Rmixmod (version 2.1.2)

oldmixmodPredict: Create an instance of the ['>MixmodPredict] class

Description

This function computes the second step of a discriminant analysis. The aim of this step is to assign remaining observations to one of the groups.

Usage

oldmixmodPredict(data, classificationRule, ...)

Arguments

data

matrix or data frame containing quantitative,qualitative or composite data. Rows correspond to observations and columns correspond to variables.

classificationRule

a ['>MixmodResults] object which contains the classification rule computed in the mixmodLearn() or mixmodCluster() step.

...

...

Value

Returns an instance of the ['>MixmodPredict] class which contains predicted partition and probabilities.

Examples

# NOT RUN {
  # start by extract 10 observations from iris data set
  remaining.obs<-sample(1:nrow(iris),10)
  # then run a mixmodLearn() analysis without those 10 observations
  learn<-mixmodLearn(iris[-remaining.obs,1:4], iris$Species[-remaining.obs])
  # create a MixmodPredict to predict those 10 observations
  prediction <- mixmodPredict(data=iris[remaining.obs,1:4], classificationRule=learn["bestResult"])
  # show results
  prediction
  # compare prediction with real results
  paste("accuracy= ",mean(as.integer(iris$Species[remaining.obs]) == prediction["partition"])*100
        ,"%",sep="")

  ## A composite example with a heterogeneous data set
  data(heterodatatrain)
  ## Learning with training data
  learn <- mixmodLearn(heterodatatrain[-1],knownLabels=heterodatatrain$V1)
  ## Prediction on the testing data
  data(heterodatatest)
  prediction <- mixmodPredict(heterodatatest[-1],learn["bestResult"])
  # compare prediction with real results
  paste("accuracy= ",mean(heterodatatest$V1 == prediction["partition"])*100,"%",sep="")

# }