classIntervals: Choose univariate class intervals

In classInt: 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. classIntervals2shingle converts a classIntervals object into a shingle. Labels generated in methods are like those found in cut unless cutlabels=FALSE.

Usage

classIntervals(var, n, style = "quantile", rtimes = 3, ...,
 intervalClosure = c("left", "right"), dataPrecision = NULL)
## S3 method for class 'classIntervals'
plot(x, pal, ...) 
## S3 method for class 'classIntervals'
print(x, digits = getOption("digits"), ..., 
 under="under", over="over", between="-", cutlabels=TRUE, unique=FALSE) 
nPartitions(x) 
classIntervals2shingle(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", "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
intervalClosure	default “left”, allows specification of whether partition intervals are closed on the left or the right (added by Richard Dunlap). Note that the sense of interval closure is hard-coded as “right”-closed whenstyle="jenks" (see Details below).
dataPrecision	default NULL, permits rounding of the interval endpoints (added by Richard Dunlap)
...	arguments to be passed to the functions called in each style
x	"classIntervals" object for printing, conversion to shingle, or plotting
under	character string value for "under" in printed table labels if cutlabels=FALSE
over	character string value for "over" in printed table labels if cutlabels=FALSE
between	character string value for "between" in printed table labels if cutlabels=FALSE
digits	minimal number of significant digits in printed table labels
cutlabels	default TRUE, use cut-style labels in printed table labels
unique	default FALSE; if TRUE, collapse labels of single-value classes
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

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.

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. Note that the sense of interval closure is reversed from the other styles, and in this implementation has to be right-closed - use cutlabels=TRUE in findColours on the object returned to show the closure clearly, and use findCols to extract the classes for each value.

Value

an object of class "classIntervals":

var	the input variable
brks	a vector of breaks

and attributes:

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
intervalClosure	string, whether closure is “left” or “right”
dataPrecision	the data precision used for printing interval values in the legend returned by findColours, and in the print method for classIntervals objects. If intervalClosure is “left”, the value returned is ceiling of the data value multiplied by 10 to the dataPrecision power, divided by 10 to the dataPrecision power.

Note

From version 0.1-11, the default representation has been changed to use cutlabels=TRUE, and representation within intervals has been corrected, thanks to Richard Dunlap. From version 0.1-15, the print method drops the calculation of the possible number of combinations of observations into classes, which generated warnings for n > 170.

Author(s)

Roger Bivand <[e-mail]>

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 Visualization, 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

findColours, findCols, pretty, quantile, kmeans, hclust, bclust, findInterval, colorRamp, nclass, shingle

Examples

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$jenks71, n=5, style="kmeans")
set.seed(1)
classIntervals(jenks71$jenks71, n=5, style="kmeans", intervalClosure="right")
set.seed(1)
classIntervals(jenks71$jenks71, n=5, style="kmeans", dataPrecision=0)
set.seed(1)
print(classIntervals(jenks71$jenks71, n=5, style="kmeans"), cutlabels=FALSE)
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")
x <- c(0, 0, 0, 1, 2, 50)
classIntervals(x, n=3, style="fisher")
classIntervals(x, n=3, style="jenks")

# Argument 'unique' will collapse the label of classes containing a 
# single value. This is particularly useful for 'censored' variables
# that contain for example many zeros. 

data_censored<-c(rep(0,10), rnorm(100, mean=20,sd=1),rep(26,10))
plot(density(data_censored))

cl2<-classIntervals(data_censored, n=5, style="jenks", dataPrecision=2)

print(cl2, unique=FALSE)
print(cl2, unique=TRUE)

Example output

style: fixed
  one of 3,921,225 possible partitions of this variable into 5 classes
 [15.57,25)     [25,50)     [50,75)    [75,100) [100,155.3] 
          6          34          42          14           6 
style: sd
  one of 75,287,520 possible partitions of this variable into 6 classes
[5.514407,30.89691) [30.89691,56.27941) [56.27941,81.66191) [81.66191,107.0444) 
                 10                  47                  31                   9 
[107.0444,132.4269) [132.4269,157.8094] 
                  4                   1 
style: equal
  one of 3,921,225 possible partitions of this variable into 5 classes
  [15.57,43.516)  [43.516,71.462)  [71.462,99.408) [99.408,127.354) 
              35               44               17                4 
 [127.354,155.3] 
               2 
style: quantile
  one of 3,921,225 possible partitions of this variable into 5 classes
 [15.57,33.822) [33.822,50.114) [50.114,57.454) [57.454,73.368)  [73.368,155.3] 
             21              20              20              20              21 
style: kmeans
  one of 3,921,225 possible partitions of this variable into 5 classes
 [15.57,36.905)  [36.905,53.33)  [53.33,72.185) [72.185,105.95)  [105.95,155.3] 
             25              26              29              17               5 
style: kmeans
  one of 3,921,225 possible partitions of this variable into 5 classes
 [15.57,36.905]  (36.905,53.33]  (53.33,72.185] (72.185,105.95]  (105.95,155.3] 
             25              26              29              17               5 
style: kmeans
  one of 3,921,225 possible partitions of this variable into 5 classes
  [16,37)   [37,54)   [54,73)  [73,106) [106,156] 
       25        26        29        17         5 
style: kmeans
  one of 3,921,225 possible partitions of this variable into 5 classes
   under 36.905  36.905 - 53.33  53.33 - 72.185 72.185 - 105.95     over 105.95 
             25              26              29              17               5 
style: hclust
  one of 3,921,225 possible partitions of this variable into 5 classes
  [15.57,54.81)  [54.81,74.405) [74.405,105.95)  [105.95,143.4)   [143.4,155.3] 
             55              27              15               4               1 
style: hclust
  one of 3,921,225 possible partitions of this variable into 5 classes
 [15.57,20.995) [20.995,105.95)  [105.95,125.7)   [125.7,143.4)   [143.4,155.3] 
              4              93               3               1               1 
style: bclust
  one of 3,921,225 possible partitions of this variable into 5 classes
   [15.57,43.3)   [43.3,82.175) [82.175,105.95)  [105.95,143.4)   [143.4,155.3] 
             35              53               9               4               1 
style: bclust
  one of 3,921,225 possible partitions of this variable into 5 classes
[15.57,29.84) [29.84,54.81) [54.81,90.16) [90.16,125.7) [125.7,155.3] 
           10            45            38             7             2 
style: fisher
  one of 3,921,225 possible partitions of this variable into 5 classes
   [15.57,43.3)    [43.3,61.36)  [61.36,78.475) [78.475,105.95)  [105.95,155.3] 
             35              33              18              11               5 
style: jenks
  one of 3,921,225 possible partitions of this variable into 5 classes
 [15.57,41.2]  (41.2,60.66] (60.66,77.29] (77.29,100.1] (100.1,155.3] 
           35            33            18            11             5 
style: fisher
  one of 3 possible partitions of this variable into 3 classes
 [0,0.5) [0.5,26)  [26,50] 
       3        2        1 
style: jenks
  one of 3 possible partitions of this variable into 3 classes
 [0,0]  (0,2] (2,50] 
     3      2      1 
style: jenks
  one of 4,082,925 possible partitions of this variable into 5 classes
        [0,0]     (0,18.91] (18.91,20.33] (20.33,23.15]    (23.15,26] 
           10            21            43            36            10 
style: jenks
  one of 4,082,925 possible partitions of this variable into 5 classes
Class found with one single (possibly repeated) value: changed label
            0     (0,18.91] (18.91,20.33] (20.33,23.15]            26 
           10            21            43            36            10 

classInt documentation built on May 2, 2019, 6:30 p.m.