binom1.1sided {BAEssd} R Documentation

## Binomial Suite: One Sample, One Sided

### Description

Generates the suite of functions related to the one sample binomial experiment with a one-sided alternative hypothesis of interest.

### Usage

```  binom1.1sided(p0, a, b)
```

### Arguments

 `p0` Scalar. The upper bound of p under null hypothesis Ho: p<=p0. `a` Scalar. Shape1 parameter for prior Beta distribution. See documentation for `dbeta`. `b` Scalar. Shape2 parameter for prior Beta distribution. See documentation for `dbeta`.

### Details

`binom1.1sided` is used to generate a suite of functions for a one-sample binomial experiment with a one-sided alternative hypothesis. That is, when

X ~ Binomial(n,p)

H0: p <= p0 vs. H1: p > p0

using the following prior on p

p ~ Beta(a,b).

The functions that are generated are useful in examining the prior and posterior densities of the parameter `p`, as well as constructing the Bayes Factor and determining the sample size via an average error based approach.

The arguments of `binom1.1sided` are passed to each of the additional functions upon their creation as default values. That is, if `p0` is set to 0.5 in the call to `binom1.1sided`, each of the functions returned will have the defaualt value of 0.5 for `p0`. If an argument is not specified in the call to `binom1.1sided`, then it remains a required parameter in all functions created.

### Value

`binom1.1sided` returns a list of 4 functions. These functions are useful for either visualizing the problem, or computing the required sample size:

 `logm` The function returns a list of three vectors: the log marginal density under the null hypothesis (`logm0`), the log marginal density under the alternative hypothesis (`logm1`), and the log marginal density (`logm`). Each are evaluated at the observed data provided. This function is also passed to `ssd.binom` to calculate required sample sizes. This function has the following usage: `logm(x, n, p0, a, b)` `x`: Vector. Number of successes observed, out of `n` independent Bernoulli trials. `n`: Scalar. Sample size, the number of independent Bernoulli trials. Remaining parameters are specified above for `binom1.1sided`. `logbf` Returns a vector: the value of the log Bayes Factor given the observed data provided and the prior parameters specified. This function has the following usage: `logbf(x, n, p0, a, b)` For details on the parameters for this function, see the above description for `logm`. `prior` Returns a vector: the value of the prior density at the specified value of `p` with parameters `a` and `b`. This function has the following usage: `prior(p, a, b)` `p`: Vector. Quantiles at which to evaluate the prior distribution. Remaining parameters are specified above for `binom1.1sided`. `post` Returns a vector: the value of the posterior density. This function has the following usage: `post(p, x, n, a, b)` `p`: Vector. Quantiles at which to evaluate the posterior distribution. `x`: Scalar. Number of successes observed, out of `n` independent Bernoulli trials. `n`: Scalar: The number of independent Bernoulli trials. Remaining parameters are specified above for `binom1.1sided`.

### See Also

`binom1.2sided`,`binom2.1sided`, `binom2.2sided`,`norm1KV.1sided`, `norm1KV.2sided`,`norm2KV.2sided` `norm1UV.2sided`,`ssd`,`BAEssd`

### Examples

```############################################################
# Generate the suite of functions for a one-sample binomial
# with a one-sided test. Consider the hypothesis
#      H0: p<=0.5   vs.  H1: p>0.5
#
# with a uniform prior on p.

# generate suite
f1 <- binom1.1sided(p0=0.5,a=1,b=1)

# attach suite
attach(f1)

# plot prior and posterior given x = 25, n = 30
ps <- seq(0.01,0.99,0.01)
p1 <- prior(ps)
p2 <- post(ps,x=25,n=30)

plot(c(p1,p2)~rep(ps,2),type="n",ylab="Density",xlab="p",main="")
lines(p1~ps,lty=1,lwd=2)
lines(p2~ps,lty=2,lwd=2)

# perform sample size calculation with TE bound of 0.25 and weight 0.5
ssd.binom(alpha=0.25,w=0.5,logm=logm)

# detain suite
detach(f1)
```

[Package BAEssd version 1.0.1 Index]