ebp_compute_cv {povmap} | R Documentation |
Coefficient of Variation (CV) estimations for Unit EBP Model Headcount Estimates
Description
Function ebp_compute_cv
estimates CVs for the headcount of the unit
model EBP functions using three different methods. CV, by definition, is the
ratio of mean square error of the head count to the head count estimates.
Therefore, the CV types are distinguished by the method of estimating the
mean square.
Usage
ebp_compute_cv(
model,
calibvar = NULL,
boot_type = "calibrate",
designvar = NULL,
threshold = NULL,
B = model$call$B
)
Arguments
model |
an object returned by the ebp function of type "emdi ebp", representing point and MSE estimates |
calibvar |
the calibration variable to be used in method 1 |
boot_type |
the bootstrap type "calibrated" or "naive" to be used in method 1 |
designvar |
the survey design variable to be used in estimating the design effect for method 3. |
threshold |
a number defining a threshold. The argument defaults to
|
B |
number of bootstrap iterations for variance estimation. Defaults
to number of bootstrap iteration in ebp obeject (specified in |
Details
Method 1 uses the calibrated/naive bootstrapping of the MSE which allows to
calibrate each bootstrap sample on auxiliary information using the
direct
function.' Calibrated bootstrap improves on the bias of the
naive bootstrap when used in the complex survey context
(see Rao and Wu (1988)) for more details.
Method 2 employs the Horowitz Thompson variance estimation technique to
compute MSE i.e. each household is assigned the probability selection within
the sample under a given sampling scheme. The computation employs
sae::direct
function.
Method 3 finally uses the design effect adjusted naive calibrated MSE. The
design effect is estimated using the survey::svydesign
function.
Value
dataframe containing different types of CV values for the headcount
Examples
data("eusilcA_pop")
data("eusilcA_smp")
# estimate a unit model
ebp_model <- ebp(fixed = eqIncome ~ gender + eqsize + cash +
self_empl + unempl_ben + age_ben + surv_ben + sick_ben +
dis_ben + rent + fam_allow + house_allow + cap_inv +
tax_adj,
pop_data = eusilcA_pop, pop_domains = "district",
smp_data = eusilcA_smp, smp_domains = "district",
na.rm = TRUE, weights = "weight",
pop_weights = "hhsize", MSE = TRUE, weights_type = "nlme",
B = 2, L = 2)
# compute CV table
ebp_compute_cv(model = ebp_model, calibvar = "gender")