README.md

In mase: Model-Assisted Survey Estimators

Development Mode

mase is still under development. Please use at your own risk!

mase

mase contains a collection of model-assisted generalized regression estimators for finite population estimation of a total or mean from a single stage, unequal probability without replacement design. It also contains several variance estimators.

The available estimators are currently:

• generalized regression: greg()
• hotvitz-thompson: horvitzThompson()
• post-stratification: postStrat()
• elastic net generalized regression: gregElasticNet()
• regression tree: gregTree()
• modified generalized regression: modifiedGreg()
• ratio estimator: ratioEstimator()
• estimate of a ratio estimator: ratio()

Installation

Install the latest CRAN release with:

install.packages("mase")
library(mase)

You can also install the developmental version of mase from GitHub with:

# install.packages("devtools")
devtools::install_github("mcconvil/mase")

Example

Horvitz-Thompson

Here’s an example of fitting the Horvitz-Thompson estimator using Forestry data in Idaho. The data comes from the Forestry Inventory & Analysis department (FIA).

library(mase)
library(dplyr)

data(IdahoSamp)
data(IdahoPop)

samp <- filter(IdahoSamp, COUNTYFIPS == 16055) 
pop <- filter(IdahoPop, COUNTYFIPS == 16055) 

horvitzThompson(y = samp$BA_TPA_ADJ,
                N = pop$npixels,
                var_est = TRUE,
                var_method = "LinHTSRS")
#> $pop_total
#> [1] 44886038
#> 
#> $pop_mean
#> [1] 107.2231
#> 
#> $pop_total_var
#> [1] 8.171847e+12
#> 
#> $pop_mean_var
#> [1] 46.63093

Linear Regression Estimator

We can also fit a linear regression estimator using that same data:

xsample <- select(samp, tcc, elev) %>%
  as.data.frame()

xpop <- select(pop, names(xsample))

greg_est <- greg(y = samp$BA_TPA_ADJ,
     N = pop$npixels,
     xsample = xsample,
     xpop = xpop,
     var_est = TRUE,
     var_method = "LinHB",
     datatype = "means")

We still get the population total and mean estimates along with their variance estimates:

greg_est[1:4]
#> $pop_total
#> [1] 39521557
#> 
#> $pop_mean
#> [1] 94.40847
#> 
#> $pop_total_var
#> [1] 6.478447e+12
#> 
#> $pop_mean_var
#> [1] 36.9679

But with this estimator we also get the weights

greg_est[5]
#> $weights
#>   [1] 8060.960 6489.037 6929.206 3145.983 8041.396 3737.945 5687.480 3577.979
#>   [9] 3020.678 1690.956 2427.990 6469.919 3123.403 4879.079 2639.280 2624.305
#>  [17] 1954.575 1186.989 3207.814 8886.670 3196.904 1876.082 4186.524 3406.140
#>  [25] 5158.757 2823.357 2449.285 4110.968 1848.589 8638.148 3758.716 4917.963
#>  [33] 2738.744 2695.673 1852.252 2086.525 2920.164 2799.124 7854.180 4707.118
#>  [41] 5730.753 1262.825 4115.680 4101.833 7274.657 2607.800 2055.045 2659.727
#>  [49] 1947.451 4639.413 3681.351 4315.134 3403.483 4823.892 1674.373 7991.121
#>  [57] 2524.517 6087.918 1799.399 5632.293 2457.738 3312.270 6021.943 5538.423
#>  [65] 3995.847 6102.368 3174.927 4497.681 4476.307 2101.377 6093.312 5503.605
#>  [73] 1478.582 2305.018 2687.543 2518.520 3852.988 5259.550 2145.174 5314.536
#>  [81] 5218.411 3132.459 7656.256 3177.261 8595.077 3461.808 2174.599 2428.715
#>  [89] 3508.262 3680.625 5900.982 5897.923 3362.667 2789.342 9305.020 5559.150
#>  [97] 5000.197 8538.360 7337.170 2925.479

and the coefficients for the model

greg_est[6]
#> $coefficients
#> [1] -30.26159045   0.79210774   0.09679431

mase documentation built on Nov. 28, 2023, 1:08 a.m.