heplots (version 1.6.2)

interpPlot: Plot an Interpolation Between Two Related Data Sets

Description

Plot an interpolation between two related data sets, typically transformations of each other. This function is designed to be used in animations.

Usage

interpPlot(
  xy1,
  xy2,
  alpha,
  xlim,
  ylim,
  points = TRUE,
  add = FALSE,
  col = palette()[1],
  ellipse = FALSE,
  ellipse.args = NULL,
  abline = FALSE,
  col.lines = palette()[2],
  lwd = 2,
  id.method = "mahal",
  labels = rownames(xy1),
  id.n = 0,
  id.cex = 1,
  id.col = palette()[1],
  segments = FALSE,
  segment.col = "darkgray",
  ...
)

Value

Returns invisibly the interpolated XY points.

Arguments

xy1

First data set, a 2-column matrix or data.frame

xy2

Second data set, a 2-column matrix or data.frame

alpha

The value of the interpolation fraction, typically (but not necessarily) 0 <= alpha <= 1).

xlim, ylim

x, y limits for the plot. If not specified, the function uses the ranges of rbind(xy1, xy2).

points

Logical. Whether to plot the points in the current interpolation?

add

Logical. Whether to add to an existing plot?

col

Color for plotted points.

ellipse

logical. TRUE to plot a dataEllipse

ellipse.args

other arguments passed to dataEllipse

abline

logical. TRUE to plot the linear regression line for XY

col.lines

line color

lwd

line width

id.method

How points are to be identified. See showLabels.

labels

observation labels

id.n

Number of points to be identified. If set to zero, no points are identified.

id.cex

Controls the size of the plotted labels. The default is 1

id.col

Controls the color of the plotted labels.

segments

logical. TRUE to draw lines segments from xy1 to xy

segment.col

line color for segments

...

other arguments passed to plot()

Author

Michael Friendly

Details

Points are plotted via the linear interpolation, $$ XY = XY1 + \alpha (XY2 - XY1)$$

The function allows plotting of the data ellipse, the linear regression line, and line segments showing the movement of points.

Interpolations other than linear can be obtained by using a non-linear series of alpha values. For example alpha=sin(seq(0,1,.1)/sin(1) will give a sinusoid interpolation.

See Also

dataEllipse, showLabels, animation

Examples

#################################################
# animate an AV plot from marginal to conditional
#################################################
data(Duncan, package="carData")
duncmod <- lm(prestige ~ income + education, data=Duncan)
mod.mat <- model.matrix(duncmod)

# function to do an animation for one variable
dunc.anim <- function(variable, other, alpha=seq(0, 1, .1)) {
  var <- which(variable==colnames(mod.mat))
  duncdev <- scale(Duncan[,c(variable, "prestige")], scale=FALSE)
  duncav <- lsfit(mod.mat[, -var], cbind(mod.mat[, var], Duncan$prestige), 
          intercept = FALSE)$residuals
  colnames(duncav) <- c(variable, "prestige")
  
  lims <- apply(rbind(duncdev, duncav),2,range)
  
  for (alp in alpha) {
    main <- if(alp==0) paste("Marginal plot:", variable)
      else paste(round(100*alp), "% Added-variable plot:", variable)
    interpPlot(duncdev, duncav, alp, xlim=lims[,1], ylim=lims[,2], pch=16,
      main = main,
      xlab = paste(variable, "| ", alp, other),
      ylab = paste("prestige | ", alp, other),
      ellipse=TRUE, ellipse.args=(list(levels=0.68, fill=TRUE, fill.alpha=alp/2)), 
      abline=TRUE, id.n=3, id.cex=1.2, cex.lab=1.25)
    Sys.sleep(1)
  }
}

# show these in the R console
if(interactive()) {
dunc.anim("income", "education")

dunc.anim("education", "income")
}

############################################
# correlated bivariate data with 2 outliers
# show rotation from data space to PCA space
############################################

set.seed(123345)
x <- c(rnorm(100), 2, -2)
y <- c(x[1:100] + rnorm(100), -2, 2)
XY <- cbind(x=x, y=y)
rownames(XY) <- seq_along(x)
XY <- scale(XY, center=TRUE, scale=FALSE)

# start, end plots

car::dataEllipse(XY, pch=16, levels=0.68, id.n=2)
mod <- lm(y~x, data=as.data.frame(XY))
abline(mod, col="red", lwd=2)

pca <- princomp(XY, cor=TRUE)
scores <- pca$scores
car::dataEllipse(scores, pch=16, levels=0.68, id.n=2)
abline(lm(Comp.2 ~ Comp.1, data=as.data.frame(scores)), lwd=2, col="red")

# show interpolation

# functions for labels, as a function of alpha
main <- function(alpha) {if(alpha==0) "Original data" 
  else if(alpha==1) "PCA scores"
  else paste(round(100*alpha,1), "% interpolation")}
xlab <- function(alpha) {if(alpha==0) "X"
  else if(alpha==1) "PCA.1"
  else paste("X +", alpha, "(X - PCA.1)")}
ylab <- function(alpha) {if(alpha==0) "Y"
  else if(alpha==1) "PCA.2"
  else paste("Y +", alpha, "(Y - PCA.2)")}

interpPCA <- function(XY, alpha = seq(0,1,.1)) {
  XY <- scale(XY, center=TRUE, scale=FALSE)
  if (is.null(rownames(XY))) rownames(XY) <- 1:nrow(XY)
  pca <- princomp(XY, cor=TRUE)
  scores <- pca$scores

  for (alp in alpha) {
    interpPlot(XY, scores, alp, 
      pch=16,
      main = main(alp),
      xlab = xlab(alp),
      ylab = ylab(alp),
      ellipse=TRUE, ellipse.args=(list(levels=0.68, fill=TRUE, fill.alpha=(1-alp)/2)), 
      abline=TRUE, id.n=2, id.cex=1.2, cex.lab=1.25, segments=TRUE)
    Sys.sleep(1)
  }
}

# show in R console
if(interactive()) {
interpPCA(XY)
}

if (FALSE) {
library(animation)
saveGIF({
  interpPCA(XY, alpha <- seq(0,1,.1))},
  movie.name="outlier-demo.gif", ani.width=480, ani.height=480, interval=1.5)

}