clusterGamma {MixAll} | R Documentation |
Create an instance of the [ClusterGamma
] class
Description
This function computes the optimal gamma mixture model according
to the criterion
among the list of model given in models
and the number of clusters given in nbCluster
, using the strategy
specified in strategy
.
Usage
clusterGamma(
data,
nbCluster = 2,
models = "gamma_pk_ajk_bjk",
strategy = clusterStrategy(),
criterion = "ICL",
nbCore = 1
)
Arguments
data |
frame or matrix containing the data. Rows correspond to observations and columns correspond to variables. If the data set contains NA values, they will be estimated during the estimation process. |
nbCluster |
[ |
models |
[ |
strategy |
a [ |
criterion |
character defining the criterion to select the best model. The best model is the one with the lowest criterion value. Possible values: "BIC", "AIC", "ICL", "ML". Default is "ICL". |
nbCore |
integer defining the number of processor to use (default is 1, 0 for all). |
Value
An instance of the [ClusterGamma
] class.
Author(s)
Serge Iovleff
Examples
## A quantitative example with the famous geyser data set
data(geyser)
## add 10 missing values
x = geyser;
x[round(runif(5,1,nrow(geyser))), 1] <- NA
x[round(runif(5,1,nrow(geyser))), 2] <- NA
## use graphics functions
set.seed(2)
model <- clusterGamma( data=x, nbCluster=2:3
, models="gamma_pk_ajk_bjk"
, strategy = clusterFastStrategy())
## use plot
plot(model)
## get summary
summary(model)
## print model (a detailed and very long output)
print(model)
## get estimated missing values
missingValues(model)