## for strategy
strategy <- mixmodStrategy()
print(strategy)
## for Gaussian models
gmodel <- mixmodGaussianModel()
print(gmodel)
## for multinomial models
mmodel <- mixmodMultinomialModel()
print(mmodel)
## for clustering
data(geyser)
xem <- mixmodCluster(geyser, 3)
print(xem)
## for Gaussian parameters
print(xem["bestResult"]["parameters"])
## for discriminant analysis
# start by extract 10 observations from iris data set
iris.partition <- sample(1:nrow(iris), 10)
# then run a mixmodLearn() analysis without those 10 observations
learn <- mixmodLearn(iris[-iris.partition, 1:4], iris$Species[-iris.partition])
# print learn results
print(learn)
# create a MixmodPredict to predict those 10 observations
prediction <- mixmodPredict(
data = iris[iris.partition, 1:4],
classificationRule = learn["bestResult"]
)
# print prediction results
print(prediction)
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