Data usage when ORE native implementation is employed - Oracle Financial Services Enterprise Modeling application - Specifics to be considered

The data which is prepared from the dataset and variables as explained in the previous section, will be available in this case as a named ORE ore.frame object ('OFSDATASET') when ORE native implementations are used (that is, when the Is ORE implementation used? checkbox is selected). Hence, the R objects(data) must always be accessed through the ore.frame object 'OFSDATASET'.

For instance, if two objects say 'x' and 'y' are used and dataset/ variables are chosen for 'x' and 'y' in the model definition, then the objects 'x' and 'y' should be accessed in the script as 'OFSDATASET$x' and 'OFSDATASET$y' respectively.

An illustration to explain the R and ORE cases is given in the following section:-

For a simple regression model which is entirely scripted in R, that uses a dataset and three variables (DependentVariable, IndependentVariable1 and IndependentVariable2), the user should not select the Is ORE implementation used? checkbox. In this case the R script is as follows:

art.mod<-lm(DependentVariable ~ IndependentVariable1 + IndependentVariable2)

art.summ<-summary(art.mod

coef( art.summ )

art.summ[[ "r.squared" ]]

#do some line plots

new.x.datafrme = data.frame(x=seq(from=range(IndependentVariable1)[ 1

],to=range( IndependentVariable1)[2],length=length(DependentVariable)))

Here the variables chosen for the model are accessed directly in the script as IndependentVariable1, IndependentVariable2, and DependentVariable.

Whereas for a parallel ORE implementation of the same (a dataset and three variables DependentVariable, IndependentVariable1 and IndependentVariable2) using ORE statistical functionalities, user must check the Is ORE implementation used? checkbox and the script is as follows:

art.mod<-ore.lm(DependentVariable ~ IndependentVariable1 +

IndependentVariable2, data=OFSDATASET)

art.summ<-summary(art.mod)

coef( art.summ )

art.summ[[ "r.squared" ]]

#do some line plots

new.x.orefrme = data.frame(x=seq(from=range(OFSDATASET$IndependentVariable1

)[ 1 ],to=range(OFSDATASET$IndependentVariable1)[2],length=

length(OFSDATASET$DependentVariable)))

#perform some operations on the new data.

Here the variables chosen for the model are accessed from OFSDATASET as OFSDATASET$IndependentVariable1, OFSDATASET$IndependentVariable2, OFSDATASET$DependentVariable.