# 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`

## 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 1, 2019, 7:54 p.m.