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kdemode <- function(data,type=c("SJ","nrd0")){
# Return mode of a vector as calculated using KDE
#
# Inputs:
# data One-dimensional vector of data
# type Bandwidth selection:
# - SJ: Sheater and Jones method
# - nrd0: Silverman heuristic
#
# Outputs:
# mode Estimated mode value.
#
# Example:
# data <- rnorm(200,mean=0,sd=1)
# kdemode(data)
#
# Notes:
# For a discussion of the selection between mean, median and mode
# for the combination of forecasts see:
# Kourentzes, N., Barrow, D. K., & Crone, S. F. (2014).
# Neural network ensemble operators for time series forecasting.
# Expert Systems with Applications, Volume 41, Issue 9, Pages 4235-4244
#
# Nikolaos Kourentzes, 2017 <nikolaos@kourentzes.com>
# Defaults
type <- match.arg(type,c("SJ","nrd0"))
# Fix from/to
from <- min(data)-0.1*diff(range(data))
to <- max(data)+0.1*diff(range(data))
# Calculate KDE
ks <- density(data,bw="SJ",n=512,from=from,to=to)
x <- ks$x
f <- ks$y
h <- ks$bw
# Find mode
mo <- x[which(f==max(f))][1] # mode
return(list(mode=mo,xd=x,fd=f,h=h))
}
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