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

Description Usage Arguments Value Author(s) Examples

View source: R/MixmodPredict.R

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

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  mixmodPredict(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.

...

internal

Value

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

Author(s)

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

Examples

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# 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="")

Rmixmod documentation built on May 30, 2017, 3:31 a.m.

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