ggstatsplot (version 0.0.11)

subtitle_onesample_proptest: Making text subtitle for Proportion Test (N Outcomes)

Description

This is going to be a chi-squared Goodness of fit test.

Usage

subtitle_onesample_proptest(data, main, counts = NULL, ratio = NULL,
  conf.level = 0.95, conf.type = "norm", nboot = 100,
  stat.title = NULL, legend.title = NULL, k = 2, messages = TRUE,
  ...)

Arguments

data

A dataframe (or a tibble) from which variables specified are to be taken. A matrix or tables will not be accepted.

main

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

counts

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

ratio

A vector of numbers: the expected proportions for the proportion test. Default is NULL, which means if there are two levels ratio = c(1,1), etc.

conf.level

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

conf.type

A vector of character strings representing the type of intervals required. The value should be any subset of the values "norm", "basic", "perc", "bca". For more, see ?boot::boot.ci.

nboot

Number of bootstrap samples for computing confidence interval for the effect size (Default: 100).

stat.title

Title for the effect being investigated with the chi-square test. The default is NULL, i.e. no title will be added to describe the effect being shown. An example of a stat.title argument will be something like "main x condition" or "interaction".

legend.title

Title text for the legend.

k

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

messages

Decides whether messages references, notes, and warnings are to be displayed (Default: TRUE).

...

Additional arguments (currently ignored).

Details

For more details about how the effect sizes and their confidence intervals were computed, see documentation in ?rcompanion::cramerVFit.

Examples

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

# with counts
subtitle_onesample_proptest(
  data = as.data.frame(HairEyeColor),
  main = Eye,
  counts = Freq,
  ratio = c(0.2, 0.2, 0.3, 0.3)
)

# in case of no variation, only sample size will be shown
subtitle_onesample_proptest(
  data = cbind.data.frame(x = rep("a", 10)),
  main = x
)
# }

Run the code above in your browser using DataCamp Workspace