print.copValidation {optiSolve} | R Documentation |
Print Validation of a Solution
Description
Print the validation results for the solution of an optimization problem.
Usage
## S3 method for class 'copValidation'
print(x, ...)
Arguments
x |
The result of function validate. |
... |
Unused additional arguments. |
Details
Print the validation results for the solution of an optimization problem.
Value
A list of class copValidation
(invisible) with components:
summary |
Data frame containing one row for each constraint with the value of the constraint in column |
info |
Data frame with component |
var |
Data frame with the values of the objective function and constraints at the optimum. |
obj.fun |
Named numeric value with value and name of the objective function at the optimum. |
See Also
The main function for solving constrained programming problems is solvecop.
Examples
### Quadratic programming with linear constraints ###
### Example from animal breeding ###
### where the mean kinship in the offspring is minized ###
data(phenotype)
data(myQ)
A <- t(model.matrix(~Sex+BV-1, data=phenotype))
rownames(A) <- c("male.cont","female.cont", "Breeding.Value")
val <- c(0.5, 0.5, 0.40)
dir <- c("==","==",">=")
mycop <- cop(f = quadfun(Q=myQ, d=0.001, name="Kinship", id=rownames(myQ)),
lb = lbcon(0, id=phenotype$Indiv),
ub = ubcon(NA, id=phenotype$Indiv),
lc = lincon(A=A, dir=dir, val=val, id=phenotype$Indiv))
res <- solvecop(mycop, solver="cccp", quiet=FALSE, trace=FALSE)
head(res$x)
Evaluation <- validate(mycop, res, quiet=TRUE)
print(Evaluation)
# valid solver status
# TRUE cccp optimal
#
# Variable Value Bound OK?
# ---------------------------------------
# Kinship 0.0322 min :
# ---------------------------------------
# lower bounds all x >= lb : TRUE
# male.cont 0.5 == 0.5 : TRUE
# female.cont 0.5 == 0.5 : TRUE
# Breeding.Value 0.4 >= 0.4 : TRUE
# ---------------------------------------