| QoI_cfg | R Documentation |
QoI_cfg is a configuration class for the Quantities of Interest to be
generated by the HTE analysis.
mcateA configuration object of type MCATE_cfg of
marginal effects to calculate.
pcateA configuration object of type PCATE_cfg of
partial effects to calculate.
vimpA configuration object of type VIMP_cfg of
variable importance to calculate.
diagA configuration object of type Diagnostics_cfg of
model diagnostics to calculate.
ateLogical flag indicating whether an estimate of the ATE should be returned.
predictionsLogical flag indicating whether estimates of the CATE for every unit should be returned.
new()Create a new QoI_cfg object with specified Quantities of Interest
to estimate.
QoI_cfg$new( mcate = NULL, pcate = NULL, vimp = NULL, diag = NULL, ate = TRUE, predictions = FALSE )
mcateA configuration object of type MCATE_cfg of marginal
effects to calculate.
pcateA configuration object of type PCATE_cfg of partial
effects to calculate.
vimpA configuration object of type VIMP_cfg of variable
importance to calculate.
diagA configuration object of type Diagnostics_cfg of
model diagnostics to calculate.
ateA logical flag for whether to calculate the Average Treatment Effect (ATE) or not.
predictionsA logical flag for whether to return predictions of the CATE for every unit or not.
A new Diagnostics_cfg object.
mcate_cfg <- MCATE_cfg$new(cfgs = list(x1 = KernelSmooth_cfg$new(neval = 100)))
pcate_cfg <- PCATE_cfg$new(
cfgs = list(x1 = KernelSmooth_cfg$new(neval = 100)),
model_covariates = c("x1", "x2", "x3"),
num_mc_samples = list(x1 = 100)
)
vimp_cfg <- VIMP_cfg$new()
diag_cfg <- Diagnostics_cfg$new(
outcome = c("SL_risk", "SL_coefs", "MSE"),
ps = c("SL_risk", "SL_coefs", "AUC")
)
QoI_cfg$new(
mcate = mcate_cfg,
pcate = pcate_cfg,
vimp = vimp_cfg,
diag = diag_cfg
)
clone()The objects of this class are cloneable with this method.
QoI_cfg$clone(deep = FALSE)
deepWhether to make a deep clone.
mcate_cfg <- MCATE_cfg$new(cfgs = list(x1 = KernelSmooth_cfg$new(neval = 100)))
pcate_cfg <- PCATE_cfg$new(
cfgs = list(x1 = KernelSmooth_cfg$new(neval = 100)),
model_covariates = c("x1", "x2", "x3"),
num_mc_samples = list(x1 = 100)
)
vimp_cfg <- VIMP_cfg$new()
diag_cfg <- Diagnostics_cfg$new(
outcome = c("SL_risk", "SL_coefs", "MSE"),
ps = c("SL_risk", "SL_coefs", "AUC")
)
QoI_cfg$new(
mcate = mcate_cfg,
pcate = pcate_cfg,
vimp = vimp_cfg,
diag = diag_cfg
)
## ------------------------------------------------
## Method `QoI_cfg$new`
## ------------------------------------------------
mcate_cfg <- MCATE_cfg$new(cfgs = list(x1 = KernelSmooth_cfg$new(neval = 100)))
pcate_cfg <- PCATE_cfg$new(
cfgs = list(x1 = KernelSmooth_cfg$new(neval = 100)),
model_covariates = c("x1", "x2", "x3"),
num_mc_samples = list(x1 = 100)
)
vimp_cfg <- VIMP_cfg$new()
diag_cfg <- Diagnostics_cfg$new(
outcome = c("SL_risk", "SL_coefs", "MSE"),
ps = c("SL_risk", "SL_coefs", "AUC")
)
QoI_cfg$new(
mcate = mcate_cfg,
pcate = pcate_cfg,
vimp = vimp_cfg,
diag = diag_cfg
)
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