Crosstabulation of two variables

Produces a 2-way table of counts and the corresponding chi-square test of independence or homogeneity.

Usage

crosstabs(formula, data)

Arguments

formula: a symbolic description of the model to be fit: ~ fac1 + fac2; where fac1 and fac2 are vectors to be crosstabulated and treated internally as factors.
data: an optional data frame containing the variables in the model.

Value

Invisibly returns an object of class ct.20x, which is a list containing the following components:

row.props: a matrix of row proportions, i.e. cell counts divided by row marginals.
col.props: a matrix of column proportions, i.e. cell counts divided by column marginals.
whole.props: a matrix of whole-table proportions.
Totals: a matrix containing the cell counts and the marginal totals.
exp: a matrix of expected counts from the chi-square calculation.
chi: a matrix of cell contributions to the chi-square statistic.

Note

This is a legacy teaching helper retained for compatibility with older course material. New code should usually prefer table() and chisq.test() directly, or a purpose-built teaching wrapper.

Deprecated in s20x 3.4.0. Use table() and chisq.test() instead.

Examples


##body image data:
data(body.df)
crosstabs(~ ethnicity + married, body.df)
#> Warning: crosstabs() is deprecated; use table() and chisq.test() instead. rowdistr() accepts a two-way table.
#>          married
#> ethnicity   1  2  3  4 Total
#>   Asian    40  1  2  7    50
#>   Europn   62 10  6 22   100
#>   Maori    47  3  4 11    65
#>   Pacific  20  0  3  8    31
#>   Total   169 14 15 48   246
#> Chisq = 11.1035  df = 9   p-value = 0.26868 
#> Warning: Chi-square approximation may be incorrect