ts_mr_dfr_mean: ts_mr_dfr_mean
In yelleKneK/SMSD: Sequential Methods for Study Design

ts_mr_dfr_mean

R Documentation

ts_mr_dfr_mean

Description

Two Stage approach to Get Minimum Risk Point Estimation for distribution free random variables with known variance

Usage

ts_mr_dfr_mean(
  data,
  A,
  c,
  lowerlim,
  pilot = FALSE,
  verbose = FALSE,
  na.rm = TRUE
)

Arguments

`data`	The data for which to calculate the confidence interval. A numeric vector.
`A`	The loss function constant.
`c`	The cost of unit sample.
`lowerlim`	The lower bound for the variance.
`pilot`	Should a pilot sample be generated. True/False value. default value is `FALSE`.
`verbose`	Should the criterion be printed. Default is `FALSE`.
`na.rm`	This parameter controls whether NA values are removed from the data prior to calculation. Default is `TRUE`.

Value

The calculated risk, the sample size, mean, standard deviation, and an indicator of if the criterion was satisfied.

Author(s)

Bhargab Chattopadhyay Bhargab@iiitvadodara.ac.in, Neetu Shah 201451015@iiitvadodara.ac.in, Ken Kelley kkelley@nd.edu

References

Mukhopadhyay, N., \& de Silva, B. M. (2009). Sequential Methods and Their Applications. New York: CRC Press.

Examples

pilot_ss <- ts_mr_dfr_mean(A=100, c=4, pilot=TRUE)
## Not run: 
SLS <- rnorm(pilot_ss, mean=0, sd=1)

## End(Not run)
SLS <- rnorm(100, mean=0, sd=1)
ts_mr_dfr_mean(data=SLS, A=100, c=4, lowerlim=2, pilot=FALSE)

yelleKneK/SMSD documentation built on Nov. 23, 2022, 6:40 p.m.