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Get class intervals

Source: R/mf_get_breaks.R
mf_get_breaks.Rd

A function to classify continuous variables.

This function is a wrapper for classIntervals with some additional methods.

Usage

mf_get_breaks(x, nbreaks, breaks, k = 1, central = FALSE, ...)

Arguments

x

a vector of numeric values. NA and Inf values are not used in the classification.

nbreaks

a number of classes

breaks

a classification method; one of "fixed", "sd", "equal", "pretty", "quantile", "kmeans", "hclust", "bclust", "fisher", "jenks", "dpih", "q6", "Q6", geom", "arith", "em", "msd" or "ckmeans" (see Details)

k

number of standard deviation for "msd" method (see Details)

central

creation of a central class for "msd" method (see Details)

...

further arguments of classIntervals

Value

A numeric vector of breaks

Details

"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.

The "jenks", "fisher" and "ckmeans" methods are based on the same concept of natural breaks and and produce similar groupings.

  • The "jenks" method produces class boundaries falling on data points and is slow.

  • The "fisher" method produces class boundaries located more conveniently between data points, and is faster than the "jenks" method.

  • The "ckmeans" method produces exactly the same class boundaries as the "fisher" method, but is much faster. It uses the optimal univariate k-means method from the Ckmeans.1d.dp package. If the "ckmeans" method is selected but the Ckmeans.1d.dp package is not installed then the "fisher" method is used.

The relative speeds of these three methods may vary depending on the number of data points and the number of classes.

The "q6" method uses the following quantile probabilities: 0, 0.05, 0.275, 0.5, 0.725, 0.95, 1.

The "Q6" method uses the following quantile probabilities: 0, 0.05, 0.25, 0.5, 0.75, 0.95, 1.

The "geom" method is based on a geometric progression along the variable values, all values must be strictly greater than zero.

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 nbreaks parameter is not relevant, use k and central instead. k indicates the extent of each class in share of standard deviation. If central=TRUE then the mean value is the center of a class else the mean is a break value.

See also

classIntervals

Examples

mtq <- mf_get_mtq()
mf_get_breaks(x = mtq$MED, nbreaks = 6, breaks = "quantile")
#> [1] 11929.0 13667.0 14786.0 15685.5 16860.0 18622.0 21761.0