kilns: The 'kilns' data sets.

In mixreg: Functions to Fit Mixtures of Regressions

Description

Data on the rotation times of kilns.

Usage

    kilnAfull
    kilnAsubset
    kilnAoneOut
    kilnB

Format

Each data set is a data frame with columns consisting of observations on variables x and y:

x

a numeric vector of reciprocals of percentages

y

a numeric vector of times of a single revolution of a kiln, in seconds

• The data set kilnAfull has 3793 observations.

• The data set kilnAoneOut has 3792 observations.

• The data set kilnAsubset has 92 observations.

• The data set kilnB has 3740 observations.

Details

These data consist of observation relating to the rotation times of two kilns, “A” and “B”. They are daily averages observed over 11 years, or 4017 days, from 1 January 2005 to 31 December 2015. The kilnAsubset data consist of a small subset of the kilnAfull data. The kilnAoneOut data set is the same as the kilnAfull data set but with one row/observation, number 1171 (which appears to be an outlier in some sense), removed.

The reason that kilnAfull and kilnB do not contain 4017 observation is that there were a number of missing values in both x and y. Rows in which either or both x and y were missing (there were 224 such) were deleted. Likewise 277 rows were deleted in the process of forming kilnB.

Plots of the percentages versus times displayed patterns of points with curved structure. Transforming the percentages to their reciprocals changed these patterns to ones that are very close to being straight lines.

The kiln “A” data clearly involve three components. The kiln “B” data involve only two components (likewise clearly discernible).

Source

The data were kindly provided by Petr Pikal (Prerov, Czech Republic).

Examples

    fit1 <- mixreg(y~x,data=kilnAfull,ncomp=3,seed=173)
    plot(fit1) # Components 1 and 2 seem to have got swapped and 
               # the component 1 (???) line is a bit skew-wiff.
# There's a point that looks to be a bit of an outlier.
# It has been identified to be point 1171.
    with(kilnAfull,text(x[1171],y[1171],labels="1171",
                        adj=-0.3,col="red"))
# Removing this point gives kilnAoneOut.
    fit2 <- mixreg(y~x,data=kilnAoneOut,ncomp=3,seed=173)
    plot(fit2) # Still no good; same as fit1, although the "outlier" is gone.
## Not run: 
    vfit <- visualFit(y~x,data=kilnAoneOut,ncomp=3)
    fit3 <- mixreg(y~x,data=kilnAoneOut,ncomp=3,thetaStart=vfit$theta)
    plot(fit3) # Much better.
    chk <- mixreg(y~x,data=kilnAfull,ncomp=3,thetaStart=vfit$theta)
    plot(chk) # No good; same as fit1 and fit2 but without the swapping
              # of components 1 and 2.  It was the outlier that caused the
              # problem, not the random starting values.
    
## End(Not run)
   thStrt <- list(
                   list(beta=c(26.07,48808),sigsq=1.1573,lambda=0.33333333),
                   list(beta=c(23.48,32387),sigsq=1.8730,lambda=0.33333333),
                   list(beta=c(-0.0597,20760),sigsq=0.2478,lambda=0.33333333)
                 )
# Roughly vfit$theta.
    fit3.a <- mixreg(y~x,data=kilnAoneOut,ncomp=3,thetaStart=thStrt)
    plot(fit3.a) # Sames as fit3.
    chk.a <- mixreg(y~x,data=kilnAfull,ncomp=3,thetaStart=thStrt)
    plot(chk.a) # Same as chk.

mixreg documentation built on Oct. 14, 2021, 9:12 a.m.