getBreaks {cartography} | R Documentation |

A function to classify continuous variables.

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

`v` |
a vector of numeric values. |

`nclass` |
a number of classes |

`method` |
a classification method; one of "fixed", "sd", "equal", "pretty", "quantile", "kmeans", "hclust", "bclust", "fisher", "jenks", "dpih", "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). |

`...` |
further arguments of |

"fixed", "sd", "equal", "pretty", "quantile", "kmeans", "hclust",
"bclust", "fisher", "jenks" and "dpih" are `classIntervals`

methods. You may need to pass additional arguments for some of them.

Jenks ("jenks" method) and Fisher-Jenks ("fisher" method) 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.

A numeric vector of breaks

This function is mainly a wrapper of `classIntervals`

+
"arith", "em", "q6", "geom" and "msd" methods.

library(sf) mtq <- st_read(system.file("gpkg/mtq.gpkg", package="cartography")) var <- mtq$MED # Histogram hist(var, probability = TRUE, breaks = 20) 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) # Pretty breaks breaks <- getBreaks(v = var, nclass = 4, method = "pretty") hist(var, probability = TRUE, breaks = breaks, col = "#F0D9F9", axes = FALSE) rug(var) axis(1, at = breaks) axis(2) abline(v = med, col = "blue", lwd = 6) # kmeans method breaks <- getBreaks(v = var, nclass = 4, method = "kmeans") hist(var, probability = TRUE, breaks = breaks, col = "#F0D9F9") rug(var) abline(v = med, col = "blue", lwd = 6) # 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)

[Package *cartography* version 3.0.0 Index]