't consist of years already, defaults to the first column of with a date class}
@ -38,18 +40,16 @@ ggplot_rsi_predict(x, main = paste("Resistance prediction of",
\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.}
\item{I_as_R}{a logical to indicate whether values \code{I} should be treated as \code{R}}
\item{I_as_S}{a logical to indicate whether values \code{I} should be treated as \code{S}}
\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}.}
\item{info}{a logical to indicate whether textual analysis should be printed with the name and \code{\link{summary}} of the statistical model.}
\item{x}{a \code{data.frame} containing isolates.}
\item{...}{parameters passed on to functions}
\item{main}{title of the plot}
\item{...}{parameters passed on to the \code{first_isolate} function}
\item{ribbon}{a logical to indicate whether a ribbon should be shown (default) or error bars}
}
\value{