forward_cost: Returns MDS stress for the forward algorithm as defined in...
In NathanWycoff/mds.methods:

Description Usage Arguments Value

View source: R/forward_methods.R

Returns MDS stress for the forward algorithm as defined in equation 8.15 of Borg and Groenen, with all weights (w_i,j) equal to 1 (do not confuse these with the weights on dimensions defined in this package).

1	forward_cost(low_d, high_d_dist, std = TRUE)

`low_d`	The low_d solution, an n by 2 matrix, the cost of which is to be evaluated.
`high_d_dist`	The distance matrix, n by n, of the high dimensional data of which we seek a low D approximation.
`std`	Boolean, should stress be standardized? This is accomplished by dividing stress by the sum of squared high D distance