ise, mise, amise {ks} | R Documentation |
The global errors ISE (Integrated Squared Error), MISE (Mean Integrated Squared Error) of kernel density estimates for normal densities, for 2- to 6-dimensional data, and AMISE (Asymptotic Mean Integrated Squared Error) for 2-dimensional data.
ise.mixt(x, H, mus, Sigmas, props) mise.mixt(H, mus, Sigmas, props, samp) amise.mixt(H, mus, Sigmas, props, samp)
x |
matrix of data values |
H |
bandwidth matrix |
mus |
(stacked) matrix of mean vectors |
Sigmas |
(stacked) matrix of variance matrices |
props |
vector of mixing proportions |
samp |
sample size |
For normal mixture densities, ISE and MISE have exact formulas for all dimensions, and AMISE has an exact form for 2 dimensions. See Wand & Jones (1995).
ISE, MISE or AMISE value.
Remember that ISE is a random variable that depends on the data
x
; and that MISE and AMISE are non-random and don't
depend on the data.
Wand, M.P. & Jones, M.C. (1995) Kernel Smoothing. Chapman & Hall. London.
samp <- 50 d <- 3 mus <- rbind(rep(0,d), rep(1,d)) Sigmas <- 0.25*rbind(diag(d), diag(d)) props <- c(2/3, 1/3) x <- rmvnorm.mixt(samp, mus, Sigmas, props) H <- Hpi(x) ise.mixt(x, H, mus, Sigmas, props) mise.mixt(H, mus, Sigmas, props, samp)