flexclust (version 1.4-1)

slswFlexclust: Segment Level Stability Within Solution.

Description

Assess segment level stability within solution.

Usage

slswFlexclust(x, object, ...)
# S4 method for resampleFlexclust,missing
plot(x, y, ...)
# S4 method for resampleFlexclust
boxplot(x, which=1, ylab=NULL, ...)
# S4 method for resampleFlexclust
densityplot(x, data, which=1, ...)
# S4 method for resampleFlexclust
summary(object)

Arguments

x

A numeric matrix of data, or an object that can be coerced to such a matrix (such as a numeric vector or a data frame with all numeric columns) passed to stepFlexclust.

object

Object of class "kcca" for slwsFlexclust and "resampleFlexclust" for the summary method.

y

Missing.

which

Integer or character indicating which validation measure is used for plotting.

ylab

Axis label.

data

Not used.

…

Additional arguments; for details see below.

Value

An object of class "resampleFlexclust".

Details

Additional arguments in slswFlexclust are argument nsamp which is by default equal to 100 and allows to change the number of bootstrap pairs drawn. Argument seed allows to set a random seed and argument multicore is by default TRUE and indicates if bootstrap samples should be drawn in parallel. Argument verbose is by default equal to FALSE and if TRUE progress information is shown during computations.

There are plotting as well as printing and summary methods implemented for objects of class "resampleFlexclust". In addition to a standard plot method also methods for densityplot and boxplot are provided.

For more details see Dolnicar and Leisch (2017) and Dolnicar et al. (2018).

References

Dolnicar S. and Leisch F. (2017) "Using Segment Level Stability to Select Target Segments in Data-Driven Market Segmentation Studies" Marketing Letters, 28 (3), pp. 423--436.

Dolnicar S., Gruen B., and Leisch F. (2018) Market Segmentation Analysis: Understanding It, Doing It, and Making It Useful. Springer Singapore.

See Also

slsaplot

Examples

# NOT RUN {
data("Nclus")
cl3 <- kcca(Nclus, k = 3)
slsw.cl3 <- slswFlexclust(Nclus, cl3, nsamp = 20)
plot(Nclus, col = clusters(cl3))
plot(slsw.cl3)
densityplot(slsw.cl3)
boxplot(slsw.cl3)
# }