aroma.core (version 3.3.1)

matrixBlockPolish.matrix: Applies a polishing function to blocks of rows and columns repeatedly

Description

Applies a polishing function to blocks of rows and columns repeatedly.

Usage

# S3 method for matrix
matrixBlockPolish(z, x=NULL, blockSizes=c(1, 1), FUN, ..., tol=0.01, maxIter=10,
  returnEffects=FALSE)

Value

Returns a named list.

Arguments

z

A numeric KxN matrix.

x

A optional KxNx2 array (or KxN matrix).

blockSizes

A positive integer vector of length two.

FUN

A function taking numeric arguments z and x and returns a numeric object with either a scalar or the same number of elements as in z.

...

Additional arguments passed to the FUN function.

tol

A positive threshold specifying when the algorithm has converged.

maxIter

The maximum number of iterations.

returnEffects

If TRUE, the row and column effects are returned, otherwise not.

Author

Henrik Bengtsson

See Also

medpolish. medianPolish().

Examples

# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# Example 1: median polish
# From example(medpolish) in the 'stats' package
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -

# Deaths from sport parachuting;  from ABC of EDA, p.224:
deaths <- matrix(c(14,15,14, 7,4,7, 8,2,10, 15,9,10, 0,2,0), ncol=3, byrow=TRUE)
rownames(deaths) <- c("1-24", "25-74", "75-199", "200++", "NA")
colnames(deaths) <- 1973:1975
print(deaths)

fit1 <- medpolish(deaths, trace=FALSE)
r1 <- residuals(fit1)
fit2 <- matrixBlockPolish(deaths, FUN=function(y, x, ...) median(y, ...))
r2 <- residuals(fit2)
stopifnot(all.equal(r1,r2))


# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# Example 2: smooth spline polish ("spatial smoothing")
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# From example(image) in 'graphics' package
x <- y <- seq(-4*pi, 4*pi, len=27)
r <- sqrt(outer(x^2, y^2, FUN="+"))
z <- cos(r^2) * exp(-r/6)

fit <- matrixBlockPolish(z, FUN=function(z, x, ...) median(z, ...),
                                                      returnEffects=TRUE)
r1 <- residuals(fit)

fit <- matrixBlockPolish(z, FUN=function(z, x, ...) {
  fit <- smooth.spline(x=x, y=z, ...)
  predict(fit, x=x)$y
}, spar=0.25)
r2 <- residuals(fit)
print(range(r2))
image(r2)