samplingNR
is an R package that allows for computing optimal
allocations under anticipated nonresponse. The underlying theory is
provided in Mendelson & Elliott (in press).
You can install the latest development version of samplingNR
from
GitHub with:
# install.packages("devtools")
devtools::install_github("jmendelson256/samplingNR", build_vignettes = TRUE)
You can learn about how to use the package in vignette("samplingNR")
,
which shows how to replicate two of the tables from our paper in an
application to a post-election survey of military personnel.
The main allocation function is opt_nh_nonresp()
, which provides the
exact version of our proposed optimal allocation under anticipated
nonresponse. The current version assumes that the goal is to minimize
the (expected) variance subject to a constraint on the total (expected)
costs or invited sample size.
If the aim is to allocate a fixed total (invited) sample size, the basic usage is:
opt_nh_nonresp(
N_h,
phibar_h,
S_h = NULL,
n_total,
...
)
where vectors N_h
and phibar_h
denote the strata population sizes,
anticipated response rates, and (optionally) strata variances,
respectively, and where scalar n_total
denotes the total sample size.
If S_h
is omitted, strata variances are assumed constant across
strata.
If the aim is to allocate sample subject to a constraint on total costs, the basic usage is:
opt_nh_nonresp(
N_h,
phibar_h,
S_h = NULL,
cost_total,
c_NR_h,
tau_h,
...
)
Here, cost_total
denotes the total allowable costs, c_NR_h
denotes
the unit costs per nonrespondent (by strata), and tau_h
denotes the
ratio of the unit costs per respondent to those of nonrespondents (by
strata). The arguments c_NR_h
and tau_h
can be specified as vectors
of dimension H
if these quantities vary by strata; alternatively, if
assumed constant across strata, they can be specified as scalars.
Mendelson, J., & Elliott, M. R. (in press). Optimal allocation under anticipated nonresponse. Journal of Survey Statistics and Methodology.