1984 salaries in various professional fields

Description

These data were published in the American Association of University Professsor's journal, Academe. There were compiled by Marcia Bellas drawing on data from the 1984 Carnegie survey of faculty, the US National Science Foundation, the National Research Council, and the US Census Bureau. The motivation for the work was to investigate salary "disparities among faculty whose education and experience are comparable and whose duties are broadly similar," in particular those due to sex. Regretably, the data do not include measures of the production of workers in the various fields or the numbers of people employed in each field.

Usage

data(AAUP)

Format

28 rows, each of which is a professional discipline:

• subject name of the discipline

• ac: average salary (USD) for academics

• nonacsal median salary (USD) for non-academics

• fem: fraction of the workforce that is female

• unemp: unemployment rate in the discipline

• nonac: fraction of the workforce that is non-academic,

• licensed: Does work in the profession require a license (from George Cobb's paper)

Source

George Cobb (2011) "Teaching statistics: some important tensions" Chilean Journal of Statistics 2(1):31-62 link

References

M Bellas & BF Reskin (1994) "On comparable worth" Academe 80:83-85

Anthropometric data from college-aged women

Description

Percentage of body fat, age, weight, height, body mass index and fourteen circumference measurements are given for 184 college women ages 18-25. Body fat was accurately determined by an underwater weighing technique which requires special equipment and training of the individuals conducting the process. Circumference measurements were made to the nearest 0.1 cm using a cloth tape in complete contact with the skin but without compression of the soft tissues. The measurement process, described somewhat incompletely below, is described in greater detail in Slack (1997) who used the standards recommended by Lohman, Roche and Martrell (1988).

Usage

data(Anthro_F)

Format

A data.frame with one row for each of 184 woman

• Weight (kg)

• Height (m)

• BMI: (Body Mass Index) Weight divided by the square of Height

• Age

• Neck: Minimal circumference perpendicular to the long axis of the neck (cm)

• Chest: Horizontal plane measurement at the sixth rib, at the end of a normal expiration (cm)

• Calf: Horizontal maximal calf measurement (cm)

• Biceps: Measurement with arm extended (cm)

• Hips: Horizontal maximal measurement around buttocks (cm)

• Waist: Horizontal minimal measurement, at the end of a normal expiration (cm)

• Forearm: Maximal measurement perpendicular to long axis (cm)

• PThigh: (Proximal Thigh) Horizontal measurement immediately distal to the gluteal furrow (cm)

• MThigh: (Middle Thigh) Measurement midway between the midpoint of the inguinal crease and the proximal border of the patella (cm)

• DThigh: (Distal Thigh) Measurement proximal to the femoral epicondyles (cm)

• Wrist: Measurement perpendicular to the long axis of the forearm (cm)

• Knee: Measurement at the mid-patellar level, with the knee slightly flexed (cm)

• Elbow: A minimal circumference measurement with the elbow extended (cm)

• Ankle: Minimal circumference measurement perpendicular to the long axis of the calf (cm)

• BFat: Amount of body fat expressed as a percentage of total body weight, using Siri's (1956) method

Source

Roger W. Johnson (2021) "Fitting Percentage of Body Fat to Simple Body Measurements: College Women" Journal of Statistics and Data Science Education 29(3) doi:10.1080/26939169.2021.1971585

Birdkeeping and Lung Cancer

Description

A 1972–1981 health survey in The Hague, Netherlands, discovered an association between keeping pet birds and increased risk of lung cancer. To investigate birdkeeping as a risk factor, researchers conducted a case–control study of patients in 1985 at four hospitals in The Hague (population 450,000). They identified 49 cases of lung cancer among the patients who were registered with a general practice, who were age 65 or younger and who had resided in the city since 1965. They also selected 98 controls from a population of residents having the same general age structure.

Usage

data(Birdkeepers)

Format

A data frame with 147 observations on the following 7 variables.

• LC Whether subject has lung cancer

• FM Sex of subject

• SS Socioeconomic status, determined by occupation of the household's principal wage earner

• BK Indicator for birdkeeping (caged birds in the home for more that 6 consecutive months from 5 to 14 years before diagnosis (cases) or examination (control))

• AG Age of subject (in years)

• YR Years of smoking prior to diagnosis or examination

• CD Average rate of smoking (in cigarettes per day)

Details

This dataset is copied and renamed from the Sleuth3 R package, where it is called case2002.

Source

Ramsey, F.L. and Schafer, D.W. (2013). The Statistical Sleuth: A Course in Methods of Data Analysis (3rd ed), Cengage Learning.

References

Holst, P.A., Kromhout, D. and Brand, R. (1988). "For Debate: Pet Birds as an Independent Risk Factor for Lung Cancer" British Medical Journal 297: 13–21.

Records on births in the US in 2022

Description

These data come from the Centers for Disease Controls "public use file" recording all 3,699,040 (known) births in the US in 2022. Births2022 is a random sample of size 20,000 from the comprehensive file

Usage

Births2022

Format

A data frame with 20,000 observations on the following 38 variables. The unit of observation is a birth.

• month: 1-12

• dow: Day of week: Sun, Mon, Tues, ...

• place: hospital, home, clinic, etc.

• paternity: is paternity acknowledged. Y, N, and X. X stands for "not applicable" which is shorthand for the mother is married (consequently the husband is presumed to be the father).

• meduc: mother's educational level. <8 is 8th grade or less, HSG+ means high school plus some college but no degree.

• feduc: father's education. Same coding as meduc.

• married: Is the mother married?

• mage: mother's age

• fage: father's age

• total_kids: how many total births to mother including this one.

• interval: months since last birth (if applicable).

• prenatal_start: Which trimester did the mother start prenatal care?

• prenatal_visits: How many total prenatal care visits.

• mheight: Mother's height in inches

• wt_pre: Mother's weight in pounds before pregnancy

• wt_delivery: Mother's weight in pounds at delivery

• diabetes_pre: Did the mother have diabetes before pregnancy

• diabetes_gest: Did the mother develop gestational diabetes

• hbp_pre: Did the mother have high blood pressure before pregancy

• hbp_gest: Did the mother develop high blood pressure during pregnancy

• eclampsia: Did the mother develop eclampsia

• induction: Was labor induced?

• augmentation: Was the uterus stimulated to increase frequency, duration, and intensity of contractions.

• anesthesia: Was the mother given anesthesia?

• presentation: Baby's presentation at birth (e.g. cephalic or breech)

• method: method of delivery (vaginal or C-section)

• trial_at_labor: For mother's who delivered by C-section, was there an attempt at labor.

• attendant: MD, DO, midwife, other

• payment: How was the bill paid?

• apgar5, apgar10: APGAR scores (0-10) at five and ten minutes after birth.

• plurality: singletons, twins, triplets, quadruplets (as an integer 1-4)

• sex: Baby's sex

• duration: Duration of gestation, in weeks by "obstretric estimate."

• menses: Last normal menses month: 1-12 (Jan-Dec)

• weight: Baby's weight (in grams)

• living: Baby living at time of birth report

• breastfed: Baby breastfed at time of discharge

Source

US Centers for Disease Control "Natality Public Use File", CDC vital stats online

References

"User Guide to the 2022 Natality Public Use File"

Winning times in the Boston Marathon

Description

The Boston marathon is the oldest continuing marathon in the US.

Usage

Boston_marathon

Format

A data frame

• year

• name: the winner's name

• country from which the winner registered

• time: the winning time

• sex: female or male

• minutes: the winning time converted to minutes

Source

Boston Athletic Association

World records in the 100 & 200 m butterfly swim

Description

World records in the 100 & 200 m butterfly swim

Usage

data(Butterfly)

Format

A data.frame object with one row for each world record and variables

• time: the record time in seconds

• swimmer the name of the swimmer

• date a Date object containing the date the record was made

• place string descripting the location

• sex: coded as F and M

• lengths: the total distance was divided into lengths of either 25 or 50 meters. lengths gives the number of such lengths in the total distance.

• dist: the total distance (in meters) of the race

Source

Wikipedia

Smoking and lung function among youths

Description

Data from the Childhood Respiratory Disease Study collected in the late 1970s to examine the effects of smoking and exposure to second-hand smoke. on pulmonary functions in youths.

Usage

data(CRDS)

Format

A data.frame with one row for each of 645 youngsters.

• age in years

• FEV (forced expiratory lung volume) in liters

• height in inches

• sex

• smoker whether or not the youngster smokes

Source

Cummiskey, et al. (2020) "Causal Inference in Introductory Statistics Courses" Journal of Statistics Education 28(1) doi:10.1080/10691898.2020.1713936

Annual precipitation in California locations

Description

These data are from an article in the journal Geography that illustrates precipitation modeling.

Usage

Calif_precip

Format

A data.frame with 30 rows, each a weather station in California

• station: the name of the station

• precip average annual precipitation in inches

• altitude in feet

• latitude the station's north-south location (degrees North)

• distance: distance in miles from the coast

• orientation: related to the rain shadow effect of the mountains. "W" means westward facing (toward the prevailing winds). "L" mean "leeward," that is, facing away from the prevailing winds.

Source

P. J. Taylor (1980) "A Pedagogic Application of Multiple Regression Analysis: Precipitation in California" Geography 65 (3) 203-212

Resume Experiment Data

Description

Data from an experimental study in which researchers sent the resumes of fictitious job applicants to potential employers. The first names of the fictitious applicants was set randomly to sound either Black or white.

Usage

data("Callback")

Names_and_race

Format

: Callback: A data frame with 4870 rows and 4 variables. Each row is one fictitious applicant

• name: first name of the fictitious job applicant

• sex: sex of applicant (female or male)

• callback: whether the potential employer called back to follow up. (1 = yes, 0 = no) Another data frame, Names_and_race: which first names are associated with which race.

An object of class grouped_df (inherits from tbl_df, tbl, data.frame) with 36 rows and 2 columns.

References

• Imai, Kosuke. 2017. Quantitative Social Science: An Introduction. Princeton University Press. URL from whence these data were added to this package. In QSS, the data are called resume.

• Marianne Bertrand and Sendhil Mullainathan (2004) “Are Emily and Greg more employable than Lakisha and Jamal? A field experiment on labor market discrimination.” American Economic Review, vol. 94, no. 4, pp. 991–1013. doi: 10.3386/w9873

Grades at a small college

Description

These are the actual grades for 400+ individual students in the courses they took at a small, liberal-arts college in the midwest US. All the students graduated in 2006. Each row corresponds to a single student in a single course. The data have been de-identified by translating the student ID, the instructor ID, and the name of the department. Typically a graduating student has taken about 32 courses. As another form of de-identification, only half of the courses each student, selected randomly, are included. Only courses with 10 or more students enrolled were included.

Usage

data(College_grades)

Format

A data frame with 6146 Grades for 443 students.

-grade The letter grade for the student in this course: A is the highest.
- sessionID An identifier for the course taken. Courses
offered multiple times in one semester or across semesters have individual IDs.
-sid The student ID
-dept The department in which the course was offered. 100 is entry-level,
200 sophomore-level, 300 junior-level, 400 senior-level.
-enroll Student enrollment in the course. This includes students who are not
part of this sample.
-iid Instructor ID
-gradepoint A translation of the letter grade into a numerical scale. 4 is high.
Some letter grades are not counted in a student's gradepoint average. These have \code{NA} for
the gradepoint.

Source

The data were helpfully provided by the registrar of the college with the proviso that the de-identification steps outlined above be performed.

SIMULATED data from an economic outlook poll

Description

SIMULATED data from an economic outlook poll

Usage

Econ_outlook_poll

Format

10,000 rows with three variables: age, income, pessimism

Data from a simple SIMULATION of people's pessimism about the economic state based on age group and income group. Nothing about the real world should be inferred from these data; they are merely to illustrate adjusting for covariates.

Source

The simulation is from "Statistical Modeling: A Fresh Approach" (2/e). Code for it is in the file system.file("SM2-simulations.R", package="LSTbook")

Annual summaries concerning motor-vehicle related fatalities in the US#'

Description

Annual summaries concerning motor-vehicle related fatalities in the US#'

Usage

data(FARS)

Format

A data.frame object with one row per year from 1994 to 2016

• year: The year covered by the summary

• crashes the number of incidents in that year

• drivers the number of drivers killed in those incidents

• passengers the number of passengers killed in those incidents

• unknown vehicle occupants killed whose status as driver or passenger is unknown

• motorcyclists the number of motorcyclists killed in those incidents

• pedestrians the number of pedestrians killed in those incidentss

• pedalcyclists the number of non-motorized cyclist deaths

• other_nonvehicle the number of other deaths in those incidents

• vehicle_miles the number of miles driven by all vehicles, whether they were involved in an incident or not. (billions of miles)

• population the population of the US (thousands of people)

• registered_vehicles the number of motor vehicles registered in the US (thousands)

• licensed_drivers the number of licenced drivers in the US (thousands)

Source

From the Fatality Analysis Reporting System of the US Department of Transportation (DOT). The data have been put into a tidy form from the untidy version published by the DOT, removing columns calculated from other columns and so on.

Data from the Framingham heart study

Description

When it launched in 1948 the original goal of The Framingham Heart Study (FHS) launched in 1948 with the goal of identifying risk factors for cardiovascular disease. FHS had over 14,000 people from three generations, including the original participants, their children, and their grandchildren. These data represent 4238 Framingham subjects and were published by Kaggle for a machine-learning competition. The goal of the competition was to predict TenYearCHD from the other factors.

Usage

data(Framingham)

Format

4238 rows, each of which is a FHS subject. There are 16 variables:

• sex

• age of the patient

• education highest level achieved: some HS, HS grad/GED, some college/vocational school, college graduate

• currentSmoker: whether or not the patient is a current smoker

• cigsPerDay: the number of cigarettes that the person smoked on average in one day.

• BPMeds: whether or not the patient was on blood pressure medication

• prevalentStroke: whether or not the patient had previously had a stroke

• prevalentHyp: whether or not the patient was hypertensive

• diabetes: whether or not the patient had diabetes

• totChol: total cholesterol level

• sysBP: systolic blood pressure

• diaBP: diastolic blood pressure

• BMI: Body Mass Index

• heartRate: heart rate

• glucose: glucose level

• TenYearCHD: Did the patient develop congestive heart disease during a 10 year follow-up? (1=Yes)

Source

Kaggle and Github repository

References

Description of FHS by the National Heart, Lung, and Blood Institute

Voting patterns in the 1933 German national election

Description

1933 was the year that Hitler and the Nazi party came to power. The initial basis for this was a national election in which the Nazis secured a substantial fraction of the vote. (Immediately after the election, the Nazis burned the Reichtag (the German parliament) and started repressing their political opposition though a campaign of imprisonment and murder.)

Usage

data("Germany1933vote")

Format

A data frame with 681 rows and 7 variables. Each row is a German precinct.

• self: share of potential voters who are self-employed

• blue: share of potential voters who are blue-collar workers

• white: share of potential voters who are white-collar workers

• domestic: share of potential voters who are employed domestically

• unemployed: share of potential voters who are un-employed

• nvoter: number of eligible voters (not clear if this include people who didn't vote)

• nazivote: number of votes for the Nazis

References

• Imai, Kosuke. 2017. Quantitative Social Science: An Introduction. Princeton University Press. URL from whence these data were added to this package. In QSS, the data are called nazis.

• G. King, O. Rosen, M. Tanner, A.F. Wagner (2008) “Ordinary economic voting behavior in the extraordinary election of Adolf Hitler.” Journal of Economic History, vol. 68, pp. 951–996.#'

Data from the trial of serial killer Kristen Gilbert

Description

Intensive care unit nurse Kristen Gilbert worked for several years in the 1990s at a Veterans Administration Hospital. Her co-workers became suspicious. The co-workers observed that unexpected patient deaths occurred more frequently on her shifts than on other shifts. They also noticed a shortage of supplies of the cardiac stimulant epinephrine, which can be fatal when administered in large enough doses through an IV drip. The hospital investigators went through all the shifts during the years Gilbert worked at the hospital, noting whether Gilbert was on duty during that shift and whether there was a death during the shift.

Usage

data(Gilbert)

Format

A data frame with one row for each shift at the VA hospital.

• death Whether a patient death occurred during the shift.

• gilbert Whether nurse Kristen Gilbert was on duty during the shift.

• time: the winning time in seconds

• race the name of the race. Many races are repeated over successive years.

• year the year the race was run

• name the name of the winning runner

• sex: the runner's sex, coded as F and M

• distance: the total distance of the race in km

• climb: the total vertical climb of the race in meters

Details

Only tabular summaries of the shift/death information are public. This data frame was reconstructed from those summaries.

Get out the vote experiment

Description

An experiment about ways to encourage voting in primary elections. During the 2006 primary election in Michigan, registered voters were randomly assigned to different treatments, each in the form of a postcard mailed to them before the primary. The most high-pressure message("Neighbors") listed the voters neighbors and whether they voted in the previous primary elections. The card promised to send out the same information after the 2006 primary, so that "you and your neighbors will all know who voted and who did not." (From the Gerber et al. reference, below.) In another treatment, "Civic Duty," the postcard said, "On August 8, remember your rights and responsibilities as a citizen. Remember to vote. DO YOUR CIVIC DUTY—VOTE!" Yet another treatment, "Hawthorne" simply told the voter that "YOU ARE BEING STUDIED!" as part of research on why people do or do not vote. There was also a control group that did not receive a postcard.

Usage

data(Go_vote)

Format

A data frame with 305866 rows and 6 variables:

• sex of the voter (female or male)

• yearofbirth: year of birth of the voter

• primary2004: whether the voter voted in the 2004 primary election (voted, abstained)

• messages: Get-out-the-vote message the voter received (Civic Duty, Control, Neighbors, Hawthorne)

• primary2006: whether the voter turned out in the 2006 primary election (voted, abstained)

• hhsize: household size of the voter

References

• Imai, Kosuke. 2017. Quantitative Social Science: An Introduction. Princeton University Press. URL.

• Alan S. Gerber, Donald P. Green, and Christopher W. Larimer (2008) “Social pressure and voter turnout: Evidence from a large-scale field experiment.” American Political Science Review, vol. 102, no. 1, pp. 33–48. doi: 10.1017/S000305540808009X

Winning times in Scottish Hill races, 2005-2017

Description

Winning times in Scottish Hill races, 2005-2017

Usage

data(Hill_racing)

Format

A data.frame object with one row for each race winner. Most races have both a male and female winner.

"year" "sex" "name" "time" "race" "distance" "climb"

• time: the winning time in seconds

• race the name of the race. Many races are repeated over successive years.

• year the year the race was run

• name the name of the winning runner

• sex: the runner's sex, coded as F and M

• distance: the total distance of the race in km

• climb: the total vertical climb of the race in meters

Source

The data were scraped from the Scottish Hill Racing site.

Fuel economy measurements on US car models

Description

Fuel economy measurements on US car models

Usage

data(MPG)

Format

A data.frame object with one row year for each model or configuration of automobile or light truck sold in the US.

• manufacturer: name of company making the vehicle

• division: name of the company division making the vehicle

• model: vehicle model name

• fuel_year: fuel consumed in 10,000 miles (roughly 1 year.)

• CO2_year: Carbon dioxide produced per year, in kilograms. 10,000 miles of driving is taken to represent a year. Note, Carbon-per-year (without the oxygen) is roughly one-quarter the mass of CO2-per-year.

• hybrid: whether the car is a hybrid

• class: the type of vehicle, e.g. midsize, compact, large, SUV

• vol_passenger: volume for passengers (cubic feet)

• vol_luggage: volume for luggage (cubic feet)

• doors: number of passenger doors

• mpg_city: Estimated fuel consumption in city driving (miles per gallon)

• mpg_hwy: like mpg_city but for highway driving

• mpg_comb: like mpg_city but for a standard combination of city and highway driving

• EPA_fuel_cost: Annual fuel cost using a standard price for gas and a standard miles per year of driving.

• valves_exhaust: how many exhaust valves per cylinder

• valves_intake: how many air intake valves per cylinder

• CO2city: estimate of carbon-dioxide (grams/mile) production per mile in city driving.

• CO2hwy: like CO2city but for highway driving

• CO2combined: like CO2city but for a standard mixture of city and highway driving

• hatchback: is there a hatchback rear door

• start_stop: does the vehicle have a system to stop the engine when idling

• cyl_deact: are cylinders in the engine deactivated when power demand warrants

• fuel: the kind of fuel used.

  • G = regular unleaded gasoline,

  • GM = mid-grade recommended,

  • GP = premium unleaded recommended,

  • GPR = premium unleaded required,

  • DU = diesel (ultra low sulfur)

• drive: type of drive, e.g. front-wheel, 4-wheel, ...

• regen: wheels with regenerative breaking (for hybrids)

• n_gears: number of transmission gears

• n_cyl: number of engine cylinders

• displacement: engine displacement (liters)

• transmission: transmission type

  • A = automatic,

  • M = manual,

  • AM = automated manual,

  • AM = automated manual (paddles),

  • CVT = continuously variable,

  • SCV = continuously variable with selection paddles,

  • SA = semi-automatic

• lockup_torque_converter:

• air_aspiration:

• model_year:

Source

Data from the US Environmental Protection Agency (EPA) available at https://www.fueleconomy.gov/feg/download.shtml. The file for 2019 model-year vehicles is https://www.fueleconomy.gov/feg/epadata/19data.zip

Data from McClave-Sincich Statistics 11/e

Description

These are relatively small data frames used for exercises

Usage

Clock_auction

Geography_journals

PGA_index

Dowsing

Format

Clock_auction: Prices for grandfather clocks sold in auction

• Sales price for the clock

• Age of the clock

• Number of bidders for the clock

Geography_journals: Prices for geography journals, c. 2005

• journal name of journal

• cost for a one-year subscription

• jif journal impact factor

• cites number of citations of the journal in the past five years

• rpi relative price index

PGA_index: Driving distance, accuracy, and a derived index from the PGA tour

• player name of the player

• dist driving distance in yards

• accuracy percent of drives that land in the fairway

• index an index score for ranking players.

Dowsing: Locations identified by dowsers in an experiment

• trial just the row number

• subject identifying number assigned to the subject

• pipe location of the flowing-water pipe along a 10-meter line (decimeters)

• guess the dowser's guess of the location of the pipe in that trial (decimeters)

Source

StatCrunch

"Big Five" personality ratings for college first-year students

Description

Abstract from the research paper: Five-factor personality ratings were provided by undergraduate freshmen, their parents, and their college peers as predictors of cumulative GPA upon graduation. Conscientiousness ratings were significant predictors of GPA by all three raters; peer ratings of Conscientiousness were the only significant predictor of GPA when self-, parent-, and peer-ratings of Conscientiousness were examined simultaneously. College major was a moderator of this relationship, with self- and parent-ratings of Conscientiousness correlating more strongly with GPA among Social Science majors and parent-ratings of Conscientiousness correlating less strongly with GPA among Science majors. These findings replicate existing research regarding the validity of informant ratings as predictors of behavioral outcomes such as academic performance, while emphasizing the importance of including multiple informants from various life contexts.

Usage

data(McCredie_Kurtz)

Format

For simplicity, only the mother's and father's ratings for the student are given. The variable name indicates whose rating and on what scale, e.g. m_extra is the mothers rating on the extraversion scale. Other variables are:

• subjid: Unique ID for the student

• age: The student's age when the ratings were collected

• GPA: The student's eventual 4-year grade-point average

• sex: The student's sex

• field: What field the student ended up studying

Details

The five personality factors are:

1. extraversion: sociability

2. neuroticism: sadness or emotional instability

3. openness to experience

4. agreeableness: kindness

5. conscientiousness: thoughtfulness

Source

McCredie_Kurtz_Open_Data.sav comes from https://data.mendeley.com/datasets/rn2bpp6f37/1

References

Morgan N. McCredie and John E. Kurtz (2020) "Prospective prediction of academic performance in college using self- and informant-rated personality traits" Journal of Research in Personality 85

Data on run-off from the Monocacy river at Jug Bridge, Maryland.

Description

When it rains, some of the water is absorbed into the ground or quickly evaporates. Some of the water runs off (the "runoff") and is collected by streams and rivers. These data, from a 1964 reference on water resource management, were measured after 25 storms in the basin of the Monocacy River at Jug Bridge, Maryland, US.

Usage

Monocacy_river

Format

A data.frame with 25 observations on the following 2 variables'

• precipitation: amount of rain in inches

• runoff runoff in inches

Details

YOU WERER HERE, COPYING from the Rd file in man/

Source

"Probability Concepts in Engineering" A H-S Ang and W H Tang, 2007, John Wiley based on R.K. Linsley and J.B. Franzini (1964) Water Resources Engineering McGraw-Hill, p.68

Short, simple data frames for textbook examples

Description

These small data frames are for illustration purposes only.

• Nats made up demographic and economic data, by country and year

• Big a simplified form of the PalmerPenguin data

• Tiny an 8-row subset of Big

Usage

Nats

Format

An object of class tbl_df (inherits from tbl, data.frame) with 8 rows and 4 columns.

Relative sizes offspring/parent for many species

Description

Body mass of adult of numerous vertebrate species and newly hatched or born offspring.

Usage

data("Offspring")

Format

A data.frame with 3971 rows, each for a different species

• species

• class (phylogenetic class)

• group (phylogenetic group)

• adult Mass of the adult (female) in grams.

• hatchling Mass of the offspring, in grams. This applies as well to species where the offspring is born rather than hatched.

Source

Shai Meiri, "Endothermy, offspring size and evolution of parental provisioning in vertebrates", Biological Journal of the Linnean Society, 128:4, pp. 1052-6 (See Appendix S1.)

Space Shuttle O-Ring Failures

Description

On January 27, 1986, the night before the space shuttle Challenger exploded, an engineer recommended to the National Aeronautics and Space Administration (NASA) that the shuttle not be launched in the cold weather. The forecasted temperature for the Challenger launch was 31 degrees Fahrenheit—the coldest launch ever. After an intense 3-hour telephone conference, officials decided to proceed with the launch. This data frame contains the launch temperatures and the number of O-ring problems in 24 shuttle launches prior to the Challenger. (This documentation comes from the Sleuth3 package, where the dataset is called ex2223.)

Usage

data(Orings)

Format

A data frame with 24 observations on the following 2 variables.

• temp Launch temperatures (in degrees Fahrenheit)

• incidents Numbers of O-ring incidents

Source

Ramsey, F.L. and Schafer, D.W. (2013). The Statistical Sleuth: A Course in Methods of Data Analysis (3rd ed), Cengage Learning.

Pima Indians Diabetes Database

Description

"The population for this study was the Pima Indian population near Phoenix, Arizona. That population has been under continuous study since 1965 by the National Institute of Diabetes and Digestive and Kidney Diseases because of its high incidence rate of diabetes. Each community resident over 5 years of age was asked to undergo a standardized examination every two years, which included an oral glucose tolerance test. Diabetes was diagnosed according to World Health Organization Criteria; that is, if the 2 hour post-load plasma glucose was at least 200 mg/dl (11.1 mmol/l) at any survey examination or if the Indian Health Service Hospital serving the community found a glucose concentration of at least 200 mg/dl during the course of routine medical care." — quoted from the reference below. The data were published by Kaggle for a machine-learning competition whose goal was to develop a prediction function for diabetes.

Usage

data(PIDD)

Format

768 rows, each of which is a woman 21 years or older. There are 9 variables:

• age of the woman

• pregnancies: number of previous pregnancies

• glucose: glucose level

• BP: systolic blood pressure

• skin_thickness:

• insulin:

• bmi: Body mass index

• pedigree: "Diabetes Pedigree Function"

• diabetes: Did the patient develop diabetes during a 5-year follow-up?

Source

Kaggle

References

Smith, J.W., Everhart, J.E., Dickson, W.C., Knowler, W.C., & Johannes, R.S. (1988) "Using the ADAP learning algorithm to forecast the onset of diabetes mellitus" Proceedings of the Symposium on Computer Applications and Medical Care

Body measurements on penguins

Description

This is the palmerpenguins::penguins data frame with slightly simplified variable names.

Usage

Penguins

Format

An object of class tbl_df (inherits from tbl, data.frame) with 333 rows and 8 columns.

Sample from a college registrar's database

Description

Grade data from students at a liberal arts college. IDs of students, professors, and departments have been dis-identified.

Usage

Sessions

Grades

Gradepoint

Format

Three data frames

• Sessions: ID for a class session, that is, a course in a semester

  • sessionID: Unique identifier for the session

  • iid: Unique identifier for the instructor

  • enroll: Total enrollment in the session (note: includes students who didn't make it into the sample in Grades)

  • dept: Unique identifier for the department

  • level: Instruction evel of the course 100, 200, 300, 400. Roughly: first-year, sophomore, junior, senior

  • sem: The semester in which the session was held.

• Grades: A 50% random sample of student-by-student grades in those Sessions

  • sid: Unique identifier or the student.

  • grade: Letter grade: A, A-, B+ and so on,

  • sessionID: The course session for the grade, as in the Sessions data frame

• Gradepoint: Letter to numerical conversion (per college policy)

  • grade: Letter grade: A, A-, and so on

  • gradepoint: Numerical equivalent

An object of class data.frame with 6124 rows and 3 columns.

An object of class grouped_df (inherits from tbl_df, tbl, data.frame) with 14 rows and 2 columns.

Source

Used with permission by the college's registrar.

STAR Project Data

Description

Data from the STAR (Student–Teacher Achievement Ratio) Project, a four-year longitudinal study examining the effect of class size in early grade levels on educational performance and personal development

Usage

data("STAR")

Format

A data frame with 6325 rows and 6 variables:

• race: black or white

• classtype: kindergarten class type: small, regular, regular with aid

• yearssmall: number of years (0 to 4) in small classes

• hsgrad: high-school graduation (graduated or not). NOTE: There are many NAs

• g4math: total scaled score for the math portion of the fourth-grade standardized test

• g4reading: total scaled score for the reading portion of the fourth-grade standardized test

References

• Imai, Kosuke. 2017. Quantitative Social Science: An Introduction. Princeton University Press. URL from whence these data were added to this package.

• Mosteller, Frederick. 1997. “The Tennessee Study of Class Size in the Early School Grades.” Bulletin of the American Academy of Arts and Sciences 50(7): 14-25. doi = 10.2307/3824562

Shipping losses in 1941 in the Atlantic

Description

A major theater of action in World War II was the Atlantic ocean. Germany attempted through submarine and aerial attacks to sink shipping supplying Britain through the war. This table summarizes the losses in 1941.

Usage

data(Shipping_losses)

Format

12 rows, one for each month of 1941

• month

• nships number of ships sunk

• tons gross tonnage of the ships sunk. This includes both the ship and the cargo.

• country whether the ships were British, or belonged to Allied or Neutral countries.

Source

From W.S. Churchill (1952) The Grand Alliance a history of the Second World War. Houghton Mifflin Co. Boston. p. 782

Roster of applicants to six major departments at UC Berkeley

Description

Roster of applicants to six major departments at UC Berkeley

Usage

data(UCB_applicants)

Format

A data.frame object with 4236 rows, one for each of the applicants to graduate school at UC Berkeley for the Fall 1973 quarter.

• admit: Whether the applicant was admitted.

• gender: male or female

• dept: The graduate department applied to. Rather than identifying the actual departments involved, the data released by Berkeley used letter codes.

Details

In 1973, officials at the University of California Berkeley noticed disturbing trends in graduate admissions rates. The data, with department names redacted, was presented and interpretted in a famous paper in Science, Bickel et al. 1975. In that paper, summary tables were presented. UCB_applicants was reverse engineered from datasets::UCBAdmissions into a data table where the unit of observation is an individual applicant. The origin of datasets::UCBAdmissions is not clear; those data are not explicitly provided in Bickel et al.

Source

The UCBApplicants summary table in the datasets R package.

References

Bickel, P. J., Hammel, E. A., and O'Connell, J. W. (1975). Sex bias in graduate admissions: Data from Berkeley. Science, 187, 398–403.

Monthly tallies of wildfires in the US from 2000 to 2022

Description

Records for each month of wildfires in the US.

Usage

data(AAUP)

Format

275 rows, each of which is a month

• date The year and month in a format that can be easily plotted

• area: total area burned by the wildfires in that month (acres)

• number: the number of wildfires in that month

• month: for convenience, the month (Jan, Feb, ..., Dec) as an ordered factor.

Source

USGS

Experimental data on the yield of winter wheat

Description

In the experiment, eight different varieties of winter wheat were planted in each of 7 calendar years (1996-2002). Each genotype was assigned randomly to a plot within a block.

Usage

data("Wheat")

Format

A data.frame with 240 rows

• genotype The type of wheat.

• yield of the wheat from this plot

• block Major region of the field

• plot Subdivision of block in which the wheat was planted

• year of the planting and measurement

Source

Andrea Onofri "Repeated measures with perennial crops"

Convenience function for adding labels to point_plot or others without needing the ggplot2 + pipe.

Description

Convenience function for adding labels to point_plot or others without needing the ggplot2 + pipe.

Usage

add_plot_labels(P, ..., color = NULL)

Arguments

Value

A ggplot graphics object

Examples

mtcars |> point_plot(mpg ~ hp + cyl) |>
  add_plot_labels(x = "The X axis", y = "Vertical", color = "# cylinders")

Add a slope "rose" to a plot.

Description

To guide a reader in quantifying the slope of components of an x-y graph, a "slope rose" is helpful. Several radiating lines are drawn, each marked with a numerical slope. A suitable choice of slopes is made automatically, based on the x- and y- scale of the plot.

Usage

add_slope_rose(
  P,
  x = NULL,
  y = NULL,
  scale = 1/4,
  color = "red",
  keepers = c("both", "pos", "neg")
)

add_violin_ruler(
  P,
  x = NULL,
  y = NULL,
  width = 1/10,
  ticks = seq(0, 1, by = 0.1),
  ...
)

Arguments

Details

For the ruler, x gives the position of the root of the ruler, with the rest of the ruler moving off to the left. (For vertically oriented rulers, use a negative width.)

Value

A ggplot graphics object

Note

Use the pipe operator to send a previously made plot to have a rose added. Don't use the {ggplot2} + connector.

Examples

mtcars |> point_plot(mpg ~ hp, annot="model") |> add_slope_rose()
mtcars |> point_plot(wt ~ hp) |> add_slope_rose(keepers="pos", color="blue", x=100, scale=.5 )

Helpers for specifying nodes in simulations

Description

Helpers for specifying nodes in simulations

Mix two variables together. The output will have the specified R-squared with var1 and variance one.

Evaluate an expression separately for each case

Usage

categorical(n = 5, ..., exact = TRUE)

cat2value(variable, ...)

bernoulli(n = 0, logodds = NULL, prob = 0.5, labels = NULL)

mix_with(signal, noise = NULL, R2 = 0.5, var = 1, exact = FALSE)

each(ex)

block_by(block_var, levels = c("treatment", "control"), show_block = FALSE)

random_levels(n, k = NULL, replace = FALSE)

Arguments

Details

datasim_make() constructs a simulation which can then be run with datasim_run(). Each argument to datasim_make() specifies one node of the simulation using an assignment-like syntax such as y <- 3*x + 2 + rnorm(n). The datasim helpers documented here are for use on the right-hand side of the specification of a node. They simplify potentially complex operations such as blocking, creation of random categorical methods, translation from categorical to numerical values, etc.

The target R-squared and variance will be achieved only if exact=TRUE or the sample size goes to infinity.

Value

A numerical or categorical vector which will be assembled into a data frame by datasim_run()

Examples

Demo <- datasim_make(
  g <- categorical(n, a=2, b=1, c=0.5),
  x <- cat2value(g, a=-1.7, b=0.1, c=1.2),
  y <- bernoulli(logodds = x, labels=c("no", "yes")),
  z <- random_levels(n, k=4),
  w <- mix_with(x, noise=rnorm(n), R2=0.75, var=1),
  treatment <- block_by(w),
  dice <- each(rnorm(1, sd = abs(w)))
)

Summaries of regression models

Description

The summaries are always in the form of a data frame

• conf_interval() — displays coefficients and their confidence intervals

• R2() — R-squared of a model together with related measures such as F, adjusted R-squared, the p-value, and degrees of freedom used in calculating the p-value.

• regression_summary() – A regression report in data-frame format.

• anova_summary() — An ANOVA report in data-frame format. If only one model is passed as an argument, the data frame will have one line per model term. If multiple models are given as arguments, the ANOVA report will show the increments from one model to the next.

Usage

conf_interval(model, level = 0.95, show_p = FALSE)

R2(model)

regression_summary(model)

anova_summary(...)

Arguments

Details

Many of these are wrappers around broom::tidy() used to emphasize to students that the results are a summary in the form of a regression report, similar to the summaries produced by stats::confint(), stats::coef(), etc.

Value

a data frame

Examples

Model <- CRDS |> model_train(FEV ~ age + smoker)
Model |> conf_interval()
Model |> R2()
Model |> anova_summary()

Draw a DAG

Description

Make a simple drawing of a Directed Acyclic Graph as constructed by datasim_make.

Usage

dag_draw(DAG, ..., report_hidden = FALSE)

Arguments

Details

See the igraph package for more details.

By default, edges are not drawn to hidden nodes, that is, those whose names begin with a dot. To show the hidden nodes, use the argument show_hidden=TRUE.

Value

No return value. Called for graphics side-effects.

Examples

dag_draw(sim_03)

Construct and modify data simulations

Description

Construct and modify data simulations

Usage

datasim_make(...)

datasim_to_igraph(sim, report_hidden = FALSE)

datasim_intervene(sim, ...)

Arguments

Details

Simulations in LSTbook are first specified by providing the code for each node (which can be written in terms of the values of other nodes). Once constructed, data can be extracted from the simulation using datasim_run(n) or the generic take_sample(n).

Each argument defines one node in the simulation. The argument syntax is unusual, using assignment. For instance, an argument y <- 3*x + rnorm(n) defines a node named y. The R code on the RHS of the assignment operator (that is, 3*x + rnorm(n) in the example) will be used by datasim_run() to generate the y variable when the simulation is run. Nodes defined by previous arguments can be used in the code for later arguments.

Helper functions such as mix_with(), categorical(), and several others can be used within the node to perform complex operations.

Remember to use commas to separate the arguments in the normal way.

Value

an object of class "datasim". Internally, this is a list of the R assignment expressions used when running the simulation.

Examples

Simple_sim <- datasim_make(x <- rnorm(n, sd=2), y <- 3*x + rnorm(n))
Simple_sim |> datasim_run(n = 5)

Run a datasim simulation, producing a data frame

Description

Run a datasim simulation, producing a data frame

Usage

datasim_run(sim, n = 5, seed = NULL, report_hidden = FALSE)

Arguments

Value

a data frame with a column for each node in the datasim object.

Utilities

Description

Functions for pulling various components from model objects. These work mainly for lm and glm objects. It's a future project to add facilities for other object types.

Usage

explanatory_vars(model, ...)

response_var(model, ...)

response_values(model, ...)

formula_from_mod(model, ...)

get_training_data(model, ...)

Arguments

Evaluate a model on inputs

Description

Evaluate a model on inputs

Usage

model_eval(
  mod,
  data = NULL,
  ...,
  skeleton = FALSE,
  ncont = 3,
  interval = c("prediction", "confidence", "none"),
  level = 0.95,
  type = c("response", "link")
)

Arguments

Value

A data frame. There is one row for each row of the input values (see data parameter). The columns include

• the explanatory variables

• .output — the output of the model that corresponds to the explanatory value

• the .lwr and .upr bounds of the prediction or confidence interval

• if training data is used as the input, then it's possible to calculate the residual. This will be called .resid.

Examples

mod <- mtcars |> model_train(mpg ~ hp + wt)
model_eval(mod, hp=100, wt=c(2,3))
model_eval(mod) # training data
model_eval(mod, skeleton=TRUE)

Helper functions to evaluate models

Description

Only used internally in {LSTbook}. These were originally arranged as S3 methods, but now the dispatch is done "by hand" in order to eliminate any exported S3 methods.

Usage

model_eval_fun(model, data = NULL, interval = "none", level = 0.95, ...)

Arguments

Value

a data frame

Check model type against model specification and data

Description

This can be used to automatically determine a model type or to determine if the specified model type is consistent with the specification/data

Usage

model_family(
  .data,
  .tilde,
  family = c("auto", "lm", "linear", "binomial", "poisson", "svm", "gaussian", "rlm")
)

Arguments

Graph a model function

Description

Every model has an implicit function whose output is the response variable and which has one or more explanatory variables. (Exceptionally, there might be no explanatory variables as in response ~ 1.) One of the explanatory variables can be mapped to the horizontal axis; this can be either quantitative or categorical. The remaining explanatory variables will be mapped to color, facet-horizontal, and facet-vertical. For visual clarity, any quantitative variables among these remaining variables must be coerced to categorical, corresponding to a discrete set of colors and a discrete set of facets.

Usage

model_plot(
  mod,
  nlevels = 3,
  interval = c("confidence", "prediction", "none"),
  level = 0.95,
  palette = LETTERS[1:8],
  model_ink = 0.7
)

Arguments

Value

A ggplot graphics object

Convert a model to a skeleton

Description

A "skeleton" is a data frame containing "nicely spaced" values for the explanatory variables in a model.

Usage

model_skeleton(mod, data = NULL, ncont = 3, nfirstcont = 50)

Arguments

Value

a data frame

Examples

Model <- CRDS |> model_train(FEV ~ sex + age + height)
Model |> model_skeleton()

train a model, easily

Description

An interface to several of the most often used model-fitting routines designed to make it easy to construct.

Usage

model_train(
  data,
  tilde,
  family = c("auto", "lm", "linear", "binomial", "poisson", "rlm")
)

Arguments

Details

Since data may be piped into this function, the training data frame will be called simply "data", the name of the first argument to this function. In order to be able to access the training data in such cases, the training data is assigned to an attribute of the resulting model, "training_data".

Value

An object of class "model_object". This is much the same as an "lm" or "glm" object but with the additional attribute of the training data and a printing method that encourages the use of the regression summary methods conf_interval(), R2(), or anova_summary()

Construct a model and return the model values

Description

One-stop shopping to fit a model and return the model output on the training data.

Usage

model_values(data, tilde, family = c("linear", "prob", "counts"))

Arguments

Details

This is intended to be used ONLY WITHIN mutate()

Value

A vector (not a data frame) of the model evaluated on the training data. This is intended mainly for use within mutate(), so that a general model can be used in the place of simple reduction verbs like mean(), median()

Examples

mtcars |> mutate(mpg_mod = model_values(mpg ~ hp + wt)) |> select(hp, wt, mpg_mod) |> head()

Cull objects used with do()

Description

The do() function facilitates easy replication for randomization tests and bootstrapping (among other things). Part of what makes this particularly useful is the ability to cull from the objects produced those elements that are useful for subsequent analysis. cull_for_do does this culling. It is generic, and users can add new methods to either change behavior or to handle additional classes of objects.

Usage

mosaic_cull_for_do(object, ...)

Arguments

Details

When do(n) * expression is evaluated, expression is evaluated n times to produce a list of n result objects. cull_for_do is then applied to each element of this list to extract from it the information that should be stored. For example, when applied to a object of class "lm", the default cull_for_do extracts the coefficients, coefficient of determinism, an the estimate for the variance, etc.

Examples

Clock_auction |> model_train(price ~ resample(bidders)) |>
  R2() |> trials(times=10)

Create vector based on roughly equally sized groups

Description

Create vector based on roughly equally sized groups

Usage

ntiles(
  x,
  n = 3,
  format = c("rank", "interval", "mean", "median", "center", "left", "right"),
  digits = 3
)

Arguments

Details

This is a functional clone of mosaic::ntiles in order to avoid the dependency. It should be removed in the future, when there is no need to avoid such dependency, e.g. when {mosaic} is available on WASM.

Value

a vector. The type of vector will depend on format.

Examples

CRDS |> head(20) |> mutate(group = ntiles(height, 3, format="center"))
CRDS |> head(20) |> mutate(group = ntiles(height, 3, format="interval"))

One-step data graphics

Description

point_plot() makes it easy to construct an informative basic graph of a data frame. "Making it easy" means that the user only needs to specify two things: 1) the data frame to be used and 2) a tilde expression with the response variable on the left and up to three explanatory variables on the right. The response variable is mapped to the vertical axis while the first explanatory variable defines the horizontal axis. The second explanatory variable (if any) maps to color, the third (if any) defines facets. Quantitative variables used for color or faceting are cut into categorical variables, so color and facets will always be discrete.

Usage

point_plot(
  D,
  tilde,
  ...,
  seed = 101,
  annot = c("none", "violin", "model", "bw"),
  jitter = c("default", "none", "all", "x", "y"),
  interval = c("confidence", "none", "prediction"),
  point_ink = 0.5,
  model_ink = 0.4,
  palette = LETTERS[1:8],
  bw = NULL,
  level = 0.95,
  nx = 50,
  model_family = NULL
)

Arguments

Details

When an x- or y- variables is categorical, jittering is automatically applied.

Using annot = "model" will annotate the data with the graph of a model — shown as confidence intervals/bands — corresponding to the tilde expression. annot = "violin" will annotate with a violin plot.

If you want to use the same explanatory variable for color and faceting (this might have pedagogical purposes) merely repeat the name of the color variable in the faceting position, e.g. mpg ~ hp + cyl + cyl.

Value

A ggplot graphics object

See Also

add_plot_labels to add labels to the plot (without needing the ggplot2 + pipe)

Examples

mosaicData::Galton |> point_plot(height ~ mother + sex + father, annot="model", model_ink=1)
mtcars |> point_plot(mpg ~ wt + cyl)
mtcars |> point_plot(mpg ~ wt + cyl + hp, annot="model")

Nice printing of some internal objects

Description

Nice printing of some internal objects

Usage

## S3 method for class 'datasim'
print(x, ..., report_hidden = FALSE)

Arguments

A printing method for model objects

Description

A printing method for model objects

Usage

## S3 method for class 'model_object'
print(x, ...)

Arguments

Create columns with random numbers for modeling

Description

For demonstration purposes, add the specified number of random columns to a model matrix. This is intended to be used in modeling functions, e.g. model_train(), lm(), and so on to explore the extent to which random columns "explain" the response variable.

Usage

random_terms(df = 1, rdist = rnorm, args = list(), n, seed = NULL)

Arguments

Details

random_terms() will try to guess a suitable value for n based on the calling function.

Examples

 mtcars |> model_train(mpg ~ wt + random_terms(4)) |> conf_interval()
 mtcars |> model_train(mpg ~ wt + random_terms(4)) |> anova_summary()
 head(mtcars) |> select(wt, mpg) |> mutate(r = random_terms(3))

Simulations for use in Lessons in Statistical Thinking

Description

These datasim objects are provided.

Usage

sim_00

sim_01

sim_02

sim_03

sim_04

sim_05

sim_06

sim_07

sim_08

sim_09

sim_10

sim_11

sim_12

sim_flights

sim_medical_observations

sim_prob_21.1

sim_satgpa

sim_school1

sim_school2

sim_vaccine

Format

An object of class list (inherits from datasim) of length 2.

An object of class list (inherits from datasim) of length 2.

An object of class list (inherits from datasim) of length 2.

An object of class list (inherits from datasim) of length 2.

An object of class list (inherits from datasim) of length 2.

An object of class list (inherits from datasim) of length 2.

An object of class list (inherits from datasim) of length 2.

An object of class list (inherits from datasim) of length 2.

An object of class list (inherits from datasim) of length 2.

An object of class list (inherits from datasim) of length 2.

An object of class list (inherits from datasim) of length 2.

An object of class list (inherits from datasim) of length 2.

An object of class list (inherits from datasim) of length 2.

An object of class list (inherits from datasim) of length 2.

An object of class list (inherits from datasim) of length 2.

An object of class list (inherits from datasim) of length 2.

An object of class list (inherits from datasim) of length 2.

An object of class list (inherits from datasim) of length 2.

An object of class list (inherits from datasim) of length 2.

An object of class list (inherits from datasim) of length 2.

Details

They are defined in the sim_library.R file in ⁠inst/⁠

Evaluate a tilde expression on a data frame

Description

Evaluate a tilde expression on a data frame

Usage

split_tilde(tilde)

Arguments

Samples from various kinds of objects

Description

A set of methods to generate random samples from data frames and data simulations. For data frames, individual rows are sampled. For vectors, elements are sampled.

Usage

take_sample(x, n, replace = FALSE, ...)

## Default S3 method:
take_sample(
  x,
  n = length(x),
  replace = FALSE,
  prob = NULL,
  .by = NULL,
  groups = .by,
  orig.ids = FALSE,
  ...
)

resample(..., replace = TRUE)

Arguments

Details

These are based in spirit on the sample functions in the {mosaic} package, but are redefined here to 1) avoid a dependency on {mosaic} and 2) bring the arguments in line with the ⁠.by =⁠ features of {dplyr}.

Value

A vector or a data frame depending on the nature of the x argument.

Examples

take_sample(sim_03, n=5) # run a simulation
take_sample(Clock_auction, n = 3) # from a data frame
take_sample(1:6, n = 6) # sample from a vector

Run the left side of the pipeline multiple times.

Description

Write a pipeline to perform some calculation whose result can be coerced into one line of a data frame. Add trials(times=3) to the end of the pipeline in order to repeat the calculation multiple times. Typically, each trial involves some random component, but another (or an additional) capability is to parameterize the pipeline expression by including some unbound variable in it, e.g. lambda. Then call trials(lambda=c(10,20)) to repeat the calculation for each of the elements of the named parameter.

Usage

trials(.ex, times = 1, ...)

Arguments

Details

This is intended as a pipeline friendly replacement for mosaic::do().

Value

a dataframe with one row for each trial. (But see the ... argument.)

Examples

mean(rnorm(10)) |> trials(times=3)
mean(rnorm(lambda)) |> trials(lambda=c(1, 100, 10000))
mean(rnorm(lambda)) |> trials(times=5, lambda=c(1, 100, 10000))
take_sample(mtcars, n=lambda, replace=TRUE) |> select(mpg, hp) |>
  model_train(mpg ~ resample(hp)) |>
  regression_summary() |> trials(times=3, lambda=c(10, 20, 40)) |>
  filter(term == "resample(hp)")

Zero-one transformation for categorical variable

Description

A convenience function for handling categorical response variables. Ordinarily, ggplot2 maps categorical levels to numerical values 1, 2, .... Such numerical mapping is inappropriate for logistic modeling, where we want the levels to be on a probability scale.

Usage

zero_one(x, one)

label_zero_one(P)

Arguments

Value

A numerical vector of 0s and 1s.

Examples

Birdkeepers |>
  point_plot(zero_one(LC, one="LungCancer") ~ AG + BK, annot = "model")

Birdkeepers |>
  mutate(Condition = zero_one(LC, one = "LungCancer")) |>
  point_plot(Condition ~ AG + BK, annot = "model") |>
  label_zero_one() |>
  add_plot_labels(x="age", color = "Birdkeeper?")