pooled.Estimator {rQCC} | R Documentation |
Pooled Estimator
Description
This function calculates the pooled estimator based on the unbiased estimators such as the mean, median, Hodges-Lehmann (HL1, HL2, HL3), standard deviation, range, median absolute deviation (MAD) and Shamos estimators.
Usage
pooledEstimator(x, estimator = c("mean", "median", "HL1", "HL2", "HL3",
"sd", "range", "mad", "shamos"),
poolType=c("A", "B", "C") )
Arguments
x |
a numeric list of observations. |
estimator |
a character string specifying the estimator, must be
one of |
poolType |
Type for how to pool estimators, must be
one of |
Details
This function calculates the pooled estimator based on
one of "mean"
(default), "median"
, "HL1"
, "HL2"
, "HL3"
,
"sd"
, "mad"
, and "shamos"
, which are all unbiased.
There are three different methods of pooling the estimators, denoted by
"A"
(default), "B"
, and "C"
.
For more details on how to pool them, refer to vignette.
Value
They return a numeric value.
Author(s)
Chanseok Park
References
Park, C. and M. Wang (2020).
A study on the X-bar and S control charts with unequal sample sizes.
Mathematics, 8(5), 698.
doi: 10.3390/math8050698
Park, C., H. Kim, and M. Wang (2022).
Investigation of finite-sample properties of robust location and scale estimators.
Communications in Statistics - Simulation and Computation,
51, 2619-2645.
doi: 10.1080/03610918.2019.1699114
Examples
x1 = c(1,2,3,4,5)
x2 = c(6,7)
x = list(x1,x2)
# Pooled sample mean (default) by type "A" pooling
pooledEstimator(x)
pooledEstimator(x, "mean", "A") # same as the above
# Pooled sample mean by type "B" pooling
pooledEstimator(x, "mean", "B")
# Pooled sample sd by type "B" pooling
pooledEstimator(x, estimator="sd", pool="B")