This function gives some helpful variable-wise information for cluster interpretation, given a clustering and a data set. The output object contains some tables. For categorical variables, tables compare clusterwise distributions with overall distributions. Continuous variables are categorised for this.

If desired, tables, histograms, some standard statistics of
continuous variables and validation plots as available through
`discrproj`

(Hennig 2004) are given out on the fly.

```
cluster.varstats(clustering,vardata,contdata=vardata,
clusterwise=TRUE,
tablevar=NULL,catvar=NULL,
quantvar=NULL, catvarcats=10,
proportions=FALSE,
projmethod="none",minsize=ncol(contdata)+2,
ask=TRUE,rangefactor=1)
```# S3 method for varwisetables
print(x,digits=3,...)

clustering

vector of integers. Clustering (needs to be in standard coding, 1,2,...).

vardata

data matrix or data frame of which variables are summarised.

contdata

variable matrix or data frame, normally all or some
variables from `vardata`

, on which cluster visualisation by
projection methods is performed unless `projmethod="none"`

. It
should make sense to interpret these variables in a quantitative
(interval-scaled) way.

clusterwise

logical. If `FALSE`

, only the output tables
are computed but no more detail and graphs are given on the fly.

tablevar

vector of integers. Numbers of variables treated as
categorical (i.e., no histograms and statistics, just tables) if
`clusterwise=TRUE`

. Note
that an error will be produced by factor type variables unless they
are declared as categorical here.

catvar

vector of integers. Numbers of variables to be categorised by proportional quantiles for table computation. Recommended for all continuous variables.

quantvar

vector of integers. Variables for which means,
standard deviations and quantiles should be given out if
`clusterwise=TRUE`

.

catvarcats

integer. Number of categories used for
categorisation of variables specified in `quantvar`

.

proportions

logical. If `TRUE`

, output tables contain
proportions, otherwise numbers of observations.

projmethod

one of `"none"`

, `"dc"`

, `"bc"`

,
`"vbc"`

, `"mvdc"`

, `"adc"`

, `"awc"`

(recommended
if not `"none"`

), `"arc"`

, `"nc"`

, `"wnc"`

,
`"anc"`

. Cluster validation projection method introduced in
Hennig (2004), passed on as `method`

argument in
`discrproj`

.

minsize

integer. Projection is not carried out for clusters with fewer points than this. (If this is chosen smaller, it may lead to errors with some projection methods.)

ask

logical. If `TRUE`

, `par(ask=TRUE)`

is set in the
beginning to prompt the user before plots and `par(ask=FALSE)`

in the end.

rangefactor

numeric. Factor by which to multiply the range for projection plot ranges.

x

an object of class `"varwisetables"`

, output object of
`cluster.varstats`

.

digits

integer. Number of digits after the decimal point to print out.

...

not used.

An object of class `"varwisetables"`

, which is a
list with a table for each variable, giving (categorised) marginal
distributions by cluster.

Hennig, C. (2004) Asymmetric linear dimension reduction for classification. Journal of Computational and Graphical Statistics 13, 930-945 .

# NOT RUN { set.seed(112233) options(digits=3) require(MASS) require(flexmix) data(Cars93) Cars934 <- Cars93[,c(3,5,8,10)] cc <- discrete.recode(Cars934,xvarsorted=FALSE,continuous=c(2,3),discrete=c(1,4)) fcc <- flexmix(cc$data~1,k=2, model=lcmixed(continuous=2,discrete=2,ppdim=c(6,3),diagonal=TRUE)) cv <- cluster.varstats(fcc@cluster,Cars934, contdata=Cars934[,c(2,3)], tablevar=c(1,4),catvar=c(2,3),quantvar=c(2,3),projmethod="awc", ask=FALSE) print(cv) # }