Add information about confidence interval

shade_confidence_interval() plots a confidence interval region on top of visualize() output. The output is a ggplot2 layer that can be added with +. The function has a shorter alias, shade_ci().

Learn more in vignette("infer").

Usage

shade_confidence_interval(
  endpoints,
  color = "mediumaquamarine",
  fill = "turquoise",
  ...
)

shade_ci(endpoints, color = "mediumaquamarine", fill = "turquoise", ...)

Arguments

endpoints: The lower and upper bounds of the interval to be plotted. Likely, this will be the output of get_confidence_interval(). For calculate()-based workflows, this will be a 2-element vector or a 1 x 2 data frame containing the lower and upper values to be plotted. For fit()-based workflows, a (p + 1) x 3 data frame with columns term, lower_ci, and upper_ci, giving the upper and lower bounds for each regression term. For use in visualizations of assume() output, this must be the output of get_confidence_interval().
color: A character or hex string specifying the color of the end points as a vertical lines on the plot.
fill: A character or hex string specifying the color to shade the confidence interval. If NULL then no shading is actually done.
...: Other arguments passed along to ggplot2 functions.

Value

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

Examples

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

# ...and a bootstrap distribution
boot_dist <- gss %>%
  # ...we're interested in the number of hours worked per week
  specify(response = hours) %>%
  # generating data points
  generate(reps = 1000, type = "bootstrap") %>%
  # finding the distribution from the generated data
  calculate(stat = "mean")

# find a confidence interval around the point estimate
ci <- boot_dist %>%
  get_confidence_interval(point_estimate = point_estimate,
                          # at the 95% confidence level
                          level = .95,
                          # using the standard error method
                          type = "se")


# and plot it!
boot_dist %>%
  visualize() +
  shade_confidence_interval(ci)


# or just plot the bounds
boot_dist %>%
  visualize() +
  shade_confidence_interval(ci, fill = NULL)


# you can shade confidence intervals on top of
# theoretical distributions, too---the theoretical
# distribution will be recentered and rescaled to
# align with the confidence interval
sampling_dist <- gss %>%
  specify(response = hours) %>%
  assume(distribution = "t")

visualize(sampling_dist) +
  shade_confidence_interval(ci)


# \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      40.8   
#>  2         1 age             0.0153
#>  3         1 collegedegree  -0.0626
#>  4         2 intercept      40.3   
#>  5         2 age             0.0278
#>  6         2 collegedegree  -0.0655
#>  7         3 intercept      42.8   
#>  8         3 age            -0.0348
#>  9         3 collegedegree   0.0726
#> 10         4 intercept      40.7   
#> # 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   

# get confidence intervals for each term
conf_ints <-
  get_confidence_interval(
    null_fits,
    point_estimate = obs_fit,
    level = .95
  )

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


# add a confidence interval shading layer to juxtapose
# the null fits with the observed fit for each term
visualize(null_fits) +
  shade_confidence_interval(conf_ints)

# }

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

Usage

Arguments

Value

See also

Examples