Author: Andreas Tille <tille@debian.org>
Last-Update: Sun, 08 Nov 2015 19:39:53 +0100
Description: Remove test using external not packaged function
 Comment says 
  This example requires plot3d() in R.basic [http://www.braju.com/R/]
 But this URL does not exist.

--- a/tests/distanceBetweenLines.R
+++ /dev/null
@@ -1,82 +0,0 @@
-library("aroma.light")
-
-for (zzz in 0) {
-
-# This example requires plot3d() in R.basic [http://www.braju.com/R/]
-if (!require(pkgName <- "R.basic", character.only=TRUE)) break
-
-layout(matrix(1:4, nrow=2, ncol=2, byrow=TRUE))
-
-############################################################
-# Lines in two-dimensions
-############################################################
-x <- list(a=c(1,0), b=c(1,2))
-y <- list(a=c(0,2), b=c(1,1))
-fit <- distanceBetweenLines(ax=x$a, bx=x$b, ay=y$a, by=y$b)
-
-xlim <- ylim <- c(-1,8)
-plot(NA, xlab="", ylab="", xlim=ylim, ylim=ylim)
-
-# Highlight the offset coordinates for both lines
-points(t(x$a), pch="+", col="red")
-text(t(x$a), label=expression(a[x]), adj=c(-1,0.5))
-points(t(y$a), pch="+", col="blue")
-text(t(y$a), label=expression(a[y]), adj=c(-1,0.5))
-
-v <- c(-1,1)*10
-xv <- list(x=x$a[1]+x$b[1]*v, y=x$a[2]+x$b[2]*v)
-yv <- list(x=y$a[1]+y$b[1]*v, y=y$a[2]+y$b[2]*v)
-
-lines(xv, col="red")
-lines(yv, col="blue")
-
-points(t(fit$xs), cex=2.0, col="red")
-text(t(fit$xs), label=expression(x(s)), adj=c(+2,0.5))
-points(t(fit$yt), cex=1.5, col="blue")
-text(t(fit$yt), label=expression(y(t)), adj=c(-1,0.5))
-print(fit)
-
-
-############################################################
-# Lines in three-dimensions
-############################################################
-x <- list(a=c(0,0,0), b=c(1,1,1))  # The 'diagonal'
-y <- list(a=c(2,1,2), b=c(2,1,3))  # A 'fitted' line
-fit <- distanceBetweenLines(ax=x$a, bx=x$b, ay=y$a, by=y$b)
-
-xlim <- ylim <- zlim <- c(-1,3)
-dummy <- t(c(1,1,1))*100
-
-# Coordinates for the lines in 3d
-v <- seq(-10,10, by=1)
-xv <- list(x=x$a[1]+x$b[1]*v, y=x$a[2]+x$b[2]*v, z=x$a[3]+x$b[3]*v)
-yv <- list(x=y$a[1]+y$b[1]*v, y=y$a[2]+y$b[2]*v, z=y$a[3]+y$b[3]*v)
-
-for (theta in seq(30,140,length.out=3)) {
-  plot3d(dummy, theta=theta, phi=30, xlab="", ylab="", zlab="",
-                             xlim=ylim, ylim=ylim, zlim=zlim)
-
-  # Highlight the offset coordinates for both lines
-  points3d(t(x$a), pch="+", col="red")
-  text3d(t(x$a), label=expression(a[x]), adj=c(-1,0.5))
-  points3d(t(y$a), pch="+", col="blue")
-  text3d(t(y$a), label=expression(a[y]), adj=c(-1,0.5))
-
-  # Draw the lines
-  lines3d(xv, col="red")
-  lines3d(yv, col="blue")
-
-  # Draw the two points that are closest to each other
-  points3d(t(fit$xs), cex=2.0, col="red")
-  text3d(t(fit$xs), label=expression(x(s)), adj=c(+2,0.5))
-  points3d(t(fit$yt), cex=1.5, col="blue")
-  text3d(t(fit$yt), label=expression(y(t)), adj=c(-1,0.5))
-
-  # Draw the distance between the two points
-  lines3d(rbind(fit$xs,fit$yt), col="purple", lwd=2)
-}
-
-print(fit)
-
-} # for (zzz in 0)
-rm(zzz)
--- a/tests/wpca.matrix.R
+++ /dev/null
@@ -1,72 +0,0 @@
-library("aroma.light")
-
-for (zzz in 0) {
-
-# This example requires plot3d() in R.basic [http://www.braju.com/R/]
-if (!require(pkgName <- "R.basic", character.only=TRUE)) break
-
-# -------------------------------------------------------------
-# A first example
-# -------------------------------------------------------------
-# Simulate data from the model y <- a + bx + eps(bx)
-x <- rexp(1000)
-a <- c(2,15,3)
-b <- c(2,3,15)
-bx <- outer(b,x)
-eps <- apply(bx, MARGIN=2, FUN=function(x) rnorm(length(x), mean=0, sd=0.1*x))
-y <- a + bx + eps
-y <- t(y)
-
-# Add some outliers by permuting the dimensions for 1/3 of the observations
-idx <- sample(1:nrow(y), size=1/3*nrow(y))
-y[idx,] <- y[idx,c(2,3,1)]
-
-# Down-weight the outliers W times to demonstrate how weights are used
-W <- 10
-
-# Plot the data with fitted lines at four different view points
-N <- 4
-theta <- seq(0,180,length.out=N)
-phi <- rep(30, length.out=N)
-
-# Use a different color for each set of weights
-col <- topo.colors(W)
-
-opar <- par(mar=c(1,1,1,1)+0.1)
-layout(matrix(1:N, nrow=2, byrow=TRUE))
-for (kk in seq(theta)) {
-  # Plot the data
-  plot3d(y, theta=theta[kk], phi=phi[kk])
-
-  # First, same weights for all observations
-  w <- rep(1, length=nrow(y))
-
-  for (ww in 1:W) {
-    # Fit a line using IWPCA through data
-    fit <- wpca(y, w=w, swapDirections=TRUE)
-
-    # Get the first principal component
-    ymid <- fit$xMean
-    d0 <- apply(y, MARGIN=2, FUN=min) - ymid
-    d1 <- apply(y, MARGIN=2, FUN=max) - ymid
-    b <- fit$vt[1,]
-    y0 <- -b * max(abs(d0))
-    y1 <-  b * max(abs(d1))
-    yline <- matrix(c(y0,y1), nrow=length(b), ncol=2)
-    yline <- yline + ymid
-
-    points3d(t(ymid), col=col)
-    lines3d(t(yline), col=col)
-
-    # Down-weight outliers only, because here we know which they are.
-    w[idx] <- w[idx]/2
-  }
-
-  # Highlight the last one
-  lines3d(t(yline), col="red", lwd=3)
-}
-
-par(opar)
-
-} # for (zzz in 0)
-rm(zzz)
--- a/tests/iwpca.matrix.R
+++ /dev/null
@@ -1,76 +0,0 @@
-library("aroma.light")
-
-for (zzz in 0) {
-
-# This example requires plot3d() in R.basic [http://www.braju.com/R/]
-if (!require(pkgName <- "R.basic", character.only=TRUE)) break
-
-# Simulate data from the model y <- a + bx + eps(bx)
-x <- rexp(1000)
-a <- c(2,15,3)
-b <- c(2,3,4)
-bx <- outer(b,x)
-eps <- apply(bx, MARGIN=2, FUN=function(x) rnorm(length(x), mean=0, sd=0.1*x))
-y <- a + bx + eps
-y <- t(y)
-
-# Add some outliers by permuting the dimensions for 1/10 of the observations
-idx <- sample(1:nrow(y), size=1/10*nrow(y))
-y[idx,] <- y[idx,c(2,3,1)]
-
-# Plot the data with fitted lines at four different view points
-opar <- par(mar=c(1,1,1,1)+0.1)
-N <- 4
-layout(matrix(1:N, nrow=2, byrow=TRUE))
-theta <- seq(0,270,length.out=N)
-phi <- rep(20, length.out=N)
-xlim <- ylim <- zlim <- c(0,45)
-persp <- list()
-for (kk in seq_along(theta)) {
-  # Plot the data
-  persp[[kk]] <- plot3d(y, theta=theta[kk], phi=phi[kk], xlim=xlim, ylim=ylim, zlim=zlim)
-}
-
-# Weights on the observations
-# Example a: Equal weights
-w <- NULL
-# Example b: More weight on the outliers (uncomment to test)
-w <- rep(1, length(x)); w[idx] <- 0.8
-
-# ...and show all iterations too with different colors.
-maxIter <- c(seq(1,20,length.out=10),Inf)
-col <- topo.colors(length(maxIter))
-# Show the fitted value for every iteration
-for (ii in seq_along(maxIter)) {
-  # Fit a line using IWPCA through data
-  fit <- iwpca(y, w=w, maxIter=maxIter[ii], swapDirections=TRUE)
-
-  ymid <- fit$xMean
-  d0 <- apply(y, MARGIN=2, FUN=min) - ymid
-  d1 <- apply(y, MARGIN=2, FUN=max) - ymid
-  b <- fit$vt[1,]
-  y0 <- -b * max(abs(d0))
-  y1 <-  b * max(abs(d1))
-  yline <- matrix(c(y0,y1), nrow=length(b), ncol=2)
-  yline <- yline + ymid
-
-  for (kk in seq_along(theta)) {
-    # Set pane to draw in
-    par(mfg=c((kk-1) %/% 2, (kk-1) %% 2) + 1)
-    # Set the viewpoint of the pane
-    options(persp.matrix=persp[[kk]])
-
-    # Get the first principal component
-    points3d(t(ymid), col=col[ii])
-    lines3d(t(yline), col=col[ii])
-
-    # Highlight the last one
-    if (ii == length(maxIter))
-      lines3d(t(yline), col="red", lwd=3)
-  }
-}
-
-par(opar)
-
-} # for (zzz in 0)
-rm(zzz)