tests/fitXYCurve.matrix.R

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

library("aroma.light")

# Simulate data from the model y <- a + bx + x^c + eps(bx)
x <- rexp(1000)
a <- c(2,15)
b <- c(2,1)
c <- c(1,2)
bx <- outer(b,x)
xc <- t(sapply(c, FUN=function(c) x^c))
eps <- apply(bx, MARGIN=2, FUN=function(x) rnorm(length(x), mean=0, sd=0.1*x))
Y <- a + bx + xc + eps
Y <- t(Y)

lim <- c(0,70)
plot(Y, xlim=lim, ylim=lim)

# Fit principal curve through a subset of (y_1, y_2)
subset <- sample(nrow(Y), size=0.3*nrow(Y))
fit <- fitXYCurve(Y[subset,], bandwidth=0.2)

lines(fit, col="red", lwd=2)

# Backtransform (y_1, y_2) keeping y_1 unchanged
YN <- backtransformXYCurve(Y, fit=fit)
points(YN, col="blue")
abline(a=0, b=1, col="red", lwd=2)