Aliases: defaultPrior
Keywords: cluster
### ** Examples # default prior irisBIC <- mclustBIC(iris[,-5], prior = priorControl())
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Warning in mclustBIC(iris[, -5], prior = priorControl()): The presence of BIC values equal to NA is likely due to one or more of the mixture proportions being estimated as zero, so that the model estimated reduces to one with a smaller number of components.
summary(irisBIC, iris[,-5])
Best BIC values:
VEV,2 VEV,3 VVV,2
BIC -580.8136 -587.403843 -592.51283
BIC diff 0.0000 -6.590289 -11.69928
Classification table for model (VEV,2):
1 2
50 100
# equivalent to previous example irisBIC <- mclustBIC(iris[,-5], prior = priorControl(functionName = "defaultPrior"))
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Warning in mclustBIC(iris[, -5], prior = priorControl(functionName = "defaultPrior")): The presence of BIC values equal to NA is likely due to one or more of the mixture proportions being estimated as zero, so that the model estimated reduces to one with a smaller number of components.
summary(irisBIC, iris[,-5])
Best BIC values:
VEV,2 VEV,3 VVV,2
BIC -580.8136 -587.403843 -592.51283
BIC diff 0.0000 -6.590289 -11.69928
Classification table for model (VEV,2):
1 2
50 100
# no prior on the mean; default prior on variance irisBIC <- mclustBIC(iris[,-5], prior = priorControl(shrinkage = 0))
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Warning in mclustBIC(iris[, -5], prior = priorControl(shrinkage = 0)): The presence of BIC values equal to NA is likely due to one or more of the mixture proportions being estimated as zero, so that the model estimated reduces to one with a smaller number of components.
summary(irisBIC, iris[,-5])
Best BIC values:
VEV,2 VEV,3 VVV,2
BIC -580.2861 -586.792195 -592.07132
BIC diff 0.0000 -6.506112 -11.78523
Classification table for model (VEV,2):
1 2
50 100
# equivalent to previous example irisBIC <- mclustBIC(iris[,-5], prior = priorControl(functionName="defaultPrior", shrinkage=0))
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Warning in mclustBIC(iris[, -5], prior = priorControl(functionName = "defaultPrior", : The presence of BIC values equal to NA is likely due to one or more of the mixture proportions being estimated as zero, so that the model estimated reduces to one with a smaller number of components.
summary(irisBIC, iris[,-5])
Best BIC values:
VEV,2 VEV,3 VVV,2
BIC -580.2861 -586.792195 -592.07132
BIC diff 0.0000 -6.506112 -11.78523
Classification table for model (VEV,2):
1 2
50 100
defaultPrior( iris[-5], G = 3, modelName = "VVV")
$shrinkage
[1] 0.01
$mean
Sepal.Length Sepal.Width Petal.Length Petal.Width
5.843333 3.057333 3.758000 1.199333
$dof
[1] 6
$scale
Sepal.Length Sepal.Width Petal.Length Petal.Width
Sepal.Length 0.39588533 -0.02449928 0.7357264 0.29806902
Sepal.Width -0.02449928 0.10968467 -0.1903272 -0.07022853
Petal.Length 0.73572636 -0.19032720 1.7991839 0.74802043
Petal.Width 0.29806902 -0.07022853 0.7480204 0.33544412