infer (version 0.3.1)

visualize: Visualize the distribution of the simulation-based inferential statistics or the theoretical distribution (or both!)

Description

Visualize the distribution of the simulation-based inferential statistics or the theoretical distribution (or both!)

Usage

visualize(data, bins = 15, method = "simulation", dens_color = "black",
  obs_stat = NULL, obs_stat_color = "red2", pvalue_fill = "pink",
  direction = NULL, endpoints = NULL,
  endpoints_color = "mediumaquamarine", ci_fill = "turquoise", ...)

Arguments

data

the output from calculate

bins

the number of bins in the histogram

method

a string giving the method to display. Options are "simulation", "theoretical", or "both" with "both" corresponding to "simulation" and "theoretical"

dens_color

a character or hex string specifying the color of the theoretical density curve

obs_stat

a numeric value or 1x1 data frame corresponding to what the observed statistic is

obs_stat_color

a character or hex string specifying the color of the observed statistic as a vertical line on the plot

pvalue_fill

a character or hex string specifying the color to shade the pvalue. In previous versions of the package this was the shade_color argument

direction

a string specifying in which direction the shading should occur. Options are "less", "greater", or "two_sided" for p-value. Can also give "left", "right", or "both" for p-value. For confidence intervals, use "between". and give the endpoint values in endpoints

endpoints

a 2 element vector or a 1 x 2 data frame containing the lower and upper values to be plotted. Most useful for visualizing conference intervals.

endpoints_color

a character or hex string specifying the color of the observed statistic as a vertical line on the plot

ci_fill

a character or hex string specifying the color to shade the confidence interval

...

other arguments passed along to ggplot2

Value

A ggplot object showing the simulation-based distribution as a histogram or bar graph. Also used to show the theoretical curves.

Examples

# NOT RUN {
# Permutations to create a simulation-based null distribution for 
# one numerical response and one categorical predictor
# using t statistic
mtcars %>%
    dplyr::mutate(am = factor(am)) %>%
    specify(mpg ~ am) %>% # alt: response = mpg, explanatory = am
    hypothesize(null = "independence") %>%
    generate(reps = 100, type = "permute") %>%
    calculate(stat = "t", order = c("1", "0")) %>%
    visualize(method = "simulation") #default method

# Theoretical t distribution for 
# one numerical response and one categorical predictor
# using t statistic
mtcars %>%
    dplyr::mutate(am = factor(am)) %>%
    specify(mpg ~ am) %>% # alt: response = mpg, explanatory = am
    hypothesize(null = "independence") %>%
    # generate() is not needed since we are not doing simulation
    calculate(stat = "t", order = c("1", "0")) %>%
    visualize(method = "theoretical")

# Overlay theoretical distribution on top of randomized t-statistics
mtcars %>%
    dplyr::mutate(am = factor(am)) %>%
    specify(mpg ~ am) %>% # alt: response = mpg, explanatory = am
    hypothesize(null = "independence") %>%
    generate(reps = 100, type = "permute") %>%
    calculate(stat = "t", order = c("1", "0")) %>%
    visualize(method = "both")
# }