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##' TITLE | ||
##' Real-wold data on PM10 pollution in Rouen area, France | ||
##' | ||
##' DESCRIPTION | ||
##' | ||
##' @format The format is a list of 2 component: | ||
##' | ||
##' $x: A data-frame containing input variables: with 100 obs. of 200 | ||
##' variables ; | ||
##' | ||
##' $y: Output variable: a factor with 2 levels "-1" and "1". | ||
##' | ||
##' @examples | ||
##' | ||
##' ##' | ||
##' \dontrun{ | ||
##' These data are TEOM (Tapered Element Oscillating Microbalance) PM10 | ||
##' concentrations from 2004 to 2006 (1096 days) measured by Air Normand, and | ||
##' the associated weather data provided by Météo France, the French national | ||
##' meteorological service, using six different monitoring sites. | ||
##' | ||
##' Six different monitoring stations of the Rouen (Haute Normandie, France) | ||
##' area are considered. The urban station \code{jus}, the traffic station | ||
##' \code{gui}, the second most polluted in the region, and \code{gcm} which is | ||
##' located in an industrial area. In Le Havre, are considered the stations | ||
##' \code{rep} (the most polluted in the region) and \code{hri} located at the | ||
##' seaside. Lastly, the station \code{ail} near Dieppe, because it is rural and | ||
##' coastal, and a priori hardly influenced by social and industrial activity. | ||
##' Grouping by categories: \code{jus} and \code{hri} are background urban | ||
##' monitoring sites, \code{gui} and \code{rep} are urban sites close to | ||
##' traffic, \code{gcm} is industrial and \code{ail} is rural. | ||
##' | ||
##' @format Each object is a data frame. | ||
##' | ||
##' The description of the 18 variables is the following (note that for | ||
##' \code{gcm} station, only the pollutant SO2 is available, and there is no | ||
##' pollutant for \code{ail} station): | ||
##' | ||
##' \describe{ | ||
##' \item{PM10}{Daily mean concentration of PM10, in \eqn{\mu}g/m3} | ||
##' \item{NO, NO2, SO2}{Daily mean concentration of NO, NO2 , SO2, in | ||
##' \eqn{\mu}g/m3} | ||
##' \item{T.min, T.max, T.moy}{Daily minimum, maximum and mean temperature, in | ||
##' °C} | ||
##' \item{DV.maxvv, DV.dom}{Daily maximum speed and dominant wind direction, | ||
##' in ° (for wind direction, 0° corresponds to north)} | ||
##' \item{VV.max, VV.moy}{Daily maximum and mean wind speed, in m/s} | ||
##' \item{PL.som}{Daily rainfall, in mm} | ||
##' \item{HR.min, HR.max, HR.moy}{Daily minimum, maximum and mean relative | ||
##' humidity, in \%} | ||
##' \item{PA.moy}{Daily mean air pressure, in hPa} | ||
##' \item{GTrouen, GTlehavre}{Daily temperature gradient, in °C} | ||
##' } | ||
##' | ||
##' @source Weston, J., Elisseff, A., Schoelkopf, B., Tipping, M. (2003), | ||
##' \emph{Use of the zero norm with linear models and Kernel methods}, | ||
##' J. Machine Learn. Res. 3, 1439-1461 | ||
##' | ||
"jus" | ||
##' @source F.-X. Jollois, J.-M. Poggi, B. Portier, \emph{Three non-linear | ||
##' statistical methods to analyze PM10 pollution in Rouen area}. CSBIGS 3(1): | ||
##' 1-17, 2009 | ||
##' | ||
##' @docType data | ||
##' @name PM10 | ||
NULL | ||
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##' @rdname PM10 | ||
"ail" | ||
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##' @rdname PM10 | ||
"gcm" | ||
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##' @rdname PM10 | ||
"gui" | ||
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##' @rdname PM10 | ||
"hri" | ||
|
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##' @rdname PM10 | ||
"jus" | ||
|
||
##' @rdname PM10 | ||
"rep" |
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