Estimate ecological quantities — ei

Produces estimates of overall conditional means from a fitted ecological inference model or Riesz representer. If both a regression model and a Riesz representer are provided, a debiased machine learning (DML) estimate is produced.

Usage

ei_est(
  regr = NULL,
  riesz = NULL,
  data,
  total,
  subset = NULL,
  outcome = NULL,
  conf_level = FALSE
)

# S3 method for class 'ei_est'
as.matrix(x, which = "estimate", ...)

# S3 method for class 'ei_est'
vcov(object, ...)

# S3 method for class 'ei_est'
nobs(object, ...)

Arguments

regr: A fitted regression model, from ei_ridge(), or another kind of regression model wrapped with ei_wrap_model(). If riesz is not provided and regr is an ei_riesz() object, then riesz will be set to the value of regr and regr will be set to NULL. This is so users can call this function as ei_est(<riesz>, data = <data>).
riesz: A fitted Riesz representer, from ei_riesz(), or a matrix of Riesz weights
data: The data frame, matrix, or ei_spec object that was used to fit the regression or Riesz representer.
total: <tidy-select> A variable containing the total number of observations in each aggregate unit. For example, the column containing the total number of voters. Required if data is not an ei_spec() object and riesz is not provided.
subset: <data-masking> An optional indexing vector describing the subset of units over which to calculate estimates.
outcome: <data-masking> A vector or matrix of outcome variables. Only required if both riesz is provided alone (without regr) and data is not an ei_spec object.
conf_level: A numeric specifying the level for confidence intervals. If FALSE (the default), no confidence intervals are calculated. Standard errors are always returned.
x, object: An object of class ei_est
which: Which column of ei_est to convert to a matrix. For example, pass which="std.error" to return standard errors instead of estimates. Partial matching supported.
...: Additional arguments (ignored)

Value

A data frame with estimates. It has class ei_est, supporting several methods, and two additional attributes: vcov, containing the estimated covariance matrix for the estimates, and n, containing the number of aggregate units used in estimation (the number of rows in data).

Methods (by generic)

as.matrix(ei_est): Format estimates, standard errors, or other columns as a matrix.
vcov(ei_est): Extract full covariance matrix of estimates
nobs(ei_est): Extract number of units covered by estimates

Examples

data(elec_1968)

spec = ei_spec(elec_1968, vap_white:vap_other, pres_dem_hum:pres_abs,
               total = pres_total, covariates = c(state, pop_urban, farm))

m = ei_ridge(spec)
rr = ei_riesz(spec, penalty = m$penalty)

ei_est(regr = m, data = spec, conf_level = 0.95) # Plug-in estimate
#> # A tibble: 12 × 6
#>    predictor outcome      estimate std.error  conf.low conf.high
#>    <chr>     <chr>           <dbl>     <dbl>     <dbl>     <dbl>
#>  1 vap_white pres_dem_hum  0.230    0.0222    0.186      0.273  
#>  2 vap_black pres_dem_hum  0.588    0.0457    0.499      0.678  
#>  3 vap_other pres_dem_hum  1.55     0.181     1.20       1.91   
#>  4 vap_white pres_rep_nix  0.382    0.0284    0.326      0.438  
#>  5 vap_black pres_rep_nix -0.0775   0.0165   -0.110     -0.0452 
#>  6 vap_other pres_rep_nix  0.494    0.0722    0.353      0.636  
#>  7 vap_white pres_ind_wal  0.387    0.0251    0.338      0.436  
#>  8 vap_black pres_ind_wal  0.488    0.0446    0.401      0.576  
#>  9 vap_other pres_ind_wal -1.05     0.131    -1.30      -0.790  
#> 10 vap_white pres_abs      0.00152  0.000251  0.00102    0.00201
#> 11 vap_black pres_abs      0.00109  0.000227  0.000641   0.00153
#> 12 vap_other pres_abs      0.00117  0.000471  0.000250   0.00210
ei_est(riesz = rr, data = spec) # Weighted (Riesz) estimate
#> # A tibble: 12 × 4
#>    predictor outcome      estimate std.error
#>    <chr>     <chr>           <dbl>     <dbl>
#>  1 vap_white pres_dem_hum  0.230    0.0224  
#>  2 vap_black pres_dem_hum  0.588    0.0948  
#>  3 vap_other pres_dem_hum  1.55     1.98    
#>  4 vap_white pres_rep_nix  0.382    0.0237  
#>  5 vap_black pres_rep_nix -0.0775   0.0695  
#>  6 vap_other pres_rep_nix  0.494    1.49    
#>  7 vap_white pres_ind_wal  0.387    0.0286  
#>  8 vap_black pres_ind_wal  0.488    0.115   
#>  9 vap_other pres_ind_wal -1.05     1.98    
#> 10 vap_white pres_abs      0.00152  0.000252
#> 11 vap_black pres_abs      0.00109  0.000840
#> 12 vap_other pres_abs      0.00117  0.0134  
est = ei_est(regr = m, riesz = rr, data = spec) # Double/debiased ML estimate
as.matrix(est)
#>            outcome
#> predictor   pres_dem_hum pres_rep_nix pres_ind_wal     pres_abs
#>   vap_white    0.2296810    0.3814135    0.3873821 0.0015233731
#>   vap_black    0.5856278   -0.0768011    0.4901049 0.0010683650
#>   vap_other    1.6370000    0.5979725   -1.2351002 0.0001277371
as.matrix(est, "std.error")
#>            outcome
#> predictor   pres_dem_hum pres_rep_nix pres_ind_wal     pres_abs
#>   vap_white   0.02346882   0.03061363   0.02459608 0.0002801522
#>   vap_black   0.04997948   0.02670867   0.05056769 0.0005101309
#>   vap_other   0.47024050   0.33462988   0.55488723 0.0058597394
vcov(est)[1:4, 1:4]
#>                        vap_white:pres_dem_hum vap_black:pres_dem_hum
#> vap_white:pres_dem_hum           0.0005507857           0.0004144159
#> vap_black:pres_dem_hum           0.0004144159           0.0024979486
#> vap_other:pres_dem_hum           0.0021912165           0.0056012709
#> vap_white:pres_rep_nix           0.0006103741           0.0007945676
#>                        vap_other:pres_dem_hum vap_white:pres_rep_nix
#> vap_white:pres_dem_hum            0.002191216           0.0006103741
#> vap_black:pres_dem_hum            0.005601271           0.0007945676
#> vap_other:pres_dem_hum            0.221126131           0.0045939440
#> vap_white:pres_rep_nix            0.004593944           0.0009371944

est = ei_est(m, rr, data = spec, subset = (state == "Alabama"))
as.matrix(est)
#>            outcome
#> predictor   pres_dem_hum pres_rep_nix pres_ind_wal    pres_abs
#>   vap_white  0.006649839   0.16292434    0.8158650 0.014560791
#>   vap_black  0.733211508  -0.04036185    0.2995285 0.007621831
#>   vap_other  1.803101753   0.16471677   -0.9702721 0.002453595
nobs(est)
#> [1] 67