graphe_3Sets {DEMOVA}

Predictions for the external validation set and graph

Description

Calulate the predicted values for the external validation set and trace the graph experimental values vs predicted values for training, test and external validation sets.

Usage

graphe_3Sets(fit, mydata, mynewdata, mynewdata2, n)

Arguments

fit

Multi linear regression between property and selected descriptors (lm object)

mydata

Dataframe containing names and values of response and descriptors

mynewdata

Dataframe containing property and selected descriptors values for the test set

mynewdata2

Dataframe containing property and selected descriptors values for the external validation set

n

Numbers of selected descriptors of the regression (determined using select_MLR)

Value

Rext, Rext2

return a list containing the value of the determination coefficient of the test set and of the external validation set

Graphe_3sets.tiff

Image representing experimental values vs predicted values for the all three sets

Examples

# This function have to be run last!

## "Test_set.csv" should be with the following form
## ID property SelectedDesc1 SelectedDesc2 ... 

# new_nom<-'Test_set.csv'
# newdata<-read.csv(new_nom,header=TRUE , sep=" ")
# mynewdata=newdata[,2:dim[2]]


## "External_set.csv" should be with the following form
## ID property SelectedDesc1 SelectedDesc2 ... 

# new_nom2<-'External_set.csv'
# newdata2<-read.csv(new_nom2,header=TRUE , sep=" ")
# mynewdata2=newdata2[,2:dim[2]]

#graphe_3Sets(fit,mynewdata,mynewdata2,dim(MLR)[2])