imputePairs: Pairwise Scatter Plots showing Missing Data Imputations

Description Usage Arguments Side Effects References See Also Examples

View source: R/impute.R

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

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imputePairs(data, dataImp, 
            symbols = c(1,16), 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

data

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.

dataImp

The dataset data 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.

Schafer J. L. (1997). Analysis of Imcomplete Multivariate Data, Chapman and Hall.

See Also

pairs, imputeData

Examples

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## Not run: 
# Note that package 'mix' must be installed
data(stlouis, package = "mix")
 
# impute the continuos variables in the stlouis data
stlimp <- imputeData(stlouis[,-(1:3)])

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

## End(Not run)

mclust documentation built on May 23, 2017, 9:05 a.m.

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