show {Rmixmod} | R Documentation |
Show description of a Rmixmod class to standard output.
## S4 method for signature 'Model' show(object) ## S4 method for signature 'MultinomialParameter' show(object) ## S4 method for signature 'GaussianParameter' show(object) ## S4 method for signature 'CompositeParameter' show(object) ## S4 method for signature 'MixmodResults' show(object) ## S4 method for signature 'Mixmod' show(object) ## S4 method for signature 'Strategy' show(object) ## S4 method for signature 'MixmodCluster' show(object) ## S4 method for signature 'MixmodDAResults' show(object) ## S4 method for signature 'MixmodLearn' show(object) ## S4 method for signature 'MixmodPredict' show(object)
object |
a Rmixmod object: a |
NULL. Prints to standard out.
## for strategy strategy <- mixmodStrategy() show(strategy) ## for Gaussian models gmodel <- mixmodGaussianModel() show(gmodel) ## for multinomial models mmodel <- mixmodMultinomialModel() show(mmodel) ## for clustering data(geyser) xem <- mixmodCluster(geyser,3) show(xem) ## for Gaussian parameters show(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]) # create a MixmodPredict to predict those 10 observations prediction <- mixmodPredict(data=iris[iris.partition,1:4], classificationRule=learn["bestResult"]) # show results show(prediction)