Given the row vectorized matrix, it computes the vector of discrepancies with respect to a certain rank and its derivative.
Usage
Drank(ga, lev, k, der = FALSE)
Arguments
ga
row vectorized matrix of interaction
lev
vector of the number of row and column categories in the original table (the numbers of rows and columns of the input matrix must be increased by 1)
k
matrix rank
der
to require derivative
Value
fr
vector of discrepancies with respect to the rank
Dfr
derivative of fr
References
Bartolucci, F. and Forcina, A. (2002). Extended RC association models allowing for order restrictions and marginal modeling. Journal of the American Statistical Association, 97, 1192-1199.
# NOT RUN {A = matrix(rnorm(12),4) # matrix the rank of which must be checkeda = as.vector(t(A))
out = Drank(a,c(5,4),1,der=TRUE)
(out$fr)
(out$Dfr)
# }