Description Usage Arguments Value
View source: R/lopt nonmyop.R View source: R/lopt_nonmyop.R
Assuming a current response, break down the expected future optimality by cases for every combination of: 1) future possible covariate 2) future possible treatment Find a weighted average across all these cases
1 2 | cr.future.y(y.now, z.now, t.now, zp, N, design, k, beta, y, cr, wr, prior.scale,
bayes, dyn = NULL, Nprobs = NULL, ...)
|
y.now |
scalar for response of current 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 below) |
N |
natural number greater than 0 for horizon |
design |
design matrix constructed for all units up until the current unit |
k |
number of covariates |
beta |
vector of current estimates for regression coefficients |
y |
vectir of responses generated up until current unit |
cr |
matrix of contrasts |
wr |
matrix of weights |
prior.scale |
prior scale parameter |
bayes |
set to T if bayesglm is used instead of glm. Default prior assumed |
dyn |
set to NULL of there are no dynamic covariates, set to T if there are dynamic covariates |
Nprobs |
a counter to be used if there are dynamic covariates |
... |
further arguments to be passed to <lossfunc> |
expected value of objective function assuming current response
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