plotrix (version 2.8-2)

hierobrk: Perform a nested breakdown of numeric values

Description

Breaks down a numeric or categorical element of a data frame by one or more categorical elements.

Usage

hierobrk(formula,data,maxlevels=10,mct=mean,lmd=NULL,umd=lmd,trueval=NA)

Arguments

formula

A formula with a numeric element of a data frame on the left and one or more categorical elements on the right.

data

A data frame containing the elements in formula.

maxlevels

The maximum number of levels in any categorical element. Mainly to prevent the mess caused by breaking down by a huge number of categories.

mct

The measure of central tendency function to use (default is the mean).

lmd

The lower measure of dispersion function to use (default is standard error).

umd

The upper measure of dispersion function to use.

trueval

The value to use in calculating proportions of a categorical response variable. See Details.

Value

• A list with four elements:
• mctlist
  The array produced by the function passed as the mct argument.
• lcllist
  The array produced by the function passed as the lmd argument.
• ucllist
  The array produced by the function passed as the umd argument.
• barlabels
  A list containing the unique elements of the variables on the right side of the formula (or the levels if they are factors), in the order in which they appear in the formula. These will be the default labels for the hierobarp function.
• This function is similar to brkdn in the prettyR package, but is structured to be used with the hierobarp function.

Details

hierobrk performs the breakdown of a numeric element of a data frame by one or more categorical elements. For each category and optionally subcategories, the variable on the left of the formula is summarized as specified by the functions named in num.desc. If trueval is not NA, hierobrk will calculate the proportion of trueval values in the response variable out of the total valid responses. Remember that lcllist and ucllist will be meaningless in this case.

The user should take care when specifying different summary functions. hierobarp expects a measure of central tendency as the first function and measures of dispersion as the second and third, if "error bars" are to be displayed.

See Also

by

Examples

test.df<-data.frame(Age=rnorm(100,25,10),
  Sex=sample(c("M","F"),100,TRUE),
  Marital=sample(c("M","X","S","W"),100,TRUE),
  Employ=sample(c("FT","PT","NO"),100,TRUE))
 hierobrk(formula=Age~Sex+Marital+Employ,data=test.df)