Description Usage Arguments Value Author(s) Examples
View source: R/ClusterDiagGaussian.R
This function computes the optimal diagonal Gaussian 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
.
1 2 3 | clusterDiagGaussian(data, nbCluster = 2,
models = clusterDiagGaussianNames(), strategy = clusterStrategy(),
criterion = "ICL", nbCore = 1)
|
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 processors to use (default is 1, 0 for all). |
An instance of the [ClusterDiagGaussian
] class.
Serge Iovleff
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 | ## A quantitative example with the famous geyser data set
data(geyser)
## add 10 missing values as random
x = as.matrix(geyser); n <- nrow(x); p <- ncol(x);
indexes <- matrix(c(round(runif(5,1,n)), round(runif(5,1,p))), ncol=2);
x[indexes] <- NA;
## estimate model (using fast strategy, results may be misleading)
model <- clusterDiagGaussian( data=x, nbCluster=2:3
, models=c( "gaussian_pk_sjk")
, strategy = clusterFastStrategy()
)
## use graphics functions
## Not run:
plot(model)
## End(Not run)
## get summary
summary(model)
## print model
## Not run:
print(model)
## End(Not run)
## get estimated missing values
missingValues(model)
|
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