`BinomCIn.Rd`

Returns the necessary sample size to achieve a given width of a binomial confidence interval, as calculated by `BinomCI()`

. The function uses `uniroot()`

to find a numeric solution.

```
BinomCIn(p = 0.5, width, interval = c(1, 100000),
conf.level = 0.95, sides = "two.sided", method = "wilson")
```

- p
probability for success, defaults to

`0.5`

.- width
the width of the confidence interval

- interval
a vector containing the end-points of the interval to be searched for the root. The defaults are set to

`c(1, 100000)`

.- conf.level
confidence level, defaults to

`0.95`

.- sides
a character string specifying the side of the confidence interval, must be one of

`"two.sided"`

(default),`"left"`

or`"right"`

. You can specify just the initial letter.`"left"`

would be analogue to a hypothesis of`"greater"`

in a`t.test`

.- method
character string specifing which method to use; this can be one out of:

`"wald"`

,`"wilson"`

,`"wilsoncc"`

,`"agresti-coull"`

,`"jeffreys"`

,`"modified wilson"`

,`"modified jeffreys"`

,`"clopper-pearson"`

,`"arcsine"`

,`"logit"`

,`"witting"`

or`"pratt"`

. Defaults to`"wilson"`

. Abbreviation of method are accepted. See details in`BinomCI()`

.

The required sample sizes for a specific width of confidence interval depends on the proportion in the population. This value might be unknown right from the start when a study is planned. In such cases the sample size needed for a given level of accuracy can be estimated using the worst case percentage which is p=50%. When a better estimate is available you can you can use it to get a smaller interval.

a numeric value

`BinomCI()`

```
BinomCIn(p=0.1, width=0.05, method="pratt")
#> [1] 586.9031
```