Description Usage Arguments Details Value Note Author(s) See Also Examples
Computes the criterion value of a design w
in the model determined by the matrix Fx
of all regressors.
1 |
Fx |
the |
w |
a non-negative vector of length |
crit |
the criterion; possible values are |
h |
a non-zero vector of length |
echo |
Print the call of the function? |
The package works with optimality criteria as information functions, i.e., the criteria are concave, positive homogeneous and upper semicontinuous on the set of all non-negative definite matrices. The criteria are normalized such that they assign the value of 1 to any design with information matrix equal to the identity matrix.
A non-negative number corresponding to the criterion value.
Since the criteria are positive homogeneous, the relative efficiency of two designs is just the ratio of their criterion values.
Radoslav Harman, Lenka Filova
1 2 3 4 5 6 7 8 9 10 11 12 13 14 | # The Fx matrix for the spring balance weighing model with 6 weighed items.
Fx <- Fx_cube(~x1 + x2 + x3 + x4 + x5 + x6 - 1, lower = rep(0, 6), n.levels = rep(2, 6))
# Criteria of the design of size 15 that weighs each pair of items exactly once.
w2 <- rep(0, 64); w2[apply(Fx, 1, sum) == 2] <- 1
optcrit(Fx, w2, crit = "D")
optcrit(Fx, w2, crit = "A")
optcrit(Fx, w2, crit = "I")
# Criteria for the design of size 15 that weighs each quadruple of items exactly once.
w4 <- rep(0, 64); w4[apply(Fx, 1, sum) == 4] <- 1
optcrit(Fx, w4, crit = "D")
optcrit(Fx, w4, crit = "A")
optcrit(Fx, w4, crit = "I")
|
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.