Description Usage Arguments Value
View source: R/linear nonmyop.R View source: R/linear_nonmyop.R
Assuming the currrent response, future covariate value and future treatment, Calculate optimality if horizon is 1. If not, iterate back to exp.loss function.
1 2 | future.loss.k(z.next, t.next, z.now, t.now, zp, N, design, int, lossfunc,
dyn = NULL, ...)
|
z.next |
vector of covariate values for future unit |
t.next |
treatment of future unit |
z.now |
vector of covariate values for current unit |
t.now |
treatment of current unit |
zp |
vector of probabilities for each level of covariate z (needs to in the same order as all.z) |
N |
natural number greater than 0 for horizon |
design |
design matrix constructed for all units up until the current unit |
int |
set to NULL if there are no interactions, set to T of there are interactions |
lossfunc |
the objective function to minimize |
dyn |
set to NULL of there are no dynamic covariates, set to a real number if there are dynamic covariates |
... |
further arguments to be passed to <lossfunc> |
value of objective function one step ahead in the future, assuming z.next and t.next
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