prevalencePower: Power analysis for establishing a prevalence
In userfriendlyscience: Quantitative Analysis Made Accessible

Description Usage Arguments Details Value Author(s) See Also Examples

This function can be used to establish how many participants are required to establish a prevalence rate with a given margin of error.

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prevalencePower(expectedPrevalence,
                marginOfError = 0.05,
                conf.level = 0.95)

`expectedPrevalence`	The expected prevalence.
`marginOfError`	The desired precision.
`conf.level`	The confidence of the confidence interval.

Note that when uncertain as to the expected prevalence, it's better to assume a prevalence closer to 50%. Prevalences closer to 0% or 100% are easier to detect and therefore have more power.

The required number of participants.

Gjalt-Jorn Peters

Maintainer: Gjalt-Jorn Peters <gjalt-jorn@userfriendlyscience.com>

convert.percentage.to.se

### Required participants for detecting a prevalence of 10%
### with a 95% confidence interval of 10% wide:
prevalencePower(.1);

### Required participants for detecting a prevalence of 10%
### with a 95% confidence interval of 4% wide:
prevalencePower(.1, .02);

### Required participants for detecting a prevalence of 60%
### with a 95% confidence interval of 10% wide:
prevalencePower(.6);