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Last data update: 2014.03.03

R: Pairwise Scatter Plots showing Missing Data Imputations

imputePairs

R Documentation

Pairwise Scatter Plots showing Missing Data Imputations

Description

Creates a scatter plot for each pair of variables in given data, allowing display of imputations for missing values in different colors and symbols than non missing values.

Usage

imputePairs(x, impx, symbols = c(16,1), colors = c("black", "red"), labels,
        panel = points, ..., lower.panel = panel, upper.panel = panel, 
        diag.panel = NULL, text.panel = textPanel, label.pos = 0.5 + 
        has.diag/3, cex.labels = NULL, font.labels = 1, row1attop = TRUE, 
        gap = 0.2)

Arguments

`x`	A numeric vector, matrix, or data frame of observations containing missing values. Categorical variables are not allowed. If a matrix or data frame, rows correspond to observations and columns correspond to variables.
`impx`	The dataset `x` with missing values imputed.
`symbols`	Either an integer or character vector assigning plotting symbols to the nonmissing data and impued values, respectively. The default is a closed circle for the nonmissing data and an open circle for the imputed values.
`colors`	Either an integer or character vector assigning colors to the nonmissing data and impued values, respectively. The default is black for the nonmissing data and red for the imputed values.
`labels`	As in function `pairs`.
`panel`	As in function `pairs`.
`...`	As in function `pairs`.
`lower.panel`	As in function `pairs`.
`upper.panel`	As in function `pairs`.
`diag.panel`	As in function `pairs`.
`text.panel`	As in function `pairs`.
`label.pos`	As in function `pairs`.
`cex.labels`	As in function `pairs`.
`font.labels`	As in function `pairs`.
`row1attop`	As in function `pairs`.
`gap`	As in function `pairs`.

Side Effects

A pairs plot displaying the location of missing and nonmissing values.

References

C. Fraley, A. E. Raftery, T. B. Murphy and L. Scrucca (2012). mclust Version 4 for R: Normal Mixture Modeling for Model-Based Clustering, Classification, and Density Estimation. Technical Report No. 597, Department of Statistics, University of Washington.

Examples

# Note that package 'mix' must be installed.

 
# impute the continuos variables in the stlouis data
stlimp <- imputeData(stlouis[,-(1:3)])

# plot imputed values
imputePairs(stlouis[,-(1:3)], stlimp)