cartography (version 2.1.2)

getBreaks: Discretization

Description

A function to discretize continuous variables.

Usage

getBreaks(v, nclass = NULL, method = "quantile", k = 1,
  middle = FALSE)

Arguments

v

a vector of numeric values.

nclass

a number of classes

method

a discretization method; one of "sd", "equal", "quantile", "fisher-jenks","q6", "geom", "arith", "em" or "msd" (see Details).

k

number of standard deviation for "msd" method (see Details)..

middle

creation of a central class for "msd" method (see Details).

Value

A numeric vector of breaks

Details

"sd", "equal", "quantile" and "fisher-jenks" are classIntervals methods. Jenks and Fisher-Jenks algorithms are based on the same principle and give quite similar results but Fisher-Jenks is much faster. The "q6" method uses the following quantile probabilities: 0, 0.05, 0.275, 0.5, 0.725, 0.95, 1. The "geom" method is based on a geometric progression along the variable values. The "arith" method is based on an arithmetic progression along the variable values. The "em" method is based on nested averages computation. The "msd" method is based on the mean and the standard deviation of a numeric vector. The nclass parameter is not relevant, use k and middle instead. k indicates the extent of each class in share of standard deviation. If middle=TRUE then the mean value is the center of a class else the mean is a break value.

Examples

# NOT RUN {
library(sp)
data("nuts2006")
# Create the natality rate
var <- nuts2.df$birth_2008/nuts2.df$pop2008 * 1000

# Histogram
hist(var, probability = TRUE, nclass = 30)
rug(var)
moy <- mean(var)
med <- median(var)
abline(v = moy, col = "red", lwd = 3)
abline(v = med, col = "blue", lwd = 3)

# Quantile intervals
breaks <- getBreaks(v = var, nclass = 6, method = "quantile")
hist(var, probability = TRUE, breaks = breaks, col = "#F0D9F9")
rug(var)
med <- median(var)
abline(v = med, col = "blue", lwd = 3)

# Geometric intervals
breaks <- getBreaks(v = var, nclass = 8, method = "geom")
hist(var, probability = TRUE, breaks = breaks, col = "#F0D9F9")
rug(var)

# Mean and standard deviation (msd)
breaks <- getBreaks(v = var, method = "msd", k = 1, middle = TRUE)
hist(var, probability = TRUE, breaks = breaks, col = "#F0D9F9")
rug(var)
moy <- mean(var)
sd <- sd(var)
abline(v = moy, col = "red", lwd = 3)
abline(v = moy + 0.5 * sd, col = "blue", lwd = 3)
abline(v = moy - 0.5 * sd, col = "blue", lwd = 3)
# }