covpSIM: Coverage Probability using simulation Coverage probability...

Description Usage Arguments Details Value See Also Examples

View source: R/225.CoverageProb_GENERAL_SIMULATEDp.R

Description

Coverage Probability using simulation Coverage probability for CI obtained from any method over the space [0, 1]

Usage

1
covpSIM(n, LL, UL, alp, s, a, b, t1, t2)

Arguments

n

- Number of trials

LL

- Lower limit

UL

- Upper limit

alp

- Alpha value (significance level required)

s

- Number of hypothetical "p"

a

- Beta parameters for hypo "p"

b

- Beta parameters for hypo "p"

t1

- Lower tolerance limit to check the spread of coverage Probability

t2

- Upper tolerance limit to check the spread of coverage Probability

Details

Evaluation of intervals obtained from any method using coverage probability, root mean square statistic, and the proportion of proportion lies within the desired level of coverage for the n + 1 intervals and pre-defined space for the parameter p using Monte Carle simulation

Value

A dataframe with

mcp

Mean Coverage Probability

micp

Minimum coverage probability

RMSE_N

Root Mean Square Error from nominal size

RMSE_M

Root Mean Square Error for Mean Coverage Probability

RMSE_MI

Root Mean Square Error for minimum coverage probability

tol

Required tolerance for coverage probability

See Also

Other Simulated methods for coverage probability: PlotcovpSIM

Examples

1
2
3
4
LL=c(0,0.01,0.0734,0.18237,0.3344,0.5492)		#Lower and Upper Limits
UL=c(0.4507,0.6655,0.8176,0.9265,0.9899,1)
n= 5; alp=0.05; s=5000; a=1; b=1; t1=0.93; t2=0.97
covpSIM(n,LL,UL,alp,s,a,b,t1,t2)

Example output

        mcp      micp     RMSE_N     RMSE_M    RMSE_MI  tol
1 0.9782681 0.9313991 0.03342816 0.01784255 0.05015034 25.6

proportion documentation built on May 29, 2017, 10:31 a.m.