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 [ |
... |
... |
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)
[Package Rmixmod version 2.1.10 Index]