ts_br_norm_mean: ts_br_norm_mean

In yelleKneK/SMSD: Sequential Methods for Study Design

ts_br_norm_mean

Description

Two Stage approach to Get Bounded Risk Point Estimation for the mean of normal random variables.

Usage

ts_br_norm_mean(data, A, k, w, pilot = FALSE, verbose = FALSE, na.rm = TRUE)

Arguments

data	The data for which to calculate the bounded risk point.
A	The loss function constant.
k	Controls whether the absolute error or squared error is used. K=1 uses absolute error, K=2 uses squared error.
w	The risk bound.
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, the mean and the number of stages, and an indicator of if the criterion is satisfied.

Author(s)

Bhargab Chattopadhyay [e-mail], Neetu Shah [e-mail], Ken Kelley [e-mail]

References

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

Examples

pilot_ss <- ts_br_norm_mean(A=2, k=1, w=0.5, pilot=TRUE)
# k=1 absolute error, k=2 squared error
## Not run: 
SLS <- rnorm(pilot_ss)

## End(Not run)
SLS <- rnorm(100)
ts_br_norm_mean(data=SLS, A=2, k=1, w=0.5, pilot=FALSE)

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