psych (version 1.5.8)

pairs.panels: SPLOM, histograms and correlations for a data matrix

Description

Adapted from the help page for pairs, pairs.panels shows a scatter plot of matrices (SPLOM), with bivariate scatter plots below the diagonal, histograms on the diagonal, and the Pearson correlation above the diagonal. Useful for descriptive statistics of small data sets. If lm=TRUE, linear regression fits are shown for both y by x and x by y. Correlation ellipses are also shown. Points may be given different colors depending upon some grouping variable.

Usage

## S3 method for class 'panels':
pairs(x, smooth = TRUE, scale = FALSE, density=TRUE,ellipses=TRUE,
     digits = 2,method="pearson", pch = 20, lm=FALSE,cor=TRUE,jiggle=FALSE,factor=2, 
     hist.col="cyan",show.points=TRUE,rug=TRUE, breaks = "Sturges",cex.cor=1, ...)

Arguments

x

a data.frame or matrix

smooth

TRUE draws loess smooths

scale

TRUE scales the correlation font by the size of the absolute correlation.

density

TRUE shows the density plots as well as histograms

ellipses

TRUE draws correlation ellipses

lm

Plot the linear fit rather than the LOESS smoothed fits.

digits

the number of digits to show

method

method parameter for the correlation ("pearson","spearman","kendall")

pch

The plot character (defaults to 20 which is a '.').

cor

If plotting regressions, should correlations be reported?

jiggle

Should the points be jittered before plotting?

factor

factor for jittering (1-5)

hist.col

What color should the histogram on the diagonal be?

show.points

If FALSE, do not show the data points, just the data ellipses and smoothed functions

rug

if TRUE (default) draw a rug under the histogram, if FALSE, don't draw the rug

breaks

If specified, allows control for the number of breaks in the histogram (see the hist function)

cex.cor

If this is specified, this will change the size of the text in the correlations. this allows one to also change the size of the points in the plot by specifying the normal cex values. If just specifying cex, it will change the character size, if cex.cor i

...

other options for pairs

Value

• A scatter plot matrix (SPLOM) is drawn in the graphic window. The lower off diagonal draws scatter plots, the diagonal histograms, the upper off diagonal reports the Pearson correlation (with pairwise deletion).

  If lm=TRUE, then the scatter plots are drawn above and below the diagonal, each with a linear regression fit. Useful to show the difference between regression lines.

Details

Shamelessly adapted from the pairs help page. Uses panel.cor, panel.cor.scale, and panel.hist, all taken from the help pages for pairs. Also adapts the ellipse function from John Fox's car package.

pairs.panels is most useful when the number of variables to plot is less than about 6-10. It is particularly useful for an initial overview of the data.

To show different groups with different colors, use a plot character (pch) between 21 and 25 and then set the background color to vary by group. (See the second example).

When plotting more than about 10 variables, it is useful to set the gap parameter to something less than 1 (e.g., 0). Alternatively, consider using cor.plot

In addition, when plotting more than about 100-200 cases, it is useful to set the plotting character to be a point. (pch=".")

Sometimes it useful to draw the correlation ellipses and best fitting lowess without the points. (points.false=TRUE).

See Also

pairs which is the base from which pairs.panels is derived, cor.plot to do a heat map of correlations, and scatter.hist to draw a single correlation plot with histograms and best fitted lines.

To find the probability "significance" of the correlations using normal theory, use corr.test. To find confidence intervals using boot strapping procedures, use cor.ci. To graphically show confidence intervals, see cor.plot.upperLowerCi.

Examples

pairs.panels(attitude)   #see the graphics window
data(iris)
pairs.panels(iris[1:4],bg=c("red","yellow","blue")[iris$Species],
        pch=21,main="Fisher Iris data by Species") #to show color grouping

pairs.panels(iris[1:4],bg=c("red","yellow","blue")[iris$Species],
  pch=21+as.numeric(iris$Species),main="Fisher Iris data by Species",hist.col="red") 
   #to show changing the diagonal

#demonstrate not showing the data points
data(sat.act)
pairs.panels(sat.act,show.points=FALSE)
#better yet is to show the points as a period
pairs.panels(sat.act,pch=".")
#show many variables with 0 gap between scatterplots
# data(bfi)
# pairs.panels(bfi,show.points=FALSE,gap=0)