statsExpressions (version 0.7.0)

expr_contingency_tab: Expression and dataframe for contingency table analysis

Description

Expression and dataframe for contingency table analysis

Usage

expr_contingency_tab(
  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,
  output = "expression",
  ...
)

Arguments

data

A dataframe (or a tibble) from which variables specified are to be taken. A matrix or tables 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

Type of statistic expected ("parametric" or "nonparametric" or "robust" or "bayes").Corresponding abbreviations are also accepted: "p" (for parametric), "np" (nonparametric), "r" (robust), or "bf"resp.

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

Scalar between 0 and 1. If unspecified, the defaults return 95% confidence/credible intervals (0.95).

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.

output

If "expression", will return expression with statistical details, while "dataframe" will return a dataframe containing the results.

...

Additional arguments (currently ignored).

Value

Expression or a dataframe for contingency table analysis (Pearson's chi-square test for independence for between-subjects design or McNemar's test for within-subjects design) or goodness of fit test for a single categorical variable.

References

For more details, see- https://indrajeetpatil.github.io/statsExpressions/articles/stats_details.html

Examples

# NOT RUN {
# for reproducibility
set.seed(123)
library(statsExpressions)

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

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

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

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

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

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