mase is still under development. Please use at your own risk!
mase contains a collection of model-assisted generalized regression estimators (the post-stratification estimator, the ratio estimator, the linear and logistic regression estimator, the elastic net regression estimator, and the regression tree estimator) for finite population estimation of a total or mean from a single stage, unequal probability without replacement design. It also contains the Horvitz-Thompson estimator and several variance estimators.
You can install mase from github with:
Here’s an example of fitting the Horvitz-Thompson estimator:
library(mase)
## Estimates the mean and total of the api00 variable using the apisrs dataset in the survey package
library(survey)
#> Loading required package: grid
#> Loading required package: Matrix
#> Loading required package: survival
#>
#> Attaching package: 'survey'
#> The following object is masked from 'package:graphics':
#>
#> dotchart
data(api)
horvitzThompson(y = apisrs$api00, pi = apisrs$pw^(-1), var_est = TRUE, var_method = "lin_HTSRS")
#> $pop_total
#> [1] 4066887
#>
#> $pop_mean
#> [1] 656.585
#>
#> $pop_total_var
#> [1] 3282462447
#>
#> $pop_mean_var
#> [1] 85.55736