mirror of https://github.com/msberends/AMR.git
88 lines
4.0 KiB
R
88 lines
4.0 KiB
R
% Generated by roxygen2: do not edit by hand
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% Please edit documentation in R/pca.R
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\name{pca}
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\alias{pca}
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\title{Principal Component Analysis (for AMR)}
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\usage{
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pca(
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x,
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...,
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retx = TRUE,
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center = TRUE,
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scale. = TRUE,
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tol = NULL,
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rank. = NULL
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)
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}
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\arguments{
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\item{x}{a \link{data.frame} containing numeric columns}
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\item{...}{columns of \code{x} to be selected for PCA}
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\item{retx}{a logical value indicating whether the rotated variables
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should be returned.}
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\item{center}{a logical value indicating whether the variables
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should be shifted to be zero centered. Alternately, a vector of
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length equal the number of columns of \code{x} can be supplied.
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The value is passed to \code{scale}.}
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\item{scale.}{a logical value indicating whether the variables should
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be scaled to have unit variance before the analysis takes
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place. The default is \code{FALSE} for consistency with S, but
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in general scaling is advisable. Alternatively, a vector of length
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equal the number of columns of \code{x} can be supplied. The
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value is passed to \code{\link{scale}}.}
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\item{tol}{a value indicating the magnitude below which components
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should be omitted. (Components are omitted if their
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standard deviations are less than or equal to \code{tol} times the
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standard deviation of the first component.) With the default null
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setting, no components are omitted (unless \code{rank.} is specified
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less than \code{min(dim(x))}.). Other settings for tol could be
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\code{tol = 0} or \code{tol = sqrt(.Machine$double.eps)}, which
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would omit essentially constant components.}
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\item{rank.}{optionally, a number specifying the maximal rank, i.e.,
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maximal number of principal components to be used. Can be set as
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alternative or in addition to \code{tol}, useful notably when the
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desired rank is considerably smaller than the dimensions of the matrix.}
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}
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\value{
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An object of classes \link{pca} and \link{prcomp}
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}
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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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}
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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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}
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\section{Experimental lifecycle}{
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\if{html}{\figure{lifecycle_experimental.svg}{options: style=margin-bottom:5px} \cr}
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The \link[AMR:lifecycle]{lifecycle} of this function is \strong{experimental}. An experimental function is in the very early stages of development. The unlying code might be changing frequently as we rapidly iterate and explore variations in search of the best fit. Experimental functions might be removed without deprecation, so you are generally best off waiting until a function is more mature before you use it in production code. Experimental functions will not be included in releases we submit to CRAN, since they have not yet matured enough.
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}
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\examples{
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# `example_isolates` is a dataset available in the AMR package.
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# See ?example_isolates.
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# calculate the resistance per group first
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library(dplyr)
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resistance_data <- example_isolates \%>\%
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group_by(order = mo_order(mo), # group on anything, like order
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genus = mo_genus(mo)) \%>\% # and genus as we do here
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summarise_if(is.rsi, resistance) # then get resistance of all drugs
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# now conduct PCA for certain antimicrobial agents
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pca_result <- resistance_data \%>\%
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pca(AMC, CXM, CTX, CAZ, GEN, TOB, TMP, SXT)
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pca_result
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summary(pca_result)
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biplot(pca_result)
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ggplot_pca(pca_result) # a new and convenient plot function
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}
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