R: Create an instance of the ['MixmodPredict'] class

oldmixmodPredict {Rmixmod} | R Documentation

## 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.

### Author(s)

Florent Langrognet and Remi Lebret and Christian Poli ans Serge Iovleff, with contributions from C. Biernacki and G. Celeux and G. Govaert contact@mixmod.org

### Examples

      # 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="")

[Package Rmixmod version 2.1.2.2 Index]