% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/data.R
\docType{data}
\name{china_climate_month.hist}
\alias{china_climate_month.hist}
\title{Chinese Climate Monthly Histogram Dataset}
\format{
A data frame with 60 observations (stations) and 168
histogram-valued variables. Variables follow the pattern
\code{variable_Month} (e.g., \code{mean.temp_Jan}). The 14 climate
variables are: mean pressure, mean temperature, mean max/min
temperature, total precipitation, sunshine duration, mean cloud amount,
mean relative humidity, snow days, dominant wind direction, mean wind
speed, dominant wind frequency, extreme max/min temperature.
}
\source{
HistDAWass R package (\code{China_Month} dataset).
}
\usage{
data(china_climate_month.hist)
}
\description{
Histogram-valued monthly climate data for 60 Chinese weather stations.
Each station has 14 climate variables measured across 12 months
(168 histogram columns total). Histograms are reduced to 10 decile
bins from the original HistDAWass distributions.
}
\section{Metadata}{

\tabular{ll}{
  \strong{Sample size (n)} \tab 60 \cr
  \strong{Variables (p)} \tab 168 \cr
  \strong{Subject area} \tab Climate \cr
  \strong{Symbolic format} \tab Histogram \cr
  \strong{Analytical tasks} \tab Clustering
}
}

\examples{
data(china_climate_month.hist)
}
\references{
Irpino, A. and Verde, R. (2015). Basic statistics for distributional
symbolic variables: a new metric-based approach. \emph{Advances in
Data Analysis and Classification}, 9(2), 143--175.

Original data from the HistDAWass R package (\code{China_Month} dataset).
}
\keyword{datasets}
\keyword{histogram}
