cont_table {descstat}

Contingency table

Description

A contingency table returns the counts of all the combinations of the modalities of two series in a table for which every modality of the first series is a row and every modality of the second series is a column. The joint, marginal and conditional functions compute these three distributions from the contingency table (by indicating one series for the last two).

Usage

cont_table(
  data,
  x1,
  x2,
  weights = NULL,
  freq = NULL,
  total = FALSE,
  xfirst1 = NULL,
  xlast1 = NULL,
  wlast1 = NULL,
  xfirst2 = NULL,
  xlast2 = NULL,
  wlast2 = NULL
)

joint(data)

conditional(data, x = NULL)

marginal(data, x = NULL, f = "f", vals = NULL)

Arguments

data

a tibble,

x1, x2

the two series used the construct the contingency table, the distinct values of the first and the second will respectively be the rows and the columns of the contingency table,

weights

a series containing the weights that should be used to mimic the population,

freq

the frequencies (in the case where data is already contingency table),

total

if TRUE, a total is added to the table,

xfirst1, xfirst2, xlast1, xlast2, wlast1, wlast2

see as_numeric(),

x

the series on which the operation should be computed,

f

see freq_table(),

vals

see freq_table(),

Details

cont_table actually returns a tibble in "long format", as the dplyr::count table does. As the returned object is of class cont_table, this is the format and print methods that turns the tibble in a wide format before printing.

The conditional and joint functions return a cont_table object, as the marginal function returns a freq_table object.

Value

a tibble

Author(s)

Yves Croissant

Examples

library("dplyr")
# get a contingency table containing education and sex
cont_table(employment, education, sex)
# instead of counts, sum the weights
cont_table(employment, education, sex, weights = weights)
# get the joint distribution and the conditional and marginal
# distribution of sex
cont_table(employment, education, sex) %>% joint
cont_table(employment, education, sex) %>% marginal(sex)
cont_table(employment, education, sex) %>% conditional(sex)