covpSIM: Coverage Probability using simulation Coverage probability...

In proportion: Inference on Single Binomial Proportion and Bayesian Computations

Description

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

Usage

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

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 1, 2019, 7:54 p.m.