stats (version 3.3.1)

medpolish: Median Polish of a Matrix

Description

Fits an additive model using Tukey's median polish procedure.

Usage

medpolish(x, eps = 0.01, maxiter = 10, trace.iter = TRUE, na.rm = FALSE)

Arguments

x
a numeric matrix.
eps
real number greater than 0. A tolerance for convergence: see ‘Details’.
maxiter
the maximum number of iterations
trace.iter
logical. Should progress in convergence be reported?
na.rm
logical. Should missing values be removed?

Value

An object of class medpolish with the following named components:
overall
the fitted constant term.
row
the fitted row effects.
col
the fitted column effects.
residuals
the residuals.
name
the name of the dataset.

Details

The model fitted is additive (constant + rows + columns). The algorithm works by alternately removing the row and column medians, and continues until the proportional reduction in the sum of absolute residuals is less than eps or until there have been maxiter iterations. The sum of absolute residuals is printed at each iteration of the fitting process, if trace.iter is TRUE. If na.rm is FALSE the presence of any NA value in x will cause an error, otherwise NA values are ignored.

medpolish returns an object of class medpolish (see below). There are printing and plotting methods for this class, which are invoked via by the generics print and plot.

References

Tukey, J. W. (1977). Exploratory Data Analysis, Reading Massachusetts: Addison-Wesley.

See Also

median; aov for a mean instead of median decomposition.

Examples

Run this code
require(graphics)

## Deaths from sport parachuting;  from ABC of EDA, p.224:
deaths <-
    rbind(c(14,15,14),
          c( 7, 4, 7),
          c( 8, 2,10),
          c(15, 9,10),
          c( 0, 2, 0))
dimnames(deaths) <- list(c("1-24", "25-74", "75-199", "200++", "NA"),
                         paste(1973:1975))
deaths
(med.d <- medpolish(deaths))
plot(med.d)
## Check decomposition:
all(deaths ==
    med.d$overall + outer(med.d$row,med.d$col, "+") + med.d$residuals)

Run the code above in your browser using DataCamp Workspace