# info.binomial.kgroup: Expected Information Matrix for Single or Multiple Group... In asypow: Calculate Power Utilizing Asymptotic Likelihood Ratio Methods

## Description

Calculates expected information matrix for a single observation for single or multiple group binomial distribution.

The natural null hypothesis for a single group is that that the probability is some specified value. For multiple groups, the natural null hypothesis is that the group probabilities are the same.

## Usage

 `1` ``` info.binomial.kgroup(p, group.size=1) ```

## Arguments

 `p` Scalar or vector of probability values. The i'th component is the (alternative hypothesis or true) probability of an event in the i'th group. `group.size` Needed only if there are several groups with unequal sample sizes. The value of the i'th component is the relative sample size of the i'th group. The calculation made is for a single observation spread over the several groups in proportion to the specified relative sizes. If this value is specified, it should be a vector whose length is the same as p.

## Value

Expected information matrix for a single observation. The matrix is square with each dimension the number of groups.

## References

Cox, D.R. and Hinkley, D.V. (1974). Theoretical Statistics Chapman and Hall, London.

`info.poisson.kgroup`, `info.ordinal.kgroup`, `info.expsurv.kgroup`
 ```1 2 3 4``` ```# Find the information matrix for a 2 sample binomial with # probability of events .2 and .4 and sample sizes 10 and 11 info.binom <- info.binomial.kgroup(c(.2,.4), c(10,11)) print(info.binom) ```