Nothing
R/datasets.R
In mev: Modelling of Extreme Values
#' French wind data
#'
#' Daily mean wind speed (in km/h) at four stations in the south of France, namely Cap Cépet (\code{S1}), Lyon St-Exupéry (\code{S2}), Marseille Marignane (\code{S3}) and Montélimar (\code{S4}).
#' The data includes observations from January 1976 until April 2023; days containing missing values are omitted.
#' @source European Climate Assessment and Dataset project \url{https://www.ecad.eu/}
#' @references
#' Klein Tank, A.M.G. and Coauthors, 2002. Daily dataset of 20th-century surface air temperature and precipitation series for the
#' European Climate Assessment. Int. J. of Climatol., 22, 1441-1453.
#' @name frwind
#' @docType data
#' @format A data frame with 17209 observations and 8 variables:
#' \describe{
#' \item{\code{date}}{date of measurement}
#' \item{\code{S1}}{wind speed (in km/h) at Cap Cépet}
#' \item{\code{S2}}{wind speed (in km/h) at Lyon Saint-Exupéry}
#' \item{\code{S3}}{wind speed (in km/h) at Marseille Marignane}
#' \item{\code{S4}}{wind speed (in km/h) at Montélimar}
#' \item{\code{H2}}{humidity (in percentage) at Lyon Saint-Exupéry}
#' \item{\code{T2}}{mean temperature (in degree Celcius) at Lyon Saint-Exupéry}
#' }
#' The \code{metadata} attribute includes latitude and longitude (in degrees, minutes, seconds), altitude (in m), station name and station id.
#' @examples
#' data(frwind, package = "mev")
#' head(frwind)
#' attr(frwind, which = "metadata")
NULL
#' Eskdalemuir Observatory Daily Rainfall
#'
#' This dataset contains exceedances of 30mm for daily
#' cumulated rainfall observations over the period 1970-1986.
#' These data were aggregated from hourly series.
#'
#' The station is one of the rainiest of the whole UK, with an average 1554m of cumulated rainfall per year.
#' The data consisted of 6209 daily observations, of which 4409 were non-zero.
#' Only the 93 largest observations are provided.
#' @name eskrain
#' @docType data
#' @format a vector with 93 daily cumulated rainfall measurements exceeding 30mm.
#' @source Met Office.
NULL
#' Venice Sea Levels
#'
#' The \code{venice} data contains the 10 largest yearly sea levels (in cm)
#' from 1887 until 2019. Only the yearly maximum is available for 1922
#' and the six largest observations for 1936.
#'
#' @format a data frame with 133 rows and 11 columns containing the year of the measurement (first column)
#' and ordered 10-largest yearly observations, reported in decreasing order from largest (\code{r1}) to smallest (\code{r10}).
#'
#' @note Smith (1986) notes that the annual maxima seems to fluctuate around a constant sea level
#' up to 1930 or so, after which there is potential linear trend. Records of threshold exceedances above
#' 80 cm (reported on the website) indicate that observations are temporally clustered.
#'
#' The observations from 1931 until 1981 can be found in
#' Table 1 in Smith (1986), who reported data from Pirazzoli (1982).
#' The values from 1983 until 2019 were extracted by Anthony Davison from the City
#' of Venice website (accessed in May 2020) and are licensed under the CC BY-NC-SA 3.0 license.
#' The Venice City website indicates
#' that later measurements were recorded by an instrument located in Punta Salute.
#'
#'
#' @references Smith, R. L. (1986) Extreme value theory based on the \emph{r}
#' largest annual events. \emph{Journal of Hydrology} \bold{86}, 27–43.
#' @references Pirazzoli, P., 1982. Maree estreme a Venezia (periodo 1872-1981). \emph{Acqua Aria} \bold{10}, 1023-1039.
#' @references Coles, S. G. (2001) \emph{An Introduction to Statistical Modelling of Extreme Values}. London: Springer.
#' @source City of Venice, Historical archive <https://www.comune.venezia.it/node/6214>. Last accessed November 5th, 2020.
#' @name venice
#' @seealso \code{\link[ismev]{venice}}
#' @docType data
NULL
#' Best 200 times of Women 1500m Track
#'
#' 200 all-time best performance (in seconds) of women 1500-meter run.
#' @format a vector of size 200
#' @source <http://www.alltime-athletics.com/w_1500ok.htm>, accessed 14.08.2018
#' @name w1500m
#' @docType data
NULL
#' Maiquetia Daily Rainfall
#'
#' Daily cumulated rainfall (in mm) at Maiquetia airport, Venezuela.
#' The observations cover the period from January 1961 to December 1999.
#' The original series had missing days in February 1996 (during which there were
#' 2 days with 1hr each of light rain) and January 1998 (no rain). These were replaced by zeros.
#'
#' @format a vector of size 14244 containing daily rainfall (in mm),
#' @source J.R. Cordova and M. González, accessed 25.11.2018 from <https://rss.onlinelibrary.wiley.com/hub/journal/14679876/series-c-datasets>
#' @name maiquetia
#' @references Coles, S. and L.R. Pericchi (2003). Anticipating Catastrophes through Extreme Value Modelling, \emph{Applied Statistics}, \bold{52}(4), 405-416.
#' @references Coles, S., Pericchi L.R. and S. Sisson (2003). A fully probabilistic approach to extreme rainfall modeling, \emph{Journal of Hydrology}, \bold{273}, 35-50.
#' @docType data
#' @examples
#' \dontrun{
#' data(maiquetia, package = "mev")
#' day <- seq.Date(from = as.Date("1961-01-01"), to = as.Date("1999-12-31"), by = "day")
#' nzrain <- maiquetia[substr(day, 1, 4) < 1999 & maiquetia > 0]
#' fit.gpd(nzrain, threshold = 30, show = TRUE)
#'
#' }
NULL
#' River Nidd Flow
#'
#' The data consists of exceedances over the threshold 65 cubic meter per second of the River Nidd at Hunsingore Weir, for 35 years of data between 1934 and 1969.
#'
#' @source Natural Environment Research Council (1975). \emph{Flood Studies Report}, volume 4. pp. 235--236.
#' @references Davison, A.C. and R.L. Smith (1990). Models for Exceedances over High Thresholds, \emph{Journal of the Royal Statistical Society. Series B (Methodological)}, \bold{52}(3), 393--442. With discussion.
#' @format a vector of size 154
#' @name nidd
#' @seealso \code{nidd.thresh} from the \code{evir} package
#' @docType data
NULL
#' Magnetic storms
#'
#' Absolute magnitude of 373 geomagnetic storms lasting more than 48h with absolute magnitude (dst) larger than 100 in 1957-2014.
#'
#' @source Aki Vehtari
#' @references World Data Center for Geomagnetism, Kyoto, M. Nose, T. Iyemori, M. Sugiura, T. Kamei (2015), \emph{Geomagnetic Dst index}, doi:10.17593/14515-74000.
#' @docType data
#' @note For a detailed article presenting the derivation of the Dst index, see \code{http://wdc.kugi.kyoto-u.ac.jp/dstdir/dst2/onDstindex.html}
#' @format a vector of size 373
#' @name geomagnetic
NULL
#' Abisko rainfall
#'
#' Daily non-zero rainfall measurements in Abisko (Sweden) from January 1913 until December 2014.
#' @param date \code{Date} of the measurement
#' @param precip rainfall amount (in mm)
#' @format a data frame with 15132 rows and two variables
#' @name abisko
#' @source Abisko Scientific Research Station
#' @references A. Kiriliouk, H. Rootzén, J. Segers and J.L. Wadsworth (2019), \emph{Peaks over thresholds modeling With multivariate generalized Pareto distributions}, Technometrics, \bold{61}(1), 123--135, \code{DOI:10.1080/00401706.2018.1462738}
NULL
#' Nutrient data
#'
#' Interview component of survey 'What we eat in
#' America'. These are extracted from the 2015–2016 National Health and Nutrition Examination Survey (NHANES, \url{https://wwwn.cdc.gov/nchs/nhanes/Default.aspx}) report and consist of the total nutrients for all food and beverage intake ingested over a 24 hours period.
#' @source National Center for Health Statistics, \url{https://wwwn.cdc.gov/Nchs/Nhanes/2015-2016/DR1TOT_I.XPT}
#'
#' @details Note that the sample design oversampled specific population targets and that only respondants are provided. The website contains more information about sampling weights. There are multiple missing records.
#' @format A data frame with 9544 rows and 38 variables:
#' \describe{
#' \item{\code{prot}}{proteins (in grams)}
#' \item{\code{carb}}{carbonhydrate (in gram)}
#' \item{\code{sugr}}{total sugars (in gram)}
#' \item{\code{fibe}}{dietary fibers (in grams)}
#' \item{\code{tfat}}{total fat (in grams)}
#' \item{\code{sfat}}{saturated fat (in grams)}
#' \item{\code{mfat}}{monounsaturated fat (in grams)}
#' \item{\code{pfat}}{polyunsaturated fat (in grams)}
#' \item{\code{chol}}{cholesterol (in milligrams)}
#' \item{\code{atoc}}{vitamin E as alpha-tocopherol (in milligrams)}
#' \item{\code{ret}}{retinol (in micrograms)}
#' \item{\code{vara}}{Vitamin A as retinol activity equivalents (in micrograms).}
#' \item{\code{acar}}{alpha-carotene (in micrograms)}
#' \item{\code{bcar}}{beta-carotene (in micrograms)}
#' \item{\code{cryp}}{beta-cryptoxanthin (in micrograms)}
#' \item{\code{lyco}}{lycopene (in micrograms)}
#' \item{\code{lz}}{lutein and zeaxanthin (in micrograms).}
#' \item{\code{vb1}}{thiamin (vitamin B1, in milligrams)}
#' \item{\code{vb2}}{riboflavin (vitamin B2, in milligrams)}
#' \item{\code{niac}}{niacin (in milligrams)}
#' \item{\code{vb6}}{vitamin B5 (in milligrams)}
#' \item{\code{fola}}{total folate (in micrograms)}
#' \item{\code{fa}}{folic acid (in micrograms)}
#' \item{\code{ff}}{food folate (in micrograms)}
#' \item{\code{chl}}{total choline (in milligrams)}
#' \item{\code{vb12}}{vitamin B12 (in micrograms)}
#' \item{\code{vc}}{vitamin C (in milligrams)}
#' \item{\code{vd}}{vitamin D (comprising D2 and D3, in micrograms)}
#' \item{\code{vk}}{vitamin K (in micrograms)}
#' \item{\code{calc}}{calcium (in milligrams)}
#' \item{\code{phos}}{phosphorus (in milligrams)}
#' \item{\code{magn}}{magnesium (in milligrams)}
#' \item{\code{iron}}{iron (in milligrams)}
#' \item{\code{zinc}}{zinc (in milligrams)}
#' \item{\code{copp}}{copper (in milligrams)}
#' \item{\code{sodi}}{sodium (in milligrams)}
#' \item{\code{pota}}{potassium (in milligrams)}
#' \item{\code{sele}}{selenium (in micrograms)}
#'}
#' @name nutrients
#' @note These data are subject to a data user agreement, available at \url{https://www.cdc.gov/nchs/data_access/restrictions.htm}
"nutrients"
#' Deaths from pandemics
#'
#' The data base contains estimated records of the number of deaths from pandemics.
#' @format A data frame with 72 rows and 8 variables:
#' \describe{
#' \item{\code{event}}{name of the event}
#' \item{\code{startyear}}{start year of the event}
#' \item{\code{endyear}}{end year of the event}
#' \item{\code{lower}}{lower bound on estimated deaths (in thousands)}
#' \item{\code{average}}{average estimated deaths (in thousands)}
#' \item{\code{upper}}{upper bound on estimated deaths (in thousands)}
#' \item{\code{saverage}}{scaled average of estimated deaths (in thousands)}
#' \item{\code{population}}{estimated population at risk (in thousands)}
#' }
#' @name pandemics
#' @source Cirillo, P. and N.N. Taleb (2020). \emph{Tail risk of contagious diseases}. Nat. Phys. \bold{16}, 606–613 (2020). <doi:10.1038/s41567-020-0921-x>
"pandemics"
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#' French wind data
#'
#' Daily mean wind speed (in km/h) at four stations in the south of France, namely Cap Cépet (\code{S1}), Lyon St-Exupéry (\code{S2}), Marseille Marignane (\code{S3}) and Montélimar (\code{S4}).
#' The data includes observations from January 1976 until April 2023; days containing missing values are omitted.
#' @source European Climate Assessment and Dataset project \url{https://www.ecad.eu/}
#' @references
#' Klein Tank, A.M.G. and Coauthors, 2002. Daily dataset of 20th-century surface air temperature and precipitation series for the
#' European Climate Assessment. Int. J. of Climatol., 22, 1441-1453.
#' @name frwind
#' @docType data
#' @format A data frame with 17209 observations and 8 variables:
#' \describe{
#' \item{\code{date}}{date of measurement}
#' \item{\code{S1}}{wind speed (in km/h) at Cap Cépet}
#' \item{\code{S2}}{wind speed (in km/h) at Lyon Saint-Exupéry}
#' \item{\code{S3}}{wind speed (in km/h) at Marseille Marignane}
#' \item{\code{S4}}{wind speed (in km/h) at Montélimar}
#' \item{\code{H2}}{humidity (in percentage) at Lyon Saint-Exupéry}
#' \item{\code{T2}}{mean temperature (in degree Celcius) at Lyon Saint-Exupéry}
#' }
#' The \code{metadata} attribute includes latitude and longitude (in degrees, minutes, seconds), altitude (in m), station name and station id.
#' @examples
#' data(frwind, package = "mev")
#' head(frwind)
#' attr(frwind, which = "metadata")
NULL
#' Eskdalemuir Observatory Daily Rainfall
#'
#' This dataset contains exceedances of 30mm for daily
#' cumulated rainfall observations over the period 1970-1986.
#' These data were aggregated from hourly series.
#'
#' The station is one of the rainiest of the whole UK, with an average 1554m of cumulated rainfall per year.
#' The data consisted of 6209 daily observations, of which 4409 were non-zero.
#' Only the 93 largest observations are provided.
#' @name eskrain
#' @docType data
#' @format a vector with 93 daily cumulated rainfall measurements exceeding 30mm.
#' @source Met Office.
NULL
#' Venice Sea Levels
#'
#' The \code{venice} data contains the 10 largest yearly sea levels (in cm)
#' from 1887 until 2019. Only the yearly maximum is available for 1922
#' and the six largest observations for 1936.
#'
#' @format a data frame with 133 rows and 11 columns containing the year of the measurement (first column)
#' and ordered 10-largest yearly observations, reported in decreasing order from largest (\code{r1}) to smallest (\code{r10}).
#'
#' @note Smith (1986) notes that the annual maxima seems to fluctuate around a constant sea level
#' up to 1930 or so, after which there is potential linear trend. Records of threshold exceedances above
#' 80 cm (reported on the website) indicate that observations are temporally clustered.
#'
#' The observations from 1931 until 1981 can be found in
#' Table 1 in Smith (1986), who reported data from Pirazzoli (1982).
#' The values from 1983 until 2019 were extracted by Anthony Davison from the City
#' of Venice website (accessed in May 2020) and are licensed under the CC BY-NC-SA 3.0 license.
#' The Venice City website indicates
#' that later measurements were recorded by an instrument located in Punta Salute.
#'
#'
#' @references Smith, R. L. (1986) Extreme value theory based on the \emph{r}
#' largest annual events. \emph{Journal of Hydrology} \bold{86}, 27–43.
#' @references Pirazzoli, P., 1982. Maree estreme a Venezia (periodo 1872-1981). \emph{Acqua Aria} \bold{10}, 1023-1039.
#' @references Coles, S. G. (2001) \emph{An Introduction to Statistical Modelling of Extreme Values}. London: Springer.
#' @source City of Venice, Historical archive <https://www.comune.venezia.it/node/6214>. Last accessed November 5th, 2020.
#' @name venice
#' @seealso \code{\link[ismev]{venice}}
#' @docType data
NULL
#' Best 200 times of Women 1500m Track
#'
#' 200 all-time best performance (in seconds) of women 1500-meter run.
#' @format a vector of size 200
#' @source <http://www.alltime-athletics.com/w_1500ok.htm>, accessed 14.08.2018
#' @name w1500m
#' @docType data
NULL
#' Maiquetia Daily Rainfall
#'
#' Daily cumulated rainfall (in mm) at Maiquetia airport, Venezuela.
#' The observations cover the period from January 1961 to December 1999.
#' The original series had missing days in February 1996 (during which there were
#' 2 days with 1hr each of light rain) and January 1998 (no rain). These were replaced by zeros.
#'
#' @format a vector of size 14244 containing daily rainfall (in mm),
#' @source J.R. Cordova and M. González, accessed 25.11.2018 from <https://rss.onlinelibrary.wiley.com/hub/journal/14679876/series-c-datasets>
#' @name maiquetia
#' @references Coles, S. and L.R. Pericchi (2003). Anticipating Catastrophes through Extreme Value Modelling, \emph{Applied Statistics}, \bold{52}(4), 405-416.
#' @references Coles, S., Pericchi L.R. and S. Sisson (2003). A fully probabilistic approach to extreme rainfall modeling, \emph{Journal of Hydrology}, \bold{273}, 35-50.
#' @docType data
#' @examples
#' \dontrun{
#' data(maiquetia, package = "mev")
#' day <- seq.Date(from = as.Date("1961-01-01"), to = as.Date("1999-12-31"), by = "day")
#' nzrain <- maiquetia[substr(day, 1, 4) < 1999 & maiquetia > 0]
#' fit.gpd(nzrain, threshold = 30, show = TRUE)
#'
#' }
NULL
#' River Nidd Flow
#'
#' The data consists of exceedances over the threshold 65 cubic meter per second of the River Nidd at Hunsingore Weir, for 35 years of data between 1934 and 1969.
#'
#' @source Natural Environment Research Council (1975). \emph{Flood Studies Report}, volume 4. pp. 235--236.
#' @references Davison, A.C. and R.L. Smith (1990). Models for Exceedances over High Thresholds, \emph{Journal of the Royal Statistical Society. Series B (Methodological)}, \bold{52}(3), 393--442. With discussion.
#' @format a vector of size 154
#' @name nidd
#' @seealso \code{nidd.thresh} from the \code{evir} package
#' @docType data
NULL
#' Magnetic storms
#'
#' Absolute magnitude of 373 geomagnetic storms lasting more than 48h with absolute magnitude (dst) larger than 100 in 1957-2014.
#'
#' @source Aki Vehtari
#' @references World Data Center for Geomagnetism, Kyoto, M. Nose, T. Iyemori, M. Sugiura, T. Kamei (2015), \emph{Geomagnetic Dst index}, doi:10.17593/14515-74000.
#' @docType data
#' @note For a detailed article presenting the derivation of the Dst index, see \code{http://wdc.kugi.kyoto-u.ac.jp/dstdir/dst2/onDstindex.html}
#' @format a vector of size 373
#' @name geomagnetic
NULL
#' Abisko rainfall
#'
#' Daily non-zero rainfall measurements in Abisko (Sweden) from January 1913 until December 2014.
#' @param date \code{Date} of the measurement
#' @param precip rainfall amount (in mm)
#' @format a data frame with 15132 rows and two variables
#' @name abisko
#' @source Abisko Scientific Research Station
#' @references A. Kiriliouk, H. Rootzén, J. Segers and J.L. Wadsworth (2019), \emph{Peaks over thresholds modeling With multivariate generalized Pareto distributions}, Technometrics, \bold{61}(1), 123--135, \code{DOI:10.1080/00401706.2018.1462738}
NULL
#' Nutrient data
#'
#' Interview component of survey 'What we eat in
#' America'. These are extracted from the 2015–2016 National Health and Nutrition Examination Survey (NHANES, \url{https://wwwn.cdc.gov/nchs/nhanes/Default.aspx}) report and consist of the total nutrients for all food and beverage intake ingested over a 24 hours period.
#' @source National Center for Health Statistics, \url{https://wwwn.cdc.gov/Nchs/Nhanes/2015-2016/DR1TOT_I.XPT}
#'
#' @details Note that the sample design oversampled specific population targets and that only respondants are provided. The website contains more information about sampling weights. There are multiple missing records.
#' @format A data frame with 9544 rows and 38 variables:
#' \describe{
#' \item{\code{prot}}{proteins (in grams)}
#' \item{\code{carb}}{carbonhydrate (in gram)}
#' \item{\code{sugr}}{total sugars (in gram)}
#' \item{\code{fibe}}{dietary fibers (in grams)}
#' \item{\code{tfat}}{total fat (in grams)}
#' \item{\code{sfat}}{saturated fat (in grams)}
#' \item{\code{mfat}}{monounsaturated fat (in grams)}
#' \item{\code{pfat}}{polyunsaturated fat (in grams)}
#' \item{\code{chol}}{cholesterol (in milligrams)}
#' \item{\code{atoc}}{vitamin E as alpha-tocopherol (in milligrams)}
#' \item{\code{ret}}{retinol (in micrograms)}
#' \item{\code{vara}}{Vitamin A as retinol activity equivalents (in micrograms).}
#' \item{\code{acar}}{alpha-carotene (in micrograms)}
#' \item{\code{bcar}}{beta-carotene (in micrograms)}
#' \item{\code{cryp}}{beta-cryptoxanthin (in micrograms)}
#' \item{\code{lyco}}{lycopene (in micrograms)}
#' \item{\code{lz}}{lutein and zeaxanthin (in micrograms).}
#' \item{\code{vb1}}{thiamin (vitamin B1, in milligrams)}
#' \item{\code{vb2}}{riboflavin (vitamin B2, in milligrams)}
#' \item{\code{niac}}{niacin (in milligrams)}
#' \item{\code{vb6}}{vitamin B5 (in milligrams)}
#' \item{\code{fola}}{total folate (in micrograms)}
#' \item{\code{fa}}{folic acid (in micrograms)}
#' \item{\code{ff}}{food folate (in micrograms)}
#' \item{\code{chl}}{total choline (in milligrams)}
#' \item{\code{vb12}}{vitamin B12 (in micrograms)}
#' \item{\code{vc}}{vitamin C (in milligrams)}
#' \item{\code{vd}}{vitamin D (comprising D2 and D3, in micrograms)}
#' \item{\code{vk}}{vitamin K (in micrograms)}
#' \item{\code{calc}}{calcium (in milligrams)}
#' \item{\code{phos}}{phosphorus (in milligrams)}
#' \item{\code{magn}}{magnesium (in milligrams)}
#' \item{\code{iron}}{iron (in milligrams)}
#' \item{\code{zinc}}{zinc (in milligrams)}
#' \item{\code{copp}}{copper (in milligrams)}
#' \item{\code{sodi}}{sodium (in milligrams)}
#' \item{\code{pota}}{potassium (in milligrams)}
#' \item{\code{sele}}{selenium (in micrograms)}
#'}
#' @name nutrients
#' @note These data are subject to a data user agreement, available at \url{https://www.cdc.gov/nchs/data_access/restrictions.htm}
"nutrients"
#' Deaths from pandemics
#'
#' The data base contains estimated records of the number of deaths from pandemics.
#' @format A data frame with 72 rows and 8 variables:
#' \describe{
#' \item{\code{event}}{name of the event}
#' \item{\code{startyear}}{start year of the event}
#' \item{\code{endyear}}{end year of the event}
#' \item{\code{lower}}{lower bound on estimated deaths (in thousands)}
#' \item{\code{average}}{average estimated deaths (in thousands)}
#' \item{\code{upper}}{upper bound on estimated deaths (in thousands)}
#' \item{\code{saverage}}{scaled average of estimated deaths (in thousands)}
#' \item{\code{population}}{estimated population at risk (in thousands)}
#' }
#' @name pandemics
#' @source Cirillo, P. and N.N. Taleb (2020). \emph{Tail risk of contagious diseases}. Nat. Phys. \bold{16}, 606–613 (2020). <doi:10.1038/s41567-020-0921-x>
"pandemics"
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