# z_tests_cfa: Two z-Approximation Tests In confreq: Configural Frequencies Analysis Using Log-Linear Modeling

## Description

Calculates the Chi-square approximation to the z-test and the binomial approximation to the z-test.

## Usage

 `1` ```z_tests_cfa(observed, expected, ccor = FALSE, ntotal = sum(observed)) ```

## Arguments

 `observed` a vector giving the observed frequencies. `expected` a vector giving the expected frequencies. `ccor` either a logical (TRUE / FALSE) determining wether to apply a continuity correction or not. When set to `ccor=TRUE` continuity correction is applied for expected values 5 =< expected =< 10. For `ccor=FALSE` no continuity correction is applied. Another option is to set `ccor=c(x,y)` where x is the lower and y the upper bound for expected values where continuity correction is applied. So `ccor=c(5,10)` is equivalent to `ccor=TRUE`. `ntotal` optional a numeric giving the total number of observations. By default ntotal is calculated as `ntotal=sum(observed)`.

## Details

An continuity correction can be applied to the binomial approximation – see argument `ccor`.

## Value

a list with z an p-values.

## References

No references in the moment

## Examples

 ```1 2 3 4 5 6 7 8``` ```####################################### # expected counts for LienertLSD data example. designmatrix<-design_cfg_cfa(kat=c(2,2,2)) # generate an designmatrix (only main effects) data(LienertLSD) # load example data observed<-LienertLSD[,4] # extract observed counts expected<-expected_cfa(des=designmatrix, observed=observed) # calculation of expected counts z_tests_cfa(observed,expected) ####################################### ```

confreq documentation built on May 29, 2017, 5:46 p.m.