print-methods: Print a Rmixmod class to standard output.

Description Usage Arguments Value See Also Examples

Description

Print a Rmixmod class to standard output.

Usage

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## S4 method for signature 'Model'
print(x, ...)

## S4 method for signature 'MultinomialParameter'
print(x, ...)

## S4 method for signature 'GaussianParameter'
print(x, ...)

## S4 method for signature 'CompositeParameter'
print(x, ...)

## S4 method for signature 'MixmodResults'
print(x, ...)

## S4 method for signature 'Mixmod'
print(x, ...)

## S4 method for signature 'Strategy'
print(x, ...)

## S4 method for signature 'MixmodCluster'
print(x, ...)

## S4 method for signature 'MixmodDAResults'
print(x, ...)

## S4 method for signature 'MixmodLearn'
print(x, ...)

## S4 method for signature '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

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  ## 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)

Rmixmod documentation built on June 26, 2018, 5:02 p.m.