classIntervals {classInt} | R Documentation |
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.
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)
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", "fisher", or "jenks" |
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 |
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.
The "jenks" style has been ported from Jenks' Basic code, and has been checked for consistency with ArcView, ArcGIS, and MapInfo (with some remaining differences); added here thanks to Hisaji Ono.
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 |
Roger Bivand <Roger.Bivand@nhh.no>
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)
pretty
, quantile
, kmeans
, hclust
, bclust
, findInterval
, colorRampPalette
, nclass.Sturges
data(jenks71) pal1 <- c("wheat1", "red3") opar <- par(mfrow=c(2,3)) 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$jenks71, n=5, style="fisher"), pal=pal1, main="Fisher's method") plot(classIntervals(jenks71$jenks71, n=5, style="jenks"), pal=pal1, main="Jenks' 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") classIntervals(jenks71$jenks71, n=5, style="jenks")