Examples for 'classInt::classIntervals'
Choose univariate class intervals
Aliases: classIntervals print.classIntervals plot.classIntervals nPartitions classIntervals2shingle
Keywords: spatial
### ** Examples if (!require("spData", quietly=TRUE)) { message("spData package needed for examples") run <- FALSE } else { run <- TRUE }
if (run) { data(jenks71, package="spData") 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") } if (run) { 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") plot(classIntervals(jenks71$jenks71, style="dpih"), pal=pal1, main="dpih method") plot(classIntervals(jenks71$jenks71, style="headtails", thr = 1), pal=pal1, main="Head Tails method") par(opar) } if (run) { print(classIntervals(jenks71$jenks71, n=5, style="fixed", fixedBreaks=c(15.57, 25, 50, 75, 100, 155.30))) } if (run) { print(classIntervals(jenks71$jenks71, n=5, style="sd")) } if (run) { print(classIntervals(jenks71$jenks71, n=5, style="equal")) } if (run) { print(classIntervals(jenks71$jenks71, n=5, style="quantile")) } if (run) { set.seed(1) print(classIntervals(jenks71$jenks71, n=5, style="kmeans")) } if (run) { set.seed(1) print(classIntervals(jenks71$jenks71, n=5, style="kmeans", intervalClosure="right")) } if (run) { set.seed(1) print(classIntervals(jenks71$jenks71, n=5, style="kmeans", dataPrecision=0)) } if (run) { set.seed(1) print(classIntervals(jenks71$jenks71, n=5, style="kmeans"), cutlabels=FALSE) } if (run) { print(classIntervals(jenks71$jenks71, n=5, style="hclust", method="complete")) } if (run) { print(classIntervals(jenks71$jenks71, n=5, style="hclust", method="single")) } if (run) { set.seed(1) print(classIntervals(jenks71$jenks71, n=5, style="bclust", verbose=FALSE)) } if (run) { print(classIntervals(jenks71$jenks71, n=5, style="bclust", hclust.method="complete", verbose=FALSE)) } if (run) { print(classIntervals(jenks71$jenks71, n=5, style="fisher")) } if (run) { print(classIntervals(jenks71$jenks71, n=5, style="jenks")) } if (run) { print(classIntervals(jenks71$jenks71, style="dpih")) } if (run) { print(classIntervals(jenks71$jenks71, style="dpih", range.x=c(0, 160))) } if (run) { print(classIntervals(jenks71$jenks71, style="headtails")) } if (run) { print(classIntervals(jenks71$jenks71, style="headtails", thr = .45)) } x <- c(0, 0, 0, 1, 2, 50) print(classIntervals(x, n=3, style="fisher"))
style: fisher
one of 3 possible partitions of this variable into 3 classes
[0,0.5) [0.5,26) [26,50]
3 2 1
print(classIntervals(x, n=3, style="jenks"))
style: jenks
one of 3 possible partitions of this variable into 3 classes
[0,0] (0,2] (2,50]
3 2 1
# 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)
style: jenks
one of 4,082,925 possible partitions of this variable into 5 classes
[0,0] (0,18.77] (18.77,20.05] (20.05,22.4] (22.4,26]
10 15 41 44 10
print(cl2, unique=TRUE)
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 (0,18.77] (18.77,20.05] (20.05,22.4] 26
10 15 41 44 10
## Not run: ##D set.seed(1) ##D n <- 1e+05 ##D x <- runif(n) ##D classIntervals(x, n=5, style="sd") ##D classIntervals(x, n=5, style="pretty") ##D classIntervals(x, n=5, style="equal") ##D classIntervals(x, n=5, style="quantile") ##D # the class intervals found vary a little because of sampling ##D classIntervals(x, n=5, style="kmeans") ##D classIntervals(x, n=5, style="fisher") ##D classIntervals(x, n=5, style="fisher") ##D classIntervals(x, n=5, style="fisher") ## End(Not run) have_units <- FALSE if (require(units, quietly=TRUE)) have_units <- TRUE
if (have_units) { set.seed(1) x_units <- set_units(sample(seq(1, 100, 0.25), 100), km/h) ## Not run: ##D classIntervals(x_units, n=5, style="sd") ## End(Not run) } if (have_units) { classIntervals(x_units, n=5, style="pretty") }
style: pretty
one of 3,764,376 possible partitions of this [km/h] variable into 5 classes
[0,20) [20,40) [40,60) [60,80) [80,100]
18 21 19 19 23
if (have_units) { ## Not run: ##D classIntervals(x_units, n=5, style="equal") ## End(Not run) }
NULL
if (have_units) { classIntervals(x_units, n=5, style="quantile") }
style: quantile
one of 3,764,376 possible partitions of this [km/h] variable into 5 classes
[4,21.95) [21.95,41.5) [41.5,64.05) [64.05,82.8) [82.8,99.5]
20 20 20 20 20
if (have_units) { ## Not run: ##D classIntervals(x_units, n=5, style="kmeans") ## End(Not run) }
NULL
if (have_units) { classIntervals(x_units, n=5, style="fisher") }
style: fisher
one of 3,764,376 possible partitions of this [km/h] variable into 5 classes
[4,19.375) [19.375,39.375) [39.375,60.5) [60.5,79.625) [79.625,99.5]
18 21 19 19 23
if (have_units) { classIntervals(x_units, style="headtails") }
style: headtails
one of 99 possible partitions of this [km/h] variable into 2 classes
[4,51.5225) [51.5225,99.5]
49 51
## Not run: ##D st <- Sys.time() ##D x_POSIXt <- sample(st+((0:500)*3600), 100) ##D fx <- st+((0:5)*3600)*100 ##D classIntervals(x_POSIXt, style="fixed", fixedBreaks=fx) ##D classIntervals(x_POSIXt, n=5, style="sd") ##D classIntervals(x_POSIXt, n=5, style="pretty") ##D classIntervals(x_POSIXt, n=5, style="equal") ##D classIntervals(x_POSIXt, n=5, style="quantile") ##D classIntervals(x_POSIXt, n=5, style="kmeans") ##D classIntervals(x_POSIXt, n=5, style="fisher") ##D classIntervals(x_POSIXt, style="headtails") ## End(Not run) # see vignette for further details ## Not run: ##D # Head Tails method is suitable for right-sided heavy-tailed distributions ##D set.seed(1234) ##D # Heavy tails----- ##D # Pareto distributions a=7 b=14 ##D paretodist <- 7 / (1 - runif(100)) ^ (1 / 14) ##D # Lognorm ##D lognormdist <- rlnorm(100) ##D # Weibull ##D weibulldist <- rweibull(100, 1, scale = 5) ##D ##D pal1 <- c("wheat1", "red3") ##D opar <- par(mfrow = c(1, 3)) ##D plot(classIntervals(paretodist, style = "headtails"), ##D pal = pal1, ##D main = "HeadTails: Pareto Dist.") ##D plot(classIntervals(lognormdist, style = "headtails"), ##D pal = pal1, ##D main = "HeadTails: LogNormal Dist.") ##D plot(classIntervals(weibulldist, style = "headtails"), ##D pal = pal1, ##D main = "HeadTails: Weibull Dist.") ##D plot(classIntervals(paretodist, n = 5, style = "fisher"), ##D pal = pal1, ##D main = "Fisher: Pareto Dist.") ##D plot(classIntervals(lognormdist, n = 7, style = "fisher"), ##D pal = pal1, ##D main = "Fisher: LogNormal Dist.") ##D plot(classIntervals(weibulldist, n= 4, style = "fisher"), ##D pal = pal1, ##D main = "Fisher: Weibull Dist.") ##D par(opar) ##D ##D ##D #Non heavy tails, thr should be increased----- ##D ##D #Normal dist ##D normdist <- rnorm(100) ##D #Left-tailed truncated Normal distr ##D leftnorm <- rep(normdist[normdist < mean(normdist)], 2) ##D # Uniform distribution ##D unifdist <- runif(100) ##D opar <- par(mfrow = c(2, 3)) ##D plot(classIntervals(normdist, style = "headtails"), ##D pal = pal1, ##D main = "Normal Dist.") ##D plot(classIntervals(leftnorm, style = "headtails"), ##D pal = pal1, ##D main = "Truncated Normal Dist.") ##D plot(classIntervals(unifdist, style = "headtails"), ##D pal = pal1, ##D main = "Uniform Dist.") ##D # thr should be increased for non heavy-tailed distributions ##D plot( ##D classIntervals(normdist, style = "headtails", thr = .6), ##D pal = pal1, ##D main = "Normal Dist. thr = .6" ##D ) ##D plot( ##D classIntervals(leftnorm, style = "headtails", thr = .6), ##D pal = pal1, ##D main = "Truncated Normal Distribution thr = .6" ##D ) ##D plot( ##D classIntervals(unifdist, style = "headtails", thr = .6), ##D pal = pal1, ##D main = "Uniform Distribution thr = .6" ##D ) ##D par(opar) ## End(Not run)
[Package classInt version 0.4-7 Index]