Description Usage Arguments Value Author(s) Examples
View source: R/ClusterMixedData.R
This function computes the optimal mixture model for mixed data according
to the criterion
among the number of clusters given in
nbCluster
using the strategy specified in [strategy
].
1 2 | clusterMixedData(data, models, nbCluster = 2,
strategy = clusterStrategy(), criterion = "ICL", nbCore = 1)
|
data |
[ |
models |
a [ |
nbCluster |
[ |
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 processors to use (default is 1, 0 for all). |
An instance of the [ClusterMixedDataModel
] class.
Serge Iovleff
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 | ## A quantitative example with the heart disease data set
data(HeartDisease.cat)
data(HeartDisease.cont)
## with default values
ldata = list(HeartDisease.cat, HeartDisease.cont);
models = c("categorical_pk_pjk","gaussian_pk_sjk")
model <- clusterMixedData(ldata, models, nbCluster=2:5, strategy = clusterFastStrategy())
## get summary
summary(model)
## get estimated missing values
missingValues(model)
## Not run:
## print model
print(model)
## use graphics functions
plot(model)
## End(Not run)
|
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