AMR/man/pca.Rd

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% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/pca.R
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\name{pca}
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\alias{pca}
\title{Principal Component Analysis (for AMR)}
\usage{
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pca(
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x,
...,
retx = TRUE,
center = TRUE,
scale. = TRUE,
tol = NULL,
rank. = NULL
)
}
\arguments{
\item{x}{a \link{data.frame} containing numeric columns}
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\item{...}{columns of \code{x} to be selected for PCA, can be unquoted since it supports quasiquotation.}
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\item{retx}{a logical value indicating whether the rotated variables
should be returned.}
\item{center}{a logical value indicating whether the variables
should be shifted to be zero centered. Alternately, a vector of
length equal the number of columns of \code{x} can be supplied.
The value is passed to \code{scale}.}
\item{scale.}{a logical value indicating whether the variables should
be scaled to have unit variance before the analysis takes
place. The default is \code{FALSE} for consistency with S, but
in general scaling is advisable. Alternatively, a vector of length
equal the number of columns of \code{x} can be supplied. The
value is passed to \code{\link{scale}}.}
\item{tol}{a value indicating the magnitude below which components
should be omitted. (Components are omitted if their
standard deviations are less than or equal to \code{tol} times the
standard deviation of the first component.) With the default null
setting, no components are omitted (unless \code{rank.} is specified
less than \code{min(dim(x))}.). Other settings for tol could be
\code{tol = 0} or \code{tol = sqrt(.Machine$double.eps)}, which
would omit essentially constant components.}
\item{rank.}{optionally, a number specifying the maximal rank, i.e.,
maximal number of principal components to be used. Can be set as
alternative or in addition to \code{tol}, useful notably when the
desired rank is considerably smaller than the dimensions of the matrix.}
}
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\value{
An object of classes \link{pca} and \link{prcomp}
}
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\description{
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Performs a principal component analysis (PCA) based on a data set with automatic determination for afterwards plotting the groups and labels, and automatic filtering on only suitable (i.e. non-empty and numeric) variables.
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}
\details{
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The \code{\link[=pca]{pca()}} function takes a \link{data.frame} as input and performs the actual PCA with the \R function \code{\link[=prcomp]{prcomp()}}.
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The result of the \code{\link[=pca]{pca()}} function is a \link{prcomp} object, with an additional attribute \code{non_numeric_cols} which is a vector with the column names of all columns that do not contain numeric values. These are probably the groups and labels, and will be used by \code{\link[=ggplot_pca]{ggplot_pca()}}.
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}
\section{Maturing Lifecycle}{
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\if{html}{\figure{lifecycle_maturing.svg}{options: style=margin-bottom:5px} \cr}
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The \link[=lifecycle]{lifecycle} of this function is \strong{maturing}. The unlying code of a maturing function has been roughed out, but finer details might still change. Since this function needs wider usage and more extensive testing, you are very welcome \href{https://github.com/msberends/AMR/issues}{to suggest changes at our repository} or \link[=AMR]{write us an email (see section 'Contact Us')}.
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}
\section{Read more on Our Website!}{
On our website \url{https://msberends.github.io/AMR/} you can find \href{https://msberends.github.io/AMR/articles/AMR.html}{a comprehensive tutorial} about how to conduct AMR data analysis, the \href{https://msberends.github.io/AMR/reference/}{complete documentation of all functions} and \href{https://msberends.github.io/AMR/articles/WHONET.html}{an example analysis using WHONET data}. As we would like to better understand the backgrounds and needs of our users, please \href{https://msberends.github.io/AMR/survey.html}{participate in our survey}!
}
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\examples{
# `example_isolates` is a data set available in the AMR package.
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# See ?example_isolates.
\donttest{
if (require("dplyr")) {
# calculate the resistance per group first
resistance_data <- example_isolates \%>\%
group_by(order = mo_order(mo), # group on anything, like order
genus = mo_genus(mo)) \%>\% # and genus as we do here;
summarise_if(is.rsi, resistance) # then get resistance of all drugs
# now conduct PCA for certain antimicrobial agents
pca_result <- resistance_data \%>\%
pca(AMC, CXM, CTX, CAZ, GEN, TOB, TMP, SXT)
pca_result
summary(pca_result)
biplot(pca_result)
ggplot_pca(pca_result) # a new and convenient plot function
}
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}
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}