Tag Archives: AppliedPredictiveModeling

concrete data from the applied predictive modeling library

Variable names in concrete data:

Cement

BlastFurnaceSlag

FlyAsh

Water

Superplasticizer

CoarseAggregate

FineAggregate

Age

CompressiveStrength

 

Concrete data Summary

summary(concrete)

20150419-data(concrete)_summary(concrete)

 

Mixtures data Summary

summary(mixtures)

20150419-data(concrete)-mixtures_summary(mixtures)

Mixtures data Feature Plot

requires library(caret)

We could list out all the variable names as shown below:

featurePlot(x=mixtures[,c(“Cement”, “BlastFurnaceSlag”, “FlyAsh”, “Water”, “Superplasticizer”, “CoarseAggregate”, “FineAggregate”, “Age”)],y=mixtures$CompressiveStrength, plot=”pairs”)

or simplify a bit:

names <- colnames(mixtures)

names <- names[-length(names)]

then plot:

featurePlot(x = mixtures[, names], y = mixtures$CompressiveStrength, plot=“pairs”)

 

20150419-data(concrete)-mixtures_featurePlot

R: Get AppliedPredictiveModeling Library

install.packages(“AppliedPredictiveModeling”)

Verify it installed correctly; try loading the library:

library(AppliedPredictiveModeling)

 

If you also see “also installing the dependency ‘CORElearn’” in the console is because Applied Predictive Modeling requires CORElearn which should install automatically.  If it already is installed you won’t see that message.