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Construct a Multi-Resample DDML Object

Source: R/ddml_rep.R
ddml_rep.Rd

Validates a list of ddml fits and stamps class "ddml_rep" for multi-resample aggregation.

Usage

ddml_rep(fits)

# S3 method for class 'ddml_rep'
print(x, ...)

Arguments

fits

A list of at least 2 objects inheriting from class "ddml". All fits must share the same primary class, coefficient names, ensemble type, and number of observations.

x

A ddml_rep object.

...

Currently unused.

Value

An object of class c("ddml_rep", "ral_rep") with fields:

fits

List of ddml objects.

nresamples

Number of resamples.

model_type

Primary class of the fits.

coef_names

Coefficient names.

ensemble_type

Ensemble types.

nobs

Number of observations.

sample_folds

Number of cross-fitting folds.

shortstack

Logical, whether short-stacking was used.

See also

ddml_replicate()

Other ddml replication: ddml_replicate()

Examples

# \donttest{
y = AE98[, "worked"]
D = AE98[, "morekids"]
X = AE98[, c("age","agefst","black","hisp","othrace")]
fits = lapply(1:3, function(r) {
  ddml_plm(y, D, X,
           learners = list(what = ols),
           sample_folds = 2, silent = TRUE)
})
reps = ddml_rep(fits)
summary(reps)
#> DDML estimation: Partially Linear Model 
#> Obs: 5000   Folds: 2   Resamples: 3   Aggregation: median
#> 
#>              Estimate Std. Error z value Pr(>|z|)    
#> D1          -1.54e-01   1.48e-02  -10.43   <2e-16 ***
#> (Intercept) -9.98e-05   6.91e-03   -0.01     0.99    
#> ---
#> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
# }