AMR/man/pca.Rd

% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/pca.R
\name{pca}
\alias{pca}
\title{Principal Component Analysis (for AMR)}
\usage{
pca(
  x,
  ...,
  retx = TRUE,
  center = TRUE,
  scale. = TRUE,
  tol = NULL,
  rank. = NULL
)
}
\arguments{
\item{x}{a \link{data.frame} containing \link{numeric} columns}

\item{...}{columns of \code{x} to be selected for PCA, can be unquoted since it supports quasiquotation.}

\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.}
}
\value{
An object of classes \link{pca} and \link{prcomp}
}
\description{
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.
}
\details{
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()}}.

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 \link{numeric} values. These are probably the groups and labels, and will be used by \code{\link[=ggplot_pca]{ggplot_pca()}}.
}
\section{Stable Lifecycle}{

\if{html}{\figure{lifecycle_stable.svg}{options: style=margin-bottom:"5"} \cr}
The \link[=lifecycle]{lifecycle} of this function is \strong{stable}. In a stable function, major changes are unlikely. This means that the unlying code will generally evolve by adding new arguments; removing arguments or changing the meaning of existing arguments will be avoided.

If the unlying code needs breaking changes, they will occur gradually. For example, an argument will be deprecated and first continue to work, but will emit a message informing you of the change. Next, typically after at least one newly released version on CRAN, the message will be transformed to an error.
}

\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}.
}

\examples{
# `example_isolates` is a data set available in the AMR package.
# 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
}
}
}