Skip to contents

Kernel Density Estimates of strictly positive distributions.

logdensity.Rd

The function logdensity computes kernel density estimates (KDE) of strictly positive distributions by performing the KDE in the log domain and then transforming the result back again. The syntax and function structure is largely borrowed from the function density in package stats.

Usage

logdensity(
  x,
  bw = "nrd0",
  adjust = 1,
  kernel = "gaussian",
  weights = NULL,
  n = 512,
  from,
  to,
  cut = 3,
  na.rm = FALSE
)

Arguments

x

the data from which the estimate is to be computed.

bw

the smoothing bandwidth to be used. Can also be can also be a character string giving a rule to choose the bandwidth. Like density defaults to "nrd0". All options in help(bw.nrd) are available as well as "bw.logCV" and "bw.logG".

adjust

the bandwidth used is actually adjust*bw.

kernel

a character string giving the smoothing kernel to be used. Choose from "gaussian", "epanechnikov", "triangular", "uniform", "laplace" and "logistic". Default value is "gaussian".

weights

numeric vector of non-negative observation weights of the same length as x.

n

the number of equally spaced points at which the density is to be estimated. Note that these are equally spaced in the original domain.

from, to

the left and right-most points of the grid at which the density is to be estimated; the defaults are cut * bw outside of range(x).

cut

by default, the values of from and to are cut bandwidths beyond the extremes of the data

na.rm

logical; if TRUE, missing values are removed from x. If FALSE any missing values cause an error.

Value

An object with class "density". See help(density) for details.

References

Charpentier, A., & Flachaire, E. (2015). Log-transform kernel density estimation of income distribution. L'Actualite economique, 91(1-2), 141-159.

Wand, M. P., Marron, J. S., & Ruppert, D. (1991). Transformations in density estimation. Journal of the American Statistical Association, 86(414), 343-353.

See also

density, plot.density, logdensity_fft, bw.nrd, bw.logCV, bw.logG.

Examples

logdensity(abs(rnorm(100)), from =.1, to=2, kernel='triangular')
#> 
#> Call:
#> 	logdensity(x = abs(rnorm(100)), kernel = "triangular", from = 0.1,     to = 2)
#> 
#> Data: abs(rnorm(100)) (100 obs.);	Bandwidth 'bw' = 0.3718
#> 
#>        x               y         
#>  Min.   :0.100   Min.   :0.1532  
#>  1st Qu.:0.575   1st Qu.:0.2646  
#>  Median :1.050   Median :0.4160  
#>  Mean   :1.050   Mean   :0.4376  
#>  3rd Qu.:1.525   3rd Qu.:0.5382  
#>  Max.   :2.000   Max.   :1.1026