demoKde-package {demoKde}

Kernel density estimation demonstration and exploration

Description

Teaching demonstration code for kernel density estimates. KDEs are computed in native R code directly from the definition. The slight innovation here is to replace the observations by their frequencies in a fine partition of the range of the sample. Kernels may be supplied as a function in a standard form, thus allowing alternative kernel functions to be devised and empirically investigated. A wide selection of kernel function is also provided with the package. The canonical reference is B. W. Silverman, (1998). See Refrences.

Author(s)

Bill Venables

Maintainer: Bill Venables, <[e-mail]>

References

See https://en.wikipedia.org/wiki/Kernel_(statistics) for details of the kernel functions. See also B. W. Silverman, (1998) Density Estimation for Statistics and Data Analysis. Taylor & Franis Group, Boca Raton. doi:10.1201/9781315140919.

See Also

density

Examples

if(require("graphics")) {
  with(MASS::Boston, {
      Criminality <- log(crim)
      hist(Criminality, freq=FALSE, main="", border="grey", las=1)
      lines(stats::density(Criminality), col="skyblue", lwd=8)
      lines(kde(Criminality))
      lines(kde(Criminality, kernel = kernelUniform), col="red")
      rug(jitter(Criminality), col="blue")
      legend("topright", c("density histogram",
        "KDE gaussian (denstiy)", "KDE gaussian (kde)",
        "KDE rectangular (kde)"), lty = "solid", lwd=c(1,8,1,1),
        col=c("grey", "skyblue", "black", "red"), bty="n")
  })
}