exp.loss: Break down expected future optimality into two components: 1)...

In mst1g15/biasedcoin:

Description

Break down expected future optimality into two components: 1) assuming that the current response is 0 2) assuming that the current response is 1 Find the weighted average of the two cases

Usage

## S3 method for class 'loss'
exp(z.now, t.now, zp, N, design, int, lossfunc, beta, y, bayes,
  dyn = NULL, ...)

Arguments

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
int	set to NULL if there are no interactions, set to T of there are interactions
lossfunc	the objective function to minimize
beta	estimate of the regression coefficients
y	responses that have been observed up until the current unit
bayes	set to T if bayesglm is used instead of glm. Default prior assumed.
dyn	set to T if there is a dynamic covariate
...	further arguments to be passed to <lossfunc>

Value

expected value of objective function one step ahead in the future

mst1g15/biasedcoin documentation built on Nov. 26, 2019, 4:01 a.m.