steamUse R Documentation

## Steam Usage Data (Excerpt)

### Description

The monthly use of steam (Steam) in a factory may be modeled and described as function of the operating days per month (Operating.Days) and mean outside temperature per month (Temperature).

### Usage

data("steamUse", package="robustbase")

### Format

A data frame with 25 observations on the following 9 variables.

Steam:

regression response Y, the poinds of steam used monthly.

fattyAcid:

pounds of Real Fatty Acid in storage per month.

glycerine:

wind:

average wind velocity in miles per hour (a numeric vector).

days:

an integer vector with number of days of that month, i.e., in 28..31.

op.days:

the number of operating days for the given month (integer).

freeze.d:

the number of days below 32 degrees Fahrenheit (= 0°C (C=Celsius) = freezing temperature of water).

temperature:

a numeric vector of average outside temperature in Fahrenheit (F).

startups:

the number of startups (of production in that month).

### Details

Nor further information is given in Draper and Smith, about the place and exacts years of the measurements, though some educated guesses should be possible, see the examples.

### Source

Data from Draper and Smith, 1st ed, 1966; appendix A.

A version of this has been used in teaching at SfS ETH Zurich, since at least 1996, https://stat.ethz.ch/Teaching/Datasets/NDK/dsteam.dat

The package aprean3 contains all data sets from the 3rd edition of Draper and Smith (1998), and this data set with variable names x1 .. x10 (x9 being wind^2, hence extraneous).

### References

Draper and Smith (1981) Applied Regression Analysis (2nd ed., p. 615 ff)

### Examples

## Not run:
if(require("aprean3")) { # show how  'steamUse'  is related to 'dsa01a'
stm <- dsa01a
names(stm) <- c("Steam", "fattyAcid", "glycerine", "wind",
"days", "op.days", "freeze.d",
"temperature", "wind.2", "startups")
## prove that wind.2 is  wind^2, "traditionally" rounded to 1 digit:
stopifnot(all.equal(floor(0.5 + 10*stm[,"wind"]^2)/10,
stm[,"wind.2"], tol = 1e-14))
## hence drop it
steamUse <- stm[, names(stm) != "wind.2"]
}

## End(Not run)
data(steamUse)
str(steamUse)
## Looking at this,
cbind(M=rep_len(month.abb, 25), steamUse[,5:8, drop=FALSE])
## one will conjecture that these were 25 months, Jan--Jan in a row,
## starting in a leap year (perhaps 1960 ?).

plot(steamUse)

summary(fm1 <- lmrob(Steam ~ temperature + op.days, data=steamUse))
## diagnoses 2 outliers: month of July, maybe company-wide summer vacations

## KS2014 alone seems not robust enough:
summary(fm.14 <- lmrob(Steam ~ temperature + op.days, data=steamUse,
setting="KS2014"))
pairs(Steam ~ temperature+op.days, steamUse)