Shade histogram area beyond an observed statistic

shade_p_value() plots a p-value region on top of visualize() output. The output is a ggplot2 layer that can be added with +. The function has a shorter alias, shade_pvalue().

Learn more in vignette("infer").

Usage

shade_p_value(obs_stat, direction, color = "red2", fill = "pink", ...)

shade_pvalue(obs_stat, direction, color = "red2", fill = "pink", ...)

Arguments

obs_stat

The observed statistic or estimate. For calculate()-based workflows, this will be a 1-element numeric vector or a 1 x 1 data frame containing the observed statistic. For fit()-based workflows, a (p + 1) x 2 data frame with columns term and estimate giving the observed estimate for each term.

direction

A string specifying in which direction the shading should occur. Options are "less", "greater", or "two-sided". Can also give "left", "right", "both", "two_sided", "two sided", or "two.sided". If NULL, the function will not shade any area.

color

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

fill

A character or hex string specifying the color to shade the p-value region. If NULL, the function will not shade any area.

...

Other arguments passed along to ggplot2 functions. For expert use only.

Value

If added to an existing infer visualization, a ggplot2 object displaying the supplied statistic on top of its corresponding distribution. Otherwise, an infer_layer list.

See also

Other visualization functions: shade_confidence_interval()

Examples

# find the point estimate---mean number of hours worked per week
point_estimate <- gss %>%
  specify(response = hours) %>%
  hypothesize(null = "point", mu = 40) %>%
  calculate(stat = "t")

# ...and a null distribution
null_dist <- gss %>%
  # ...we're interested in the number of hours worked per week
  specify(response = hours) %>%
  # hypothesizing that the mean is 40
  hypothesize(null = "point", mu = 40) %>%
  # generating data points for a null distribution
  generate(reps = 1000, type = "bootstrap") %>%
  # estimating the null distribution
  calculate(stat = "t")

# shade the p-value of the point estimate
null_dist %>%
  visualize() +
  shade_p_value(obs_stat = point_estimate, direction = "two-sided")


# you can shade confidence intervals on top of
# theoretical distributions, too!
null_dist_theory <- gss %>%
  specify(response = hours) %>%
  assume(distribution = "t")

null_dist_theory %>%
  visualize() +
  shade_p_value(obs_stat = point_estimate, direction = "two-sided")


# \donttest{
# to visualize distributions of coefficients for multiple
# explanatory variables, use a `fit()`-based workflow

# fit 1000 linear models with the `hours` variable permuted
null_fits <- gss %>%
 specify(hours ~ age + college) %>%
 hypothesize(null = "independence") %>%
 generate(reps = 1000, type = "permute") %>%
 fit()

null_fits
#> # A tibble: 3,000 × 3
#> # Groups:   replicate [1,000]
#>    replicate term          estimate
#>        <int> <chr>            <dbl>
#>  1         1 intercept      42.3   
#>  2         1 age            -0.0191
#>  3         1 collegedegree  -0.303 
#>  4         2 intercept      37.2   
#>  5         2 age             0.105 
#>  6         2 collegedegree  -0.0498
#>  7         3 intercept      40.3   
#>  8         3 age             0.0240
#>  9         3 collegedegree   0.379 
#> 10         4 intercept      41.0   
#> # ℹ 2,990 more rows

# fit a linear model to the observed data
obs_fit <- gss %>%
  specify(hours ~ age + college) %>%
  fit()

obs_fit
#> # A tibble: 3 × 2
#>   term          estimate
#>   <chr>            <dbl>
#> 1 intercept     40.6    
#> 2 age            0.00596
#> 3 collegedegree  1.53   

# visualize distributions of coefficients
# generated under the null
visualize(null_fits)


# add a p-value shading layer to juxtapose the null
# fits with the observed fit for each term
visualize(null_fits) +
  shade_p_value(obs_fit, direction = "both")


# the direction argument will be applied
# to the plot for each term
visualize(null_fits) +
  shade_p_value(obs_fit, direction = "left")

# }

# more in-depth explanation of how to use the infer package
if (FALSE) {
vignette("infer")
}