Visualize the distribution of the simulationbased inferential statistics or the theoretical distribution (or both!).
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", ...) visualise(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", ...)
data  The output from 

bins  The number of bins in the histogram. 
method  A string giving the method to display. Options are

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. Deprecated (see Details). 
obs_stat_color  A character or hex string specifying the color of the observed statistic as a vertical line on the plot. Deprecated (see Details). 
pvalue_fill  A character or hex string specifying the color to shade
the pvalue. In previous versions of the package this was the 
direction  A string specifying in which direction the shading should
occur. Options are 
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. Deprecated (see Details). 
endpoints_color  A character or hex string specifying the color of the observed statistic as a vertical line on the plot. Deprecated (see Details). 
ci_fill  A character or hex string specifying the color to shade the confidence interval. Deprecated (see Details). 
...  Other arguments passed along to {ggplot2} functions. 
A ggplot object showing the simulationbased distribution as a histogram or bar graph. Also used to show the theoretical curves.
In order to make visualization workflow more straightforward and
explicit visualize()
now only should be used to plot statistics directly.
That is why arguments not related to this task are deprecated and will be
removed in a future release of {infer}.
To add to plot information related to pvalue use shade_p_value()
. To add
to plot information related to confidence interval use
shade_confidence_interval()
.
# Permutations to create a simulationbased 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")#> Warning: Check to make sure the conditions have been met for the theoretical method. {infer} currently does not check these for you.# Overlay theoretical distribution on top of randomized tstatistics 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")#> Warning: Check to make sure the conditions have been met for the theoretical method. {infer} currently does not check these for you.