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

 `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

Other Simulated methods for coverage probability: `PlotcovpSIM`
 ```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) ```