`flexmixedruns`

fits a latent class
mixture (clustering) model where some variables are continuous
and modelled within the mixture components by Gaussian distributions
and some variables are categorical and modelled within components by
independent multinomial distributions. The fit is by maximum
likelihood estimation computed with the EM-algorithm. The number of
components can be estimated by the BIC.

Note that at least one categorical variable is needed, but it is possible to use data without continuous variable.

```
flexmixedruns(x,diagonal=TRUE,xvarsorted=TRUE,
continuous,discrete,ppdim=NULL,initial.cluster=NULL,
simruns=20,n.cluster=1:20,verbose=TRUE,recode=TRUE,
allout=TRUE,control=list(minprior=0.001),silent=TRUE)
```

x

data matrix or data frame. The data need to be organised case-wise, i.e., if there are categorical variables only, and 15 cases with values c(1,1,2) on the 3 variables, the data matrix needs 15 rows with values 1 1 2. (Categorical variables could take numbers or strings or anything that can be coerced to factor levels as values.)

diagonal

logical. If `TRUE`

, Gaussian models are fitted
restricted to diagonal covariance matrices. Otherwise, covariance
matrices are unrestricted. `TRUE`

is consistent with the
"within class independence" assumption for the multinomial
variables.

xvarsorted

logical. If `TRUE`

, the continuous variables
are assumed to be the first ones, and the categorical variables to
be behind them.

continuous

vector of integers giving positions of the
continuous variables. If `xvarsorted=TRUE`

, a single integer,
number of continuous variables.

discrete

vector of integers giving positions of the
categorical variables. If `xvarsorted=TRUE`

, a single integer,
number of categorical variables.

ppdim

vector of integers specifying the number of (in the data)
existing categories for each categorical variable. If
`recode=TRUE`

, this can be omitted and is computed
automatically.

initial.cluster

this corresponds to the `cluster`

parameter in `flexmix`

and should only be specified if
`simruns=1`

and `n.cluster`

is a single number.
Either a matrix with `n.cluster`

columns of initial cluster
membership probabilities for each observation; or a factor or
integer vector with the initial cluster assignments of
observations at the start of the EM algorithm. Default is
random assignment into `n.cluster`

clusters.

simruns

integer. Number of starts of the EM algorithm with random initialisation in order to find a good global optimum.

n.cluster

vector of integers, numbers of components (the optimum one is found by minimising the BIC).

verbose

logical. If `TRUE`

, some information about the
different runs of the EM algorithm is given out.

recode

logical. If `TRUE`

, the function
`discrete.recode`

is applied in order to recode categorical
data so that the `lcmixed`

-method can use it. Only set this
to `FALSE`

if your data already has that format (even it that
case, `TRUE`

doesn't do harm). If `recode=FALSE`

, the
categorical variables are assumed to be coded 1,2,3,...

allout

logical. If `TRUE`

, the regular
`flexmix`

-output is given out for every single number of
clusters, which can create a huge output object.

control

list of control parameters for `flexmix`

, for
details see the help page of `FLXcontrol-class`

.

silent

logical. This is passed on to the
`try`

-function. If `FALSE`

, error messages from
failed runs of `flexmix`

are suppressed. (The information that
a `flexmix`

-error occurred is still given out if
`verbose=TRUE`

).

A list with components

summary object for `flexmix`

object with
optimal number of components.

optimal number of components.

vector with numbers of EM runs for each number of components that led to flexmix errors.

if `allout=TRUE`

, list of flexmix output objects
for all numbers of components, for details see the help page of
`flexmix-class`

. Slots that can be used
include for example `cluster`

and `components`

. So
if `fo`

is the `flexmixedruns`

-output object,
`fo$flexout[[fo$optimalk]]@cluster`

gives a component number
vector for the observations (maximum posterior rule), and
`fo$flexout[[fo$optimalk]]@components`

gives the estimated
model parameters, which for `lcmixed`

and therefore
`flexmixedruns`

are called

- center
mean vector

- cov
covariance matrix

- pp
list of categorical variable-wise category probabilities

`allout=FALSE`

, only the flexmix output object for the
optimal number of components, i.e., the `[[fo$optimalk]]`

indexing above can then be omitted.vector of values of the BIC for each number of components.

vector of categorical variable-wise numbers of categories.

list of levels of the categorical variables
belonging to what is treated by `flexmixedruns`

as category
1, 2, 3 etc.

Sometimes flexmix produces errors because of degenerating covariance
matrices, too small clusters etc. `flexmixedruns`

tolerates these
and treats them as non-optimal runs. (Higher `simruns`

or
different `control`

may be required to get a valid solution.)

General documentation on flexmix can be found in Friedrich Leisch's "FlexMix: A General Framework for Finite Mixture Models and Latent Class Regression in R", https://CRAN.R-project.org/package=flexmix

Hennig, C. and Liao, T. (2013) How to find an appropriate clustering
for mixed-type variables with application to socio-economic
stratification, *Journal of the Royal Statistical Society, Series
C Applied Statistics*, 62, 309-369.

`lcmixed`

, `flexmix`

,
`FLXcontrol-class`

,
`flexmix-class`

,
`discrete.recode`

.

# NOT RUN { options(digits=3) set.seed(776655) v1 <- rnorm(100) v2 <- rnorm(100) d1 <- sample(1:5,100,replace=TRUE) d2 <- sample(1:4,100,replace=TRUE) ldata <- cbind(v1,v2,d1,d2) fr <- flexmixedruns(ldata, continuous=2,discrete=2,simruns=2,n.cluster=2:3,allout=FALSE) print(fr$optimalk) print(fr$optsummary) print(fr$flexout@cluster) print(fr$flexout@components) # }