R/documentation.R

In geostats: An Introduction to Statistics for Geoscientists

#' properties of 20 river catchments
#'
#' six different (three discrete, three continuous) measurements for
#' twenty fictitious river catchments, containing their dominant
#' lithology (categorical data), stratigraphic age (ordinal data),
#' number of springs (count data), the pH of the river water
#' (Cartesian quantity), its Ca/Mg ratio (Jeffreys quantity) and the
#' percentage covered by vegetation (proportion).
#' 
#' @name catchments
#' @docType data
#' @keywords data
#' @examples
#' data(catchments,package='geostats')
#' hist(catchments$pH)
NULL

#' detrital zircon U-Pb data
#'
#' Detrital zircon U-Pb data of 13 sand samples from China.
#' 
#' @name DZ
#' @docType data
#' @keywords data
#' @references Vermeesch, P. ``Multi-sample comparison of detrital age
#'     distributions.''  Chemical Geology 341 (2013): 140-146.
#' @examples
#' data(DZ,package='geostats')
#' qqplot(DZ[['Y']],DZ[['5']])
NULL

#' Rb-Sr data
#'
#' Synthetic dataset of 8 Rb-Sr analysis that form a 1Ga isochron.
#' 
#' @name rbsr
#' @docType data
#' @keywords data
#' @examples
#' data(rbsr,package='geostats')
#' plot(rbsr[,'RbSr'],rbsr[,'SrSr'])
#' fit <- lm(SrSr ~ RbSr,data=rbsr)
#' abline(fit)
NULL

#' declustered earthquake data
#'
#' Dataset of 28267 earthquakes between 1769 and 2016, with
#' aftershocks and precursor events removed.
#' 
#' @name declustered
#' @docType data
#' @keywords data
#' @references Mueller, C.S., 2019. Earthquake catalogs for the USGS
#'     national seismic hazard maps. Seismological Research Letters,
#'     90(1), pp.251-261.
#' @examples
#' data(declustered,package='geostats')
#' quakesperyear <- countQuakes(declustered,minmag=5.0,from=1917,to=2016)
#' table(quakesperyear)
NULL

#' earthquake data
#'
#' Dataset of 20000 earthquakes between 2017 and 2000, downloaded from
#' the USGS earthquake database
#' (\url{https://earthquake.usgs.gov/earthquakes/search/}).
#' 
#' @name earthquakes
#' @docType data
#' @keywords data
#' @examples
#' data(earthquakes,package='geostats')
#' gutenberg(earthquakes$mag)
NULL

#' Finnish lake data
#'
#' Table of 2327 Finnish lakes, extracted from a hydroLAKES database.
#' 
#' @name Finland
#' @docType data
#' @keywords data
#' @references Lehner, B., and Doll, P. (2004), Development and
#'     validation of a global database of lakes, reservoirs and
#'     wetlands, Journal of Hydrology, 296(1), 1-22, doi:
#'     10.1016/j.jhydrol.2004.03.028.
#' @examples
#' data(Finland,package='geostats')
#' sf <- sizefrequency(Finland$area)
#' size <- sf[,'size']
#' freq <- sf[,'frequency']
#' plot(size,freq,log='xy')
#' fit <- lm(log(freq) ~ log(size))
#' lines(size,exp(predict(fit)))
NULL

#' foram count data
#'
#' Planktic foraminifera counts in surface sediments in the Atlantic ocean.
#' 
#' @name forams
#' @docType data
#' @keywords data
#' @examples
#' data(forams,package='geostats')
#' abundant <- forams[,c('quinqueloba','pachyderma','incompta',
#'                       'glutinata','bulloides')]
#' other <- rowSums(forams[,c('uvula','scitula')])
#' dat <- cbind(abundant,other)
#' chisq.test(dat)
NULL

#' world population
#'
#' The world population from 1750 until 2014.
#' 
#' @name worldpop
#' @docType data
#' @keywords data
#' @examples
#' data(worldpop,package='geostats')
#' plot(worldpop)
NULL

#' British coast
#'
#' A \eqn{512 \times 512} pixel image of the British coastline.
#' 
#' @name Britain
#' @docType data
#' @keywords data
#' @examples
#' data(Britain,package='geostats')
#' p <- par(mfrow=c(1,2))
#' image(Britain)
#' fractaldim(Britain)
#' par(p)
NULL

#' rivers on Corsica
#'
#' A \eqn{512 \times 512} pixel image of the river network on Corsica.
#' 
#' @name Corsica
#' @docType data
#' @keywords data
#' @examples
#' data(Corsica,package='geostats')
#' p <- par(mfrow=c(1,2))
#' image(Corsica)
#' fractaldim(Corsica)
#' par(p)
NULL

#' fractures
#'
#' A \eqn{512 \times 512} pixel image of a fracture network.
#' 
#' @name fractures
#' @docType data
#' @keywords data
#' @examples
#' data(fractures,package='geostats')
#' p <- par(mfrow=c(1,2))
#' image(fractures)
#' fractaldim(fractures)
#' par(p)
NULL

#' A-CN-K compositions
#'
#' Synthetic A (Al\eqn{_2}O\eqn{_3}) -- CN (CaO+Na\eqn{_2}O) -- K
#' (K\eqn{_2}O) data table.
#' 
#' @name ACNK
#' @docType data
#' @keywords data
#' @examples
#' data(ACNK,package='geostats')
#' ternary(ACNK,type='p',labels=c(expression('Al'[2]*'O'[3]),
#'                                expression('CaO+Na'[2]*'O'),
#'                                expression('K'[2]*'O')))
NULL

#' composition of Namib dune sand
#'
#' Major element compositions of 16 Namib sand samples.
#' 
#' @name major
#' @docType data
#' @keywords data
#' @references Vermeesch, P. & Garzanti, E. ``Making geological sense
#'     of `Big Data' in sedimentary provenance analysis.'' Chemical
#'     Geology 409 (2015): 20-27.
#' @examples
#' data(major,package='geostats')
#' comp <- clr(major)
#' pc <- prcomp(comp)
#' biplot(pc)
NULL

#' composition of 646 oceanic basalts
#'
#' Major element compositions of 227 island arc basalts (IAB), 221 mid
#' oceanic ridge basalts (MORB) and 198 ocean island basalts
#' (OIB). This dataset can be used to train supervised learning
#' algorithms.
#' 
#' @name training
#' @docType data
#' @keywords data
#' @references Vermeesch, P. ``Tectonic discrimination diagrams
#'     revisited.''  Geochemistry, Geophysics, Geosystems 7.6 (2006).
#' @examples
#' library(MASS)
#' data(training,package='geostats')
#' ld <- lda(x=alr(training[,-1]),grouping=training[,1])
#' pr <- predict(ld)
#' table(training$affinity,pr$class)
NULL

#' composition of a further 147 oceanic basalts
#'
#' Major element compositions of 64 island arc basalts (IAB), 23 mid
#' oceanic ridge basalts (MORB) and 60 ocean island basalts
#' (OIB). This dataset can be used to test supervised learning
#' algorithms.
#' 
#' @name test
#' @docType data
#' @keywords data
#' @references Vermeesch, P. ``Tectonic discrimination diagrams
#'     revisited.''  Geochemistry, Geophysics, Geosystems 7.6 (2006).
#' @examples
#' library(MASS)
#' data(training,package='geostats')
#' ld <- lda(x=alr(training[,-1]),grouping=training[,1])
#' data(test,package='geostats')
#' pr <- predict(ld,newdata=alr(test[,-1]))
#' table(test$affinity,pr$class)
NULL

#' A-F-M data
#'
#' FeO - (Na\eqn{_2}O + K\eqn{_2}O) - MgO compositions of 630
#' calc-alkali basalts from the Cascade Mountains and 474 tholeiitic
#' basalts from Iceland. Arranged in F-A-M order instead of A-F-M for
#' consistency with the \code{ternary} function.
#' 
#' @name FAM
#' @docType data
#' @keywords data
#' @examples
#' data(FAM,package='geostats')
#' ternary(FAM[,-1])
NULL

#' directions of glacial striations
#'
#' Directions (in degrees) of 30 glacial striation measurements from
#' Madagascar.
#' 
#' @name striations
#' @docType data
#' @keywords data
#' @examples
#' data(striations,package='geostats')
#' circle.plot(striations,degrees=TRUE)
NULL

#' pebble orientations
#'
#' Orientations (in degrees) of 20 pebbles.
#' 
#' @name pebbles
#' @docType data
#' @keywords data
#' @examples
#' data(pebbles,package='geostats')
#' circle.plot(pebbles,degrees=TRUE)
#' m <- meanangle(pebbles,option=0,orientation=TRUE)
#' circle.points(m,degrees=TRUE,pch=22,bg='white')
NULL

#' palaeomagnetic data
#'
#' Ten paired magnetic declination (azimuth) and inclination (dip)
#' measurements, drawn from a von Mises - Fisher distribution with
#' mean vector \eqn{\mu=\{2,2,1\}/3} and concentration parameter
#' \eqn{\kappa=200}.
#' 
#' @name palaeomag
#' @docType data
#' @keywords data
#' @examples
#' data(palaeomag,package='geostats')
#' stereonet(trd=palaeomag$decl,plg=palaeomag$incl,degrees=TRUE,show.grid=FALSE)
NULL

#' fault orientation data
#'
#' Ten paired strike and dip measurements (in degrees), drawn from a
#' von Mises - Fisher distribution with mean vector
#' \eqn{\mu=\{-1,-1,1\}/\sqrt{3}} and concentration parameter
#' \eqn{\kappa=100}.
#' 
#' @name fault
#' @docType data
#' @keywords data
#' @examples
#' data(fault,package='geostats')
#' stereonet(trd=fault$strike,plg=fault$dip,option=2,degrees=TRUE,show.grid=FALSE)
NULL

#' Meuse river data set
#'
#' This data set gives locations and topsoil heavy metal
#' concentrations, collected in a flood plain of the river Meuse, near
#' the village of Stein (NL). Heavy metal concentrations are from
#' composite samples of an area of approximately 15 m x 15 m. This
#' version of the \code{meuse} dataset is a trimmed down version of
#' the eponymous dataset from the \code{sp} dataset.
#' 
#' @name meuse
#' @docType data
#' @keywords data
#' @examples
#' data(meuse,package='geostats')
#' semivariogram(x=meuse$x,y=meuse$y,z=log(meuse$cadmium))
NULL

#' hills
#'
#' 150 X-Y-Z values for a synthetic landscape that consists of three
#' Gaussian mountains.
#' 
#' @name hills
#' @docType data
#' @keywords data
#' @examples
#' data(hills,package='geostats')
#' semivariogram(x=hills$X,y=hills$Y,z=hills$Z,model='gaussian')
NULL