| Type: | Package |
| Title: | Outlier-Robust Ratio-cum-Product Estimators of Finite Population Mean |
| Version: | 0.1.0 |
| Description: | Implements robust ratio-cum-product estimators using auxiliary medians for estimating the population mean under simple random sampling without replacement (SRSWOR). Provides analytical optimal tuning parameters, bias, Mean Squared Error (MSE), and Percent Relative Efficiency (PRE) evaluations. |
| License: | MIT + file LICENSE |
| Encoding: | UTF-8 |
| LazyData: | true |
| Imports: | stats |
| Config/roxygen2/version: | 8.1.0 |
| Depends: | R (≥ 3.5) |
| NeedsCompilation: | no |
| Packaged: | 2026-08-06 20:15:01 UTC; Dr. O. J. Obulezi |
| Author: | Okechukwu J. Obulezi
 [aut, cre]
(Affiliation: Department of Statistics, Faculty of Physical
Sciences, Nnamdi Azikiwe University, Awka, Nigeria) |
| Maintainer: | Okechukwu J. Obulezi <oj.obulezi@unizik.edu.ng> |
| Repository: | CRAN |
| Date/Publication: | 2026-08-20 15:30:02 UTC |
Compare Proposed Robust Estimator against Baselines
Description
Computes baseline estimators and evaluates Percent Relative Efficiency (PRE).
Usage
compare_estimators(y, x1, x2, M1, M2, N)
Arguments
y |
Vector of study variable. |
x1 |
Vector of first auxiliary variable. |
x2 |
Vector of second auxiliary variable. |
M1 |
Known population median of x1. |
M2 |
Known population median of x2. |
N |
Finite population size. |
Value
A data frame summarizing estimates, Bias, MSE, and PRE.
Contaminated Sample Dataset for Robust Estimation
Description
A simulated dataset containing crop yield, area, and cost data with introduced outliers.
Usage
data(crop_data)
Format
A data frame with 200 rows and 3 variables:
- Yield
Primary study variable y (Crop yield with outliers).
- Area
Auxiliary variable x1 (Positively correlated area).
- Cost
Auxiliary variable x2 (Negatively correlated cost).
Robust Ratio-cum-Product Estimator of Population Mean
Description
Evaluates the outlier-robust ratio-cum-product estimator of the finite population mean using medians of two auxiliary variables under Simple Random Sampling Without Replacement (SRSWOR).
Usage
robust_rcp(y, x1, x2, M1, M2, N, alpha1 = NULL, alpha2 = NULL)
Arguments
y |
Vector of sample observations for the primary study variable. |
x1 |
Vector of sample observations for the first auxiliary variable (positively correlated with y). |
x2 |
Vector of sample observations for the second auxiliary variable (negatively correlated with y). |
M1 |
Known population median of x1. |
M2 |
Known population median of x2. |
N |
Total finite population size. |
alpha1 |
Optional numeric tuning parameter for x1. Computed analytically if NULL. |
alpha2 |
Optional numeric tuning parameter for x2. Computed analytically if NULL. |
Value
An object of class robustrcp containing:
estimate |
The estimated population mean. |
alpha1_opt |
Optimal or specified scaling parameter for x1. |
alpha2_opt |
Optimal or specified scaling parameter for x2. |
bias |
First-order approximation of the Bias. |
mse |
First-order approximation of the Mean Squared Error (MSE). |
sample_size |
Sample size n. |
population_size |
Population size N. |
Author(s)
Okechukwu J. Obulezi oj.obulezi@unizik.edu.ng
Examples
set.seed(123)
N <- 1000
n <- 100
x1 <- rlnorm(n, meanlog = 2, sdlog = 0.8)
x2 <- rlnorm(n, meanlog = 3, sdlog = 1.0)
y <- 2*x1 - 0.5*x2 + rnorm(n, mean = 10, sd = 2)
robust_rcp(y = y, x1 = x1, x2 = x2, M1 = median(x1), M2 = median(x2), N = N)