oldmixmodPredict {Rmixmod} | R Documentation |
MixmodPredict
] classThis function computes the second step of a discriminant analysis. The aim of this step is to assign remaining observations to one of the groups.
oldmixmodPredict(data, classificationRule, ...)
data |
matrix or data frame containing quantitative,qualitative or composite data. Rows correspond to observations and columns correspond to variables. |
classificationRule |
a [ |
... |
... |
Returns an instance of the [MixmodPredict
] class which contains predicted partition and probabilities.
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
# 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="")