ozone: ozone

In rchemo: Dimension Reduction, Regression and Discrimination for Chemometrics

ozone

Description

Los Angeles ozone pollution data in 1976 (sources: Breiman & Friedman 1985, Leisch & Dimitriadou 2020).

Usage

data(ozone)

Format

A list with 1 component: the matrix X with 366 observations, 13 variables. The variable to predict is V4.

V1

Month: 1 = January, ..., 12 = December

V2

Day of month

V3

Day of week: 1 = Monday, ..., 7 = Sunday

V4

Daily maximum one-hour-average ozone reading

V5

500 millibar pressure height (m) measured at Vandenberg AFB

V6

Wind speed (mph) at Los Angeles International Airport (LAX)

V7

Humidity (%) at LAX

V8

Temperature (degrees F) measured at Sandburg, CA

V9

Temperature (degrees F) measured at El Monte, CA

V10

Inversion base height (feet) at LAX

V11

Pressure gradient (mm Hg) from LAX to Daggett, CA

V12

Inversion base temperature (degrees F) at LAX

V13

Visibility (miles) measured at LAX

Source

Breiman L., Friedman J.H. 1985. Estimating optimal transformations for multiple regression and correlation, JASA, 80, pp. 580-598.

Leisch, F. and Dimitriadou, E. (2010). mlbench: Machine Learning Benchmark Problems. R package version 1.1-6. https://cran.r-project.org/.

Examples

data(ozone)

z <- ozone$X
head(z)

plotxna(z)

rchemo documentation built on Sept. 11, 2024, 8:05 p.m.