print {Rmixmod} | R Documentation |
Print a Rmixmod class to standard output.
## 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, ...)
x |
a Rmixmod object: a |
... |
further arguments passed to or from other methods |
NULL. Prints to standard out.
## 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)