Many simple hypotheses are concerned with whether the proportion of one
group (e.g. females) with a certain characteristic (e.g. tobacco smoking) is
different from that of another group (e.g. males). In the package
`ctest`

, which is now loaded automatically when
**R** starts up, is the function
`prop.test()`

. This function tests whether two or more samples
divided on a dichotomous variable have the same proportions of each value.
Here's an example:

> sexsmoke<-matrix(c(70,120,65,140),ncol=2,byrow=T) > rownames(sexsmoke)<-c("male","female") > colnames(sexsmoke)<-c("smoke","nosmoke") > prop.test(sexsmoke)

In this case, we passed a matrix of "successes" (i.e. smokers) and "failures"
(i.e. non-smokers). `prop.test()`

will also accept separate vectors
of "successes" and "totals", like this:

`> prop.test(c(70,65),c(190,205))`

You can also specify the hypothetical proportions, if you want to test the samples against a particular set of values, whether your hypothesis is directional, and the confidence interval in the case of a two sample test.

> prop.test(c(70,65),c(190,205),conf.level=0.99) > prop.test(c(70,65),c(190,205),c(0.33,0.33))

An alternative function is `fisher.test()`

, also in the package
`ctest`

. This function performs Fisher's exact test on contingency
tables. For a function that will perform multiple comparisons of proportions,
see the group.prop.test function.

For more information, see the