VarCor: VarCor Selection and Dataset Partition
In MLRMPA: A package for Multilinear Regression Model Population Analysis

Firstly, select the eligible variales according to the values of var.lower and var.upper. Secondly, divide activity as well as corresponding descriptor into training set and test set with personal setting.

1	VarCor(tst, activity, descriptor, deleted_descriptor, var.lower, var.upper, xy.cor)

`tst`	a numeric vector of the serial number of test set
`activity`	a 1*n dataframe of activity (n is the number of samples)
`descriptor`	a n*P dataframe of corresponding descriptors (n is the number of samples, P is the number of descriptors)
`deleted_descriptor`	a 1*M dataframe of needed to delete descriptors which contains insignificant and noisy variables (M is the number of deleted descriptors)
`var.lower`	a numeric in the varance selection as the lower limit value
`var.upper`	a numeric in the varance selection as the upper limit value
`xy.cor`	a numeric in the correlation selection

`expr.tr`	a dataframe with the experimental data of training set
`expr.tst`	a dataframe with the experimental data of test set
`dscrp.all`	a dataframe with the descriptors both of training set and test set
`dscrp.tr`	a dataframe with the descriptors of training set
`dscrp.tst`	a dataframe with the descriptors of test set
`VarCordim`	the dim of variance selection and correlation selection