Description Usage Format Details Source Examples

Data on the rotation times of kilns.

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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.

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).

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

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 | ```
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.
``` |

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