Sobol method quantifies the contributions of input variance to output variance. For example, given a model with two inputs and one output, one might find that 70% of the output variance is caused by the variance in the first input, 20% by the variance in the second, and 10% due to interactions between the two.
In R package "Sensitivity", a sample script of implementing Sobol method as below
library("sensitivity") n<-1000 X1<-data.frame(matrix(runif(8*n), nrow=n)) X2<-data.frame(matrix(runif(8*n), nrow=n)) sa<-sobol2002(model=NULL, X1, X2, nboot=10)
I thought X1 is output matrix, and X2 is input matrix, so I could run analysis on different dimension of X1 and X2. but the function requires the same dimension of X1 and X2.
May I get intuitive explanation on what are X1 and X2?