https://github.com/cran/CluMix
Revision 0bae9aab6eb8a689817ffbfd31fe978223989b66 authored by Manuela Hummel on 19 September 2017, 08:26:31 UTC, committed by cran-robot on 19 September 2017, 08:26:31 UTC
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Tip revision: 0bae9aab6eb8a689817ffbfd31fe978223989b66 authored by Manuela Hummel on 19 September 2017, 08:26:31 UTC
version 2.1
Tip revision: 0bae9aa
confounderPlot.Rd
\name{confounderPlot}
\alias{confounderPlot}
\title{Confounder Plot}

\description{Plots similarities of all variables to an outcome variable against similarities of all variables to a predictor of interest}

\usage{
confounderPlot(data, S, x, y, labels, method = c("associationMeasures", "distcor"), 
returnS = FALSE, plotLegend = TRUE, col, pch, font, cex.text, xlim, ylim, ...)
}

\arguments{
  \item{data}{data frame with variables of interest}
  \item{S}{similarity matrix; if missing it will be calculated from data by \code{\link{similarity.variables}}}
  \item{x}{name of the predictor variable (as used in \code{data} and \code{S}) of main interest, for which confounders / collinearities shall be detected}
  \item{y}{name of the outcome variable (as used in \code{data} and \code{S})}
  \item{labels}{variable names used for plotting; have to be in corresponding order with columns of \code{data}; if missing, names of \code{data} are used}
    \item{method}{method to calculate similarities: combination of association measures (\code{'associationMeasures'}) or distance correlation (\code{'distcor'})}
  \item{returnS}{shall similarity matrix be returned?}
  \item{plotLegend}{shall (default) legend be shown, indicating categorical and continuous variables}
  \item{col}{symbol and label color; by default categorical variables are shown in purple, continuous variables in black}
  \item{pch}{plotting symbol, default 16}
  \item{font}{font of plotted labels; by default names of variables \code{x} and \code{y} are shown in bold}
  \item{cex.text}{size of plotted labels}
  \item{xlim, ylim}{axis limits}
  \item{\dots}{graphical parameters passed to \code{plot}}
}

\details{The similarities of all variables in a dataset with two variables of special interest (i.e. predictor and outcome of a regression model) are simultaneously visualized in a scatter plot, where the x-axis shows similarities to the predictor and the y-axis similarities to the outcome. The height of the predictor variable's point indicates its association with the outcome and hence its predicting ability. Variables in the upper right part are potential confounders for which prediction model should be adjusted, or collinear variables that should be removed. Variables in the lower right part are strongly related to the predictor, but not associated with the outcome. Variables very close to the outcome variable's point are potential surrogate outcomes.}

\value{Scatterplot of variable similarities. Chosen predictor and outcome variables are highlighted in bold. Categorical/quantitative variables are shown in purple/black by default.}

%\references{}

\author{Manuela Hummel}

\seealso{\code{\link{similarity.variables}}}

\examples{
data(mixdata)

confounderPlot(mixdata, x="X2.quant", y="X1.cat")
}

\keyword{ hplot }
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