classInt (version 0.1-7)

classIntervals: Choose univariate class intervals

Description

The function provides a uniform interface to finding class intervals for continuous numerical variables, for example for choosing colours or symbols for plotting. Class intervals are non-overlapping, and the classes are left-closed --- see findInterval. Argument values to the style chosen are passed through the dot arguments.

Usage

classIntervals(var, n, style = "quantile", rtimes = 3, ...)
## S3 method for class 'classIntervals':
plot(x, pal, ...) 
## S3 method for class 'classIntervals':
print(x, digits = getOption("digits"), ..., under="under", over="over", between="-", cutlabels=FALSE) 
nPartitions(x)

Arguments

var

a continuous numerical variable

n

number of classes required, if missing, nclass.Sturges is used

style

chosen style: one of "fixed", "sd", "equal", "pretty", "quantile", "kmeans", "hclust", "bclust", or "fisher"

rtimes

number of replications of var to catenate and jitter; may be used with styles "kmeans" or "bclust" in case they have difficulties reaching a classification

...

arguments to be passed to the functions called in each style

x

"classIntervals" object for printing or plotting

under

character string value for "under" in printed table labels

over

character string value for "over" in printed table labels

between

character string value for "between" in printed table labels

digits

minimal number of significant digits in printed table labels

cutlabels

use cut-style labels in printed table labels

pal

a character vector of at least two colour names for colour coding the class intervals in an ECDF plot; colorRampPalette is used internally to create the correct number of colours

Value

• an object of class "classIntervals":
• var
  the input variable
• brks
  a vector of breaks
• style
  the style used
• parameters
  parameter values used in finding breaks
• nobs
  number of different finite values in the input variable
• call
  this function's call

Details

The "fixed" style permits a "classIntervals" object to be specified with given breaks, set in the fixedBreaks argument; the length of fixedBreaks should be n+1; this style can be used to insert rounded break values.

The "sd" style chooses breaks based on pretty of the centred and scaled variables, and may have a number of classes different from n; the returned par= includes the centre and scale values.

The "equal" style divides the range of the variable into n parts.

The "pretty" style chooses a number of breaks not necessarily equal to n using pretty, but likely to be legible; arguments to pretty may be passed through ....

The "quantile" style provides quantile breaks; arguments to quantile may be passed through ....

The "kmeans" style uses kmeans to generate the breaks; it may be anchored using set.seed; the pars attribute returns the kmeans object generated; if kmeans fails, a jittered input vector containing rtimes replications of var is tried --- with few unique values in var, this can prove necessary; arguments to kmeans may be passed through ....

The "hclust" style uses hclust to generate the breaks using hierarchical clustering; the pars attribute returns the hclust object generated, and can be used to find other breaks using getHclustClassIntervals; arguments to hclust may be passed through ....

The "bclust" style uses bclust to generate the breaks using bagged clustering; it may be anchored using set.seed; the pars attribute returns the bclust object generated, and can be used to find other breaks using getBclustClassIntervals; if bclust fails, a jittered input vector containing rtimes replications of var is tried --- with few unique values in var, this can prove necessary; arguments to bclust may be passed through ....

The "fisher" style uses the algorithm proposed by W. D. Fisher (1958) and discussed by Slocum et al. (2005) as the Fisher-Jenks algorithm; added here thanks to Hisaji Ono.

References

Armstrong, M. P., Xiao, N., Bennett, D. A., 2003. "Using genetic algorithms to create multicriteria class intervals for choropleth maps". Annals, Association of American Geographers, 93 (3), 595--623; Jenks, G. F., Caspall, F. C., 1971. "Error on choroplethic maps: definition, measurement, reduction". Annals, Association of American Geographers, 61 (2), 217--244; Dent, B. D., 1999, Cartography: thematic map design. McGraw-Hill, Boston, 417 pp.; Slocum TA, McMaster RB, Kessler FC, Howard HH 2005 Thematic Cartography and Geographic Visualizatio, Prentice Hall, Upper Saddle River NJ.; Fisher, W. D. 1958 "On grouping for maximum homogeneity", Journal of the American Statistical Association, 53, pp. 789--798 (http://lib.stat.cmu.edu/cmlib/src/cluster/fish.f)

See Also

pretty, quantile, kmeans, hclust, bclust, findInterval, colorRampPalette, nclass.Sturges

Examples

data(jenks71)
pal1 <- c("wheat1", "red3")
opar <- par(mfrow=c(3,3))
plot(classIntervals(jenks71$jenks71, n=5, style="fixed", fixedBreaks=c(15.57, 25, 50, 75, 100, 155.30)), pal=pal1, main="Fixed")
plot(classIntervals(jenks71$jenks71, n=5, style="sd"), pal=pal1, main="Pretty standard deviations")
plot(classIntervals(jenks71$jenks71, n=5, style="equal"), pal=pal1, main="Equal intervals")
plot(classIntervals(jenks71$jenks71, n=5, style="quantile"), pal=pal1, main="Quantile")
set.seed(1)
plot(classIntervals(jenks71$jenks71, n=5, style="kmeans"), pal=pal1, main="K-means")
plot(classIntervals(jenks71$jenks71, n=5, style="hclust", method="complete"), pal=pal1, main="Complete cluster")
plot(classIntervals(jenks71$jenks71, n=5, style="hclust", method="single"), pal=pal1, main="Single cluster")
set.seed(1)
plot(classIntervals(jenks71$jenks71, n=5, style="bclust", verbose=FALSE), pal=pal1, main="Bagged cluster")
plot(classIntervals(jenks71$jenks71, n=5, style="fisher"), pal=pal1, main="Fisher's method")
par(opar)
classIntervals(jenks71$jenks71, n=5, style="fixed", fixedBreaks=c(15.57, 25, 50, 75, 100, 155.30))
classIntervals(jenks71$jenks71, n=5, style="sd")
classIntervals(jenks71$jenks71, n=5, style="equal")
classIntervals(jenks71$jenks71, n=5, style="quantile")
set.seed(1)
classIntervals(jenks71$jenks71, n=5, style="kmeans")
classIntervals(jenks71$jenks71, n=5, style="hclust", method="complete")
classIntervals(jenks71$jenks71, n=5, style="hclust", method="single")
set.seed(1)
classIntervals(jenks71$jenks71, n=5, style="bclust", verbose=FALSE)
classIntervals(jenks71$jenks71, n=5, style="bclust", hclust.method="complete", verbose=FALSE)
classIntervals(jenks71$jenks71, n=5, style="fisher")