The hardware and bandwidth for this mirror is donated by dogado GmbH, the Webhosting and Full Service-Cloud Provider. Check out our Wordpress Tutorial.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]dogado.de.

R-CMD-check CRAN status

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:

The available variance estimation techniques are:

See mase/inst/REFERENCES.bib for sources related to each variance estimator.

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, c(tcc, elev, ppt, tmean))

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[c('pop_total','pop_mean', 'pop_total_var', 'pop_mean_var')]
#> $pop_total
#> [1] 39106643
#> 
#> $pop_mean
#> [1] 93.41733
#> 
#> $pop_total_var
#> [1] 6.328655e+12
#> 
#> $pop_mean_var
#> [1] 36.11314

But with this estimator we also get the weights

greg_est["weights"]
#> $weights
#>   [1] 8110.6960 8127.9599 7941.2651 3921.2834 7408.5365 4513.9805 5072.4347
#>   [8] 3113.4665 2668.6353 1624.0109 3050.8955 5767.8383 3309.6885 4758.3397
#>  [15] 3515.1741 1072.4099 1341.8432 2575.2742 4324.6776 7854.7045 1764.1326
#>  [22] 2033.4284 5607.9363 4334.7522 6112.1528 1717.4419 2122.6873 3394.7071
#>  [29] 1673.3117 7415.5078 4197.2586 6329.4902 2163.0174 3216.2894  738.0286
#>  [36] 1196.9899  665.2472 2882.4305 7690.6378 5571.6106 6321.0567  883.0485
#>  [43] 3980.2541 4728.0695 6818.1577 2608.9368 3721.9650 2126.2434 1576.9905
#>  [50] 4366.7802 4596.6651 4106.1462 3914.2027 5396.3184 1239.4076 7226.7119
#>  [57] 1828.1823 6284.2791 1678.8441 6388.1890 2120.5596 4024.6627 6659.0981
#>  [64] 6361.2053 4558.0869 7180.3791 1872.7464 3622.3400 3478.5788 4049.6881
#>  [71] 5161.6503 5505.3940 1062.8079 1378.3263 2591.6583  636.4387 3864.2963
#>  [78] 5134.7709 1522.9424 5719.7012 5138.4440 4183.3826 7971.2083 3122.3592
#>  [85] 7943.2118 4054.2819 2670.7987 2655.2078 3870.2713 2620.7724 6439.1774
#>  [92] 6255.7971 3504.0819 3620.3363 9988.3242 4310.8084 5048.3191 8485.6856
#>  [99] 6652.4721 2892.1071

and the coefficients for the model

greg_est["coefficients"]
#> $coefficients
#>   (Intercept)           tcc          elev           ppt         tmean 
#> -3.355552e+01  6.515276e-01  4.215046e-02  6.647252e-02  2.984714e-04

Variable Selection

All of the mase regression estimators can also perform variable selection internally using the parameter modelselect

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

And we can examine which predictors were chosen:

greg_select["coefficients"]
#> $coefficients
#>  (Intercept)          tcc         elev          ppt 
#> -33.24787647   0.65151379   0.04209371   0.06643125

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.