tests/wpca2.matrix.R

In aroma.light: Light-Weight Methods for Normalization and Visualization of Microarray Data using Only Basic R Data Types

library("aroma.light")

# -------------------------------------------------------------
# A second example
# -------------------------------------------------------------
# Data
x <- c(1,2,3,4,5)
y <- c(2,4,3,3,6)

opar <- par(bty="L")
opalette <- palette(c("blue", "red", "black"))
xlim <- ylim <- c(0,6)

# Plot the data and the center mass
plot(x,y, pch=16, cex=1.5, xlim=xlim, ylim=ylim)
points(mean(x), mean(y), cex=2, lwd=2, col="blue")


# Linear regression y ~ x
fit <- lm(y ~ x)
abline(fit, lty=1, col=1)

# Linear regression y ~ x through without intercept
fit <- lm(y ~ x - 1)
abline(fit, lty=2, col=1)


# Linear regression x ~ y
fit <- lm(x ~ y)
c <- coefficients(fit)
b <- 1/c[2]
a <- -b*c[1]
abline(a=a, b=b, lty=1, col=2)

# Linear regression x ~ y through without intercept
fit <- lm(x ~ y - 1)
b <- 1/coefficients(fit)
abline(a=0, b=b, lty=2, col=2)


# Orthogonal linear "regression"
fit <- wpca(cbind(x,y))

b <- fit$vt[1,2]/fit$vt[1,1]
a <- fit$xMean[2]-b*fit$xMean[1]
abline(a=a, b=b, lwd=2, col=3)

# Orthogonal linear "regression" without intercept
fit <- wpca(cbind(x,y), center=FALSE)
b <- fit$vt[1,2]/fit$vt[1,1]
a <- fit$xMean[2]-b*fit$xMean[1]
abline(a=a, b=b, lty=2, lwd=2, col=3)

legend(xlim[1],ylim[2], legend=c("lm(y~x)", "lm(y~x-1)", "lm(x~y)",
          "lm(x~y-1)", "pca", "pca w/o intercept"), lty=rep(1:2,3),
                     lwd=rep(c(1,1,2),each=2), col=rep(1:3,each=2))

palette(opalette)
par(opar)