R/data_old_documentations.R

In thoree/stat340: Data and functions for BIAS at NMBU

#' #' rats data
#' #'
#' #' Three experimental treatments were administered to rats, and the glycogen
#' #' content of the rats’ livers was analysed as the response variable. There
#' #' were two rats per treatment, so the total sample was n = 3 × 2 = 6. The
#' #' tricky bit was that after each rat was killed, its liver was cut up into
#' #' three pieces: a left-hand bit, a central bit and a right-hand bit. So now
#' #' there are six rats each producing three bits of liver, for a total of 6 × 3 = 18
#' #' numbers. Finally, two separate preparations were made from each macerated
#' #' bit of liver, to assess the measurement error associated with the analytical
#' #' machinery. At this point there are 2 × 18 = 36 numbers in the table as a whole.
#' #'
#' #' @format A data frame with 36 rows and 4 columns.
#' #'
#' "rats"

#' #' reading data
#' #'
#' #' The reading ability of children measured at different ages. A tiny
#' #' data set to illustrate the importance of dependency in data.
#' #'
#' #' @format A data frame with 6 rows and 3 columns.
#' #'
#' "reading"

#' #' poinsettia data
#' #'
#' #' The height of poinsettias is measured weekly up to around 100 days.
#' #' The plants are grown under normal conditions (control),
#' #' treated with chemicals to grow shorter, or treated with
#' #' temperature manipulations to achieve the same as with
#' #' chemicals. Some data are missing for most plants.
#' #'
#' #' @format A data frame with 330 rows and 4 columns.
#' #'
#' "poinsettia"

#' #' rootGrowth data
#' #'
#' #' The length of roots is measured bi-weekly for 10 weeks. There
#' #' are 6 repeated measurements made for each root. 15 roots grow
#' #' under normal conditions (control) while 15 under fertilized
#' #' conditions (fertilizer).
#' #'
#' #' This is an extension of the data set fertilizer
#' #' in The R Book.
#' #'
#' #' @format A data frame with 180 rows and 4 columns.
#' #'
#' "rootGrowth"

#' #' farms data
#' #'
#' #' The size of plants have been measured on 5 different locations with various
#' #' Nitrogen content in the soil, within in each of 24 farms. This data set
#' #' illustrates spatial pseudo-replication, since the 5 measurements made on
#' #' each farm are probably dependent, coming from the same farm.
#' #'
#' #' The same data set as in The R Book, but with better column names.
#' #'
#' #' @format A data frame with 120 rows and 3 columns.
#' #'
#' "farms"

#' #' farms0 data
#' #'
#' #' This is just a subsample of the \code{\link{farms}} data set, where
#' #' there are differing number of data from each farm.
#' #'
#' #' @format A data frame with 80 rows and 3 columns.
#' #'
#' "farms0"

#' #' sediments data
#' #'
#' #' Seafloor sediments from 12 different sites along the coast have been collected and
#' #' their prokaryotic composition estimated by amplicon sequencing and subsequent
#' #' bioinformatic analysis.
#' #'
#' #' The column Environment indicates if the samples are from Natural or Polluted sediments.
#' #'
#' #' All remaining columns are centered log-transformed relative abundances of
#' #' various prokaryotic genera. A large value means a larger abundance, i.e. the most
#' #' common genera have the largest values, and vice versa.
#' #'
#' #' @format A data frame with 12 rows and 4113 columns.
#' #'
#' "sediments"

#' #' Birdcount data
#' #'
#' #' Norway has been split into 3163 regions, each of 10 km × 10 km. Within each of these regions
#' #' 11 variables have been registered over a time periode of about 8 years:
#' #' COUNTS : The number of bird species observed.
#' #' NUMBERVISITS: The number of visits by ornithologists (the observers).
#' #' MINTEMP: The minimum registered temperature in the winter months.
#' #' MAXTEMP: The maximum registered temperature in the winter months.
#' #' MEANTEMP: Average registered temperature in the winter months during the project period (about 8 years).
#' #' ALTITUDE: Average altitude (to the nearest 100 m).
#' #' LATITUDE: The latitude of the center of the region.
#' #' LONGITUDE: The longitude of the center of the region.
#' #' DISTANCE: The minimum distance to the coastline.
#' #' SEA: Whether the region contains sea (coast) or not.
#' #' SQRT.POULATION: The square root of the human population (measured in 1000 persons) in the region.
#' #'
#' #' @format A data frame with 3163 rows and 11 columns.
#' #'
#' "Birdcount"

#' #' city data
#' #' Independent measurements of chlorine
#' #' (ppm parts per million) were taken from 3 large cities:
#' #'
#' #' @format A data frame, 20 observations on 3 variables
#' #' @examples
#' #' citydata <- stack(city)
#' #' colnames(citydata) <- c("y", "city")
#' "city"


#' #' exer data
#' #' The data are from statistics.ats.ucla.edu.
#' #' The data called exer, consists of people who were randomly assigned to
#' #' two different diets: low-fat and not low-fat and three different
#' #' types of exercise: resting, walking leisurely and running.
#' #' Their pulse rate was measured at three different time
#' #' points during their assigned exercise: at 1 minute, 15 minutes, and 30 minutes.
#' #'
#' #' @format A data frame, 90 observations on 5 variables
#' #'
#' "exer"

#' #' poems.test data
#' #'
#' #' The data frame poems contains letter frequencies collected from 22 poems written by either
#' #' William Shakespeare (1564–1616), William Blake (1757–1827), and Thomas Stearns Eliot (1888–1965).
#' #'
#' #' @format A data frame, 22 observations on 31 variables
#' #'
#' "poems"

#' #' poems.test data
#' #'
#' #' The data frame poems contains letter frequencies collected from 22 poems written by either
#' #' William Shakespeare (1564–1616), William Blake (1757–1827), and Thomas Stearns Eliot (1888–1965).
#' #'
#' #' @format A data frame, 22 observations on 31 variables
#' #'
#' "poems.test"

#' #' grades data
#' #'
#' #' A data set with grades on three subjects, scored from 0 (worst)
#' #' to 20 (best)
#' #'
#' #' @format A data frame, 395 observations on 3 variables
#' #'
#' "grades"


#' #' car data
#' #'
#' #' A data set with predictors for car prices
#' #'
#' #' @format A data frame, 30 observations on 10 variables
#' #'
#' "car"

#' #' foods data
#' #'
#' #' A data set containing nutritional variables
#' #'
#' #' @format A data frame, 15 observations on 17 variables
#' #'
#' "foods"

#' #' Allele frequencies
#' #'
#' #' A dataset containing allele frequencies for 198 markers.
#' #'
#' #' @format A list of length 198. Each element is a vector of allele frequencies
#' #'   for a single marker. The first 27 markers are STRs, the remaining are SNPs.
#' #'
#' "freqsBlind"

#' #' quasibin data
#' #'
#' #' 38 observations and 4 five variables (renamed since data is not open)
#' #' @format A data frame
#' #'
#' #'    * `Success` binary 0-1 response
#' #'    * `x1` continuous response
#' #'    * `x2` continuous response
#' #'    * `x3` continuous response
#' #' @examples
#' #' # First, look at data
#' #' table(quasibin$Success)
#' #' boxplot(quasibin$x3 ~ quasibin$Success)
#' #'
#' #' # Logistic regression
#' #' logit1 <- glm(Success ~ x3, data = quasibin, family = "binomial")
#' #' summary(logit1)
#' #'
#' #' # Suprisingly, `x3` is not a significant predictor.
#' #' # Probably reason, observation 7 is an outlier as seen from
#' #' plot(logit1, which = 1)
#' #'
#' #' # We model dispersion, more reasonable results, p-value = 4.57e-05:
#' #' logit2 <- glm(Success ~ x3, data = quasibin, family = "quasibinomial")
#' #' summary(logit2)
#' "quasibin"

#' #' iris.train data
#' #'
#' #' 80 observations, 5 variables from the iris data
#' #'
#' #' @format A data frame
#' #'
#' "iris.train"

#' #' Birds data
#' #'
#' #' Birds were observed in 10 km2 squares across entire mainland Norway,
#' #' which was divided into 3163 squares in total.
#' #' Every square is considered one variable. We
#' #' consider the 25 most abundant winter bird species in Norway.
#' #'
#' #' @format A data frame with 25 rows (one for each bird species) and 3163 columns.
#' #' The value reflects the largest simultaneous count
#' #' of a given bird species in a square region .
#' #' @seealso `Birds4`
#' #'
#' "Birds"

#' #' Birds4 data
#' #'
#' #' Birds were observed in 10 km2 squares across entire mainland Norway,
#' #' which was divided into 3163 squares in total.
#' #' Every square is considered one variable. We
#' #' consider 4 winter bird species in Norway.
#' #'
#' #' @format A data frame with 4 rows (one for each bird species) and 3163 columns.
#' #' The value reflects the largest simultaneous count
#' #' of a given bird species in a square region.
#' #'
#' #' @seealso `Birds`
#' #'
#' "Birds4"

#' #' litter data
#' #'
#' #' 60 observations, 3 variables
#' #'
#' #' @format A data frame
#' #'
#' #' @examples
#' #' library(lme4)
#' #' data(litter)
#' #' mod <-lmer(Size ~ 1+ (1|Boar) + (1|Farm) + (1|Boar:Farm), data =litter)
#' #' summary(mod)
#' "litter"

#' #' smoke data, Exam Stat 340 2021-05-28, Exer 1
#' #'
#' #' Data used in Exercise 1, Exam Stat 340 2018-05-28. 20 observations of three
#' #' variables.
#' #'
#' #' @format A data frame with
#' #'  * `x` Double. Average number of cigarettes per person per year.
#' #'  * `y` Double. The number deaths from Cardiac Hearth Disease (CHD) per 100000.
#' #'  * `country` Character
#' #'
#' #' @examples
#' #' lm(y ~ x, data = smoke)
#' "smoke"

#' #' Pear2011 data, Exam Stat 340 2017-05-19, Exer 1
#' #'
#' #' Data used in Exercise 1, Exam Stat 340 2021-05-28. 24 observations of three
#' #' variables.
#' #'
#' #' @format A data frame with
#' #'  * `Sort` Character.
#' #'  * `Year` Double.
#' #'  * `REF` Double
#' #'
#' #' @examples
#' #' # Table 1b, Exam Stat 340 2017-05-19
#' #' options(contrasts = c("contr.sum","contr.poly"))
#' #' LinearModel.2 <- lm(REF ~ Sort, data = Pear2011)
#' #' summary(LinearModel.2)
#' #'
#' #' # The following hack improves the output
#' #' colnames(contrasts(Pear2011$Sort)) <- levels(Pear2011$Sort)[1:3]
#' #' LinearModel.2 <- lm(REF ~ Sort, data = Pear2011)
#' #' summary(LinearModel.2)
#' "Pear2011"

#' #' yields data in R book, Ch 19.4, called `splityield` here
#' #'
#' #' 72 observations, 5 variables
#' #'
#' #' @format A data frame
#' #' @examples
#' #' library(nlme)
#' #' data(splityield)
#' #' model <- lme(yield ~ irrigation*density*fertilizer, random = ~1|block/irrigation/density, data = splityield)
#' #' summary(model)
#' "splityield"

#' #' trackfieldrecords
#' #'
#' #' Data for men and women
#' #'
#' #' @format A list with two elements, `runMen` and `runWomen`
#' #' @examples
#' #' data(trackfieldrecords)
#' #' clust <-hclust(dist(runWomen[,-8]), method = "average")
#' #' plot(clust, hang = -1, xlab = "", sub ="", cex = 0.6, labels = runWomen[,8])
#' "trackfieldrecords"

#' #' Orthodont.unstacked data
#' #'
#' #' 27 observations, 5 variables
#' #'
#' #' @format A data frame
#' #'
#' "Orthodont.unstacked"

#' #' sireunstacked data
#' #'
#' #' 9 observations
#' #'
#' #' @format A data frame
#' #'
#' "sireunstacked"

#' #' sire data
#' #'
#' #' 9 observations
#' #'
#' #' @format A data frame
#' #'
#' "sire"

#' #' SireHerd data
#' #'
#' #' 24 observations
#' #'
#' #' @format A data frame
#' #'
#' "SireHerd"

#' #' infection data
#' #'
#' #' 181 observations and 4 variables
#' #'
#' #' @format A data frame
#' #'  @examples
#' #'  plot(infection)
#' "infection"

#' #' fertilizer data
#' #'
#' #' The length of plants, as measured by `root` is recorded for
#' #' 60 plants, each plant observed at `week` 2, 4, ..., 10.
#' #' 30 plants have fertilizer added, 30 are controls.
#' #' This data has been generated at NMBU.
#' #'
#' #' @format A data frame with variables
#' #'
#' #' * root: Continuous. Length of plant.
#' #' * week: Integer. Time of measurement.
#' #' * plant: Factor. ID for plant
#' #' * treatment. Factor with levels control or fertilizer. Originally the variable was named fertilizer and the second level added
#' #'
#' #' @source `The R book`, M.J. Crawley, Wiley, 2012.
#' #'
#' #' @examples
#' #' library(nlme)
#' #' res <- lme(fixed = root ~ treatment,
#' #'        random = ~ week | plant,
#' #'        data = fertilizer)
#' "fertilizer"

#' #' island data
#' #'
#' #' 50 observations and 3 variables
#' #'
#' #' @format A data frame
#' "island"

#' #' numbers data
#' #'
#' #' 8 observations and 3 variables giving sex ratios
#' #'
#' #' @format A data frame
#' #'
#' "numbers"

#' #' small data
#' #'
#' #' 10 observations and 2 variables
#' #'
#' #' @format A data frame
#' #'
#' "small"

#' #' smallProp data
#' #'
#' #' 3 lines
#' #'
#' #' @format A data frame
#' #'
#' "smallProp"

#' #' species data
#' #'
#' #' 90 observations and 3 variables
#' #'
#' #' @format A data frame
#' #'
#' "species"

#' #' taxa data
#' #'
#' #' 120 observations and 7 variables
#' #'
#' #' @format A data frame
#' #'
#' #' @examples
#' #' summary(taxa)
#' "taxa"


#' #' Lizard data
#' #'
#' #' 25 observations and 4 variables
#' #'
#' #' @format A data frame
#' #'
#' #' @examples
#' #' summary(Lizard)
#' "Lizard"

#' #' D data
#' #'
#' #' A 4 by 4 distance matrix
#' #'
#' #' @format A data frame
#' #'
#' #' @examples
#' #' plot(hclust(as.dist(D), method = "single"))
#' "D"

#' #' Hald's cement data
#' #'
#' #' A dataset with 13 observations and 5 variables
#' #'
#' #' @format A data frame
#' #'
#' #' @examples
#' #' summary(Hald.Cement)
#' "Hald.Cement"

#' #' TrackRecords data
#' #'
#' #' A dataset with 54 observations and 8 variables, 100m  -marathon recorrds
#' #'
#' #' @format A data frame
#' #'
#' #' @examples
#' #' summary(TrackRecords)
#' "TrackRecords"

#' #' birth data
#' #'
#' #' The dataset records of 189 birth weights
#' #' from Massachusetts, USA, and some additional variables.
#' #'
#' #' @format A data frame with 4 four continuous variables
#' #'
#' #'    * `LOW` NO or YES (low birth weight)
#' #'    * `AGE` in years
#' #'    * `LWT` mothers weight in pound before pregnancy
#' #'    * `SMK` NO or YES (smoker)
#' #'    * `BTW` birth weight in g
#' #'
#' #' @examples
#' #' lm(BWT ~ LWT*SMK, data = birth)
#' "birth"


#' #' bodydata
#' #'
#' #' A dataset with 407 observations and 5 variables
#' #'
#' #' @format A data frame with 4 four continuous variables
#' #'
#' #'    * `Weight` in kg
#' #'    * `Height` in cm
#' #'    * `Age` in years
#' #'    * `Circumference` in cm
#' #'
#' #' @examples
#' #' lm(Weight ~ Height + Age, data = bodydata)
#' "bodydata"

#' #' bears data
#' #'
#' #' A dataset with 24 observations and 12 variables
#' #'
#' #' @format A data frame with 12 four continuous variables
#' #'
#' #'
#' #' @examples
#' #' res <- lm(Weight ~ Length + I(Length^2), data = bears)
#' "bears"

#' #' yields data
#' #'
#' #' A dataset with 30 observations
#' #'
#' #' @format A data frame with one continuous variable (`yield`) and one factor (`soil`)
#' #'
#' #'
#' #' @examples
#' #' summary(yields)
#' "yields"

#' #' yields unstacked data
#' #'
#' #' A dataset with 10 lines
#' #' @format A data frame with values for `yield` for three factor values
#' #'
#' #' @examples
#' #' summary(yields.unstacked)
#' "yields.unstacked"


#' #' weights data
#' #'
#' #' A dataset with 48 observations
#' #' @format A data frame with two factors and a continuous variable
#' #'
#' #' @examples
#' #' summary(weights)
#' "weights"

#' #' crabs data
#' #'
#' #' A dataset with 173 observations
#' #' @format There are 173 female crabs for which we wish to model
#' #' the presence or absence of male satellites dependent upon characteristics
#' #' of the female horseshoe crabs. Here, 𝑦=1if satellite is present and 0
#' #' otherwise. Explanatory variables are: weight, width of shell, color
#' #' (medium light, medium, medium dark, dark), and condition of spine.
#' #'
#' #' @examples
#' #' summary(crabs)
#' "crabs"

#' #' iris.test data
#' #'
#' #' A dataset with 60 observations
#' #' @format A data frame for testing the classification model from the iris.train dataset
#' #'
#' #' @examples
#' #' summary(iris.test)
#' "iris.test"

#' #' NSRdata data
#' #'
#' #' Data from the NSR education test
#' #' @format The Norwegian Centre for Science Recruitment (NSR) has an online “education test”
#' #' where youths may answer a questionnaire to check their so-called cognitive types, their
#' #' science interest, their preferred learning methods and their interest to various science
#' #' subjects. The test suggests different ares within the STEM (Science, Technology, Engineering
#' #' and Mathematics) within which the youth may find suitable work. We have an excerpt of these
#' #' data. The data.frame NSRdata contains two variables, Science and Age. Science is an average
#' #' liking score (scale 1-6) to various STEM-subjects, and Age is a factor indicating different
#' #' age-groups:
#' #' \describe{
#' #'   \item{a)1: 1-12 yrs}
#' #'   \item{b)13: 13-15 yrs}
#' #'   \item{c)16: 16-19 yrs}
#' #'   \item{d)19: 19-29 yrs}
#' #'   \item{e)30: 30 + yrs}
#' #'  }
#' #'
#' #' @examples
#' #' summary(NSRdata)
#' "NSRdata"

#' #' barley data
#' #'
#' #' A dataset with 32 observations
#' #' @format In barley.rdata are results from an experiment where the response
#' #' is yield of barley pr 1000 square meter, and the factors sorts of barley,
#' #' soil types, types of fertilizers. In addition,the experiment was done in
#' #' two different geographical areas (sites).
#' #'
#' #' @examples
#' #' summary(barley)
#' "barley"

#' #' colon data
#' #'
#' #' Data from a colon cancer study (Alon et al. (1999) Proc. Natl. Acad. Sci. U.S.A. 96 (12): 6745–6750).
#' #' @format The data set is a 62 × 2001 matrix.
#' #' Each column corresponds to the expression level of a certain gene.
#' #' The the rows correspond to different tissues.
#' #' The higher the value, the more active the gene is in a given tissue.
#' #' The first column Tissue codes the disease status.
#' #'
#' #' @examples
#' #' summary(colon)
#' "colon"

#' #' state.x77 data
#' #'
#' #' Matrix with 50 rows and 8 columns giving the following statistics in the respective columns.
#' #' @format
#' #' \describe{
#' #'   \item{Population:}{population estimate as of July 1, 1975}
#' #'   \item{Income:}{per capita income (1974)}
#' #'   \item{Illiteracy:}{illiteracy (1970, percent of population)}
#' #'   \item{Life Exp:}{life expectancy in years (1969–71)}
#' #'   \item{Murder:}{murder and non-negligent manslaughter rate per 100,000 population (1976)}
#' #'   \item{HS Grad:}{percent high-school graduates (1970)}
#' #'   \item{Frost:s}{mean number of days with minimum temperature below freezing (1931–1960) in capital or large city}
#' #'   \item{Area:}{land area in square miles}
#' #'   }
#' #'
#' #' @examples
#' #' summary(state.x77)
#' "state.x77"

#' #' Audi data
#' #'
#' #' Data for Group-Exercises-Regression I
#' #' @format Sales prices and technical data on 30 cars of type Audi A4.
#' #' The data were collected in February 2017. The variables are: Price, Km, Hk,
#' #' Transition, Volume, Fuel, CO2, Weight, year, Age where Price is in 1000 NOK,Km
#' #' is the distance driven (in 1000 km),Hk the horse power,Transition either manual
#' #' (M) or automatic (A) transmission, etc.
#' #'
#' #' @examples
#' #' summary(Audi)
#' "Audi"

#' #' stat210_5Jan2018 data
#' #'
#' #' Data for Group-Exercises-ANOVA from the Exam STAT210 (January 5th, 2018)
#' #' @format A data set contains observations of different fertilizer mixtures
#' #' coded by the factor variable mixture. The response variable Y called yield
#' #' was measured and the year was recorded.
#' #'
#' #' @examples
#' #' summary(stat210_5Jan2018)
#' "stat210_5Jan2018"

#' #' stat340_raq_quiz data
#' #'
#' #' Data for Group-Exercises-Multivariate II
#' #' @format During the STAT340 course given in 2019 and 2020, the students were
#' #' asked to fill out the ‘R Anxiety Questionnaire’ and a STAT340 quiz. The ‘R
#' #' Anxiety Questionnaire’ consists of 24 claims, and the students gave a score
#' #' of 1-5 to each claim (a score of 1 implied that they strongly disagreed, 5
#' #' implied that they strongly agreed). The STAT340 quiz consists of 20 questions,
#' #' and a total of 20 points could be scored.
#' #'
#' #' @examples
#' #' summary(stat340_raq_quiz)
#' "stat340_raq_quiz"

#' #' Cakes.miss data
#' #'
#' #' Data for Exercise 4 - "Finding the best chocolate cake recipe" in Group-Exercises-RegressionII
#' #' @format there are 5 cakes left to bake (response Y set to NA in observation
#' #' nr. 9, 11, 13, 15, and 20), and you are supposed to complete the data set
#' #' using the virtual chocolate cake factory.
#' #' @examples
#' #' summary(Cakes.miss)
#' "Cakes.miss"