statsExpressions (version 1.1.0)

contingency_table: Contingency table analyses

Description

A dataframe containing results from for contingency table analysis or goodness of fit test.

To see details about functions which are internally used to carry out these analyses, see the following vignette- https://indrajeetpatil.github.io/statsExpressions/articles/stats_details.html

Usage

contingency_table(
  data,
  x,
  y = NULL,
  paired = FALSE,
  type = "parametric",
  counts = NULL,
  ratio = NULL,
  k = 2L,
  conf.level = 0.95,
  sampling.plan = "indepMulti",
  fixed.margin = "rows",
  prior.concentration = 1,
  top.text = NULL,
  ...
)

Arguments

data

A dataframe (or a tibble) from which variables specified are to be taken. Other data types (e.g., matrix,table, array, etc.) will not be accepted.

x

The variable to use as the rows in the contingency table.

y

The variable to use as the columns in the contingency table. Default is NULL. If NULL, one-sample proportion test (a goodness of fit test) will be run for the x variable. Otherwise association test will be carried out.

paired

Logical indicating whether data came from a within-subjects or repeated measures design study (Default: FALSE). If TRUE, McNemar's test expression will be returned. If FALSE, Pearson's chi-square test will be returned.

type

A character specifying the type of statistical approach:

• "parametric"

• "nonparametric"

• "robust"

• "bayes"

You can specify just the initial letter.

counts

A string naming a variable in data containing counts, or NULL if each row represents a single observation.

ratio

A vector of proportions: the expected proportions for the proportion test (should sum to 1). Default is NULL, which means the null is equal theoretical proportions across the levels of the nominal variable. This means if there are two levels this will be ratio = c(0.5,0.5) or if there are four levels this will be ratio = c(0.25,0.25,0.25,0.25), etc.

k

Number of digits after decimal point (should be an integer) (Default: k = 2L).

conf.level

Confidence/Credible Interval (CI) level. Default to 0.95 (95%).

sampling.plan

Character describing the sampling plan. Possible options are "indepMulti" (independent multinomial; default), "poisson", "jointMulti" (joint multinomial), "hypergeom" (hypergeometric). For more, see ?BayesFactor::contingencyTableBF().

fixed.margin

For the independent multinomial sampling plan, which margin is fixed ("rows" or "cols"). Defaults to "rows".

prior.concentration

Specifies the prior concentration parameter, set to 1 by default. It indexes the expected deviation from the null hypothesis under the alternative, and corresponds to Gunel and Dickey's (1974) "a" parameter.

top.text

Text to display on top of the Bayes Factor message. This is mostly relevant in the context of ggstatsplot functions.

...

Additional arguments (currently ignored).

Examples

# NOT RUN {
# for reproducibility
set.seed(123)
library(statsExpressions)
options(tibble.width = Inf, pillar.bold = TRUE, pillar.neg = TRUE)

# ------------------------ non-Bayesian -----------------------------

# association test
contingency_table(
  data = mtcars,
  x = am,
  y = cyl,
  paired = FALSE
)

# goodness-of-fit test
contingency_table(
  data = as.data.frame(HairEyeColor),
  x = Eye,
  counts = Freq,
  ratio = c(0.2, 0.2, 0.3, 0.3)
)

# ------------------------ Bayesian -----------------------------

# association test
contingency_table(
  data = mtcars,
  x = am,
  y = cyl,
  paired = FALSE,
  type = "bayes"
)

# goodness-of-fit test
contingency_table(
  data = as.data.frame(HairEyeColor),
  x = Eye,
  counts = Freq,
  ratio = c(0.2, 0.2, 0.3, 0.3),
  type = "bayes"
)
# }