stepwise.PIC {NPRED} | R Documentation |
Calculate stepwise PIC
stepwise.PIC(x, py, alpha = 0.1)
x |
A vector of response. |
py |
A matrix containing possible predictors of x. |
alpha |
The significance level used to judge whether the sample estimate in Equation \hat{PIC} = sqrt(1-exp(-2\hat{PI}) is significant or not. A default alpha value is 0.1. |
A list of 2 elements: the column numbers of the meaningful predictors (cpy), and partial informational correlation (cpyPIC).
Sharma, A., Mehrotra, R., 2014. An information theoretic alternative to model a natural system using observational information alone. Water Resources Research, 50(1): 650-660.
data(data1) # AR9 model x(i)=0.3*x(i-1)-0.6*x(i-4)-0.5*x(i-9)+eps x<-data1[,1] # response py<-data1[,-1] # possible predictors stepwise.PIC(x,py) data(data2) # AR4 model: x(i)=0.6*x(i-1)-0.4*x(i-4)+eps x<-data2[,1] # response py<-data2[,-1] # possible predictors stepwise.PIC(x,py) data(data3) # AR1 model x(i)=0.9*x(i-1)+0.866*eps x<-data3[,1] # response py<-data3[,-1] # possible predictors stepwise.PIC(x,py)