Rmixmod (version 2.1.10)

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, ...)

Value

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

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.

...

...

Author

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

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)