Rmixmod (version 2.1.5)

print: Print a Rmixmod class to standard output.

Description

Print a Rmixmod class to standard output.

Usage

# S4 method for Model
print(x, ...)
# S4 method for MultinomialParameter
print(x, ...)
# S4 method for GaussianParameter
print(x, ...)
# S4 method for CompositeParameter
print(x, ...)
# S4 method for MixmodResults
print(x, ...)
# S4 method for Mixmod
print(x, ...)
# S4 method for Strategy
print(x, ...)
# S4 method for MixmodCluster
print(x, ...)
# S4 method for MixmodDAResults
print(x, ...)
# S4 method for MixmodLearn
print(x, ...)
# S4 method for MixmodPredict
print(x, ...)

Arguments

x

a Rmixmod object: a '>Strategy, a '>Model, a '>GaussianParameter, a '>MultinomialParameter, a '>MixmodResults, a '>MixmodCluster, a '>MixmodLearn or a '>MixmodPredict.

...

further arguments passed to or from other methods

Value

NULL. Prints to standard out.

See Also

print

Examples

# NOT RUN {
  ## 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)

# }