confounderPlot: Confounder Plot

In CluMix: Clustering and Visualization of Mixed-Type Data

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

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

Author(s)

Manuela Hummel

See Also

similarity.variables

Examples

data(mixdata)

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

CluMix documentation built on Jan. 21, 2019, 5:05 p.m.