Configuration of Quantities of Interest

Source: R/meta_cfg.R

QoI_cfg is a configuration class for the Quantities of Interest to be generated by the HTE analysis.

Public fields

mcate

A configuration object of type MCATE_cfg of marginal effects to calculate.

pcate

A configuration object of type PCATE_cfg of partial effects to calculate.

vimp

A configuration object of type VIMP_cfg of variable importance to calculate.

diag

A configuration object of type Diagnostics_cfg of model diagnostics to calculate.

ate

Logical flag indicating whether an estimate of the ATE should be returned.

predictions

Logical flag indicating whether estimates of the CATE for every unit should be returned.

Methods

Public methods

Method new()

Create a new QoI_cfg object with specified Quantities of Interest to estimate.

Usage

QoI_cfg$new(
  mcate = NULL,
  pcate = NULL,
  vimp = NULL,
  diag = NULL,
  ate = TRUE,
  predictions = FALSE
)

Arguments

mcate

A configuration object of type MCATE_cfg of marginal effects to calculate.

pcate

A configuration object of type PCATE_cfg of partial effects to calculate.

vimp

A configuration object of type VIMP_cfg of variable importance to calculate.

diag

A configuration object of type Diagnostics_cfg of model diagnostics to calculate.

ate

A logical flag for whether to calculate the Average Treatment Effect (ATE) or not.

predictions

A logical flag for whether to return predictions of the CATE for every unit or not.

Returns

A new Diagnostics_cfg object.

Examples

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 clone()

The objects of this class are cloneable with this method.

Usage

QoI_cfg$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

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
)
#> <QoI_cfg>
#>   Public:
#>     ate: TRUE
#>     clone: function (deep = FALSE) 
#>     diag: Diagnostics_cfg, R6
#>     initialize: function (mcate = NULL, pcate = NULL, vimp = NULL, diag = NULL, 
#>     mcate: MCATE_cfg, R6
#>     pcate: PCATE_cfg, R6
#>     predictions: FALSE
#>     vimp: VIMP_cfg, R6

## ------------------------------------------------
## 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
)
#> <QoI_cfg>
#>   Public:
#>     ate: TRUE
#>     clone: function (deep = FALSE) 
#>     diag: Diagnostics_cfg, R6
#>     initialize: function (mcate = NULL, pcate = NULL, vimp = NULL, diag = NULL, 
#>     mcate: MCATE_cfg, R6
#>     pcate: PCATE_cfg, R6
#>     predictions: FALSE
#>     vimp: VIMP_cfg, R6