Pull the data behind a plot — pull_plot

pull_plot_data() returns the tibble one view of a diagnostic result draws. A reader can rebuild the figure with ggplot2::ggplot() directly, take the numbers somewhere else, or read what a view is showing without rendering it.

Usage

pull_plot_data(x, ...)

Arguments

x: A positivity_diagnostic or a positivity_check.
...: Passed to methods. A class drawing more than one view takes type, the name of the view whose data to return, matched against that class's autoplot() menu. A check_port() result takes low_support_only as autoplot() does, and a positivity_check takes the name of the diagnostic to pull.

Value

A tibble holding one view's data.

Details

A view returns exactly one tibble, whatever the figure does with it. Where a view splits its rows across layers, the whole frame comes back and the split stays derivable from it.

type names the view. Each method mirrors its class's autoplot() menu, including the view that menu defaults to, so pull_plot_data(x) returns the frame autoplot(x) draws. Every view is built from the frame this returns, so the figure and the frame cannot disagree, and a view a result cannot draw is refused here exactly as autoplot() refuses it.

For a check_edp() result:

"boxplot" returns the box statistics themselves, one row per intervention, or one row per measure and intervention for the estimator variant. ymin and ymax are the fifth and the ninety-fifth percentile, lower, middle, and upper are the quartiles, n is the number of observations summarized, and outliers is a list-column of the values lying outside the whiskers, which the view draws as points. The estimator variant covers edp_outcome and edp_treatment; ideal_weight shares no scale with them and is left to the scatter view.
"histogram", "ecdf", and "density" return the results at their own grain, one row per observation and intervention, with intervention as a factor whose levels follow the order the interventions were given in. The label is what separates the interventions rather than value, which a function intervention varies from observation to observation.
"scatter" returns the whole estimator results tibble. The view draws the rows of finite ideal_weight in one layer and the infinite rows in another, and both are read off ideal_weight. It needs the estimator variant, and asking for it after a data-variant run is an error.

For a check_density_ratios() result:

"distribution" returns one row per ratio and time point, time and ratio. That is the grain the point-treatment histogram bins and the time-varying boxplot summarizes, and the raw ratios live nowhere else in the result.
"cumulative" returns the cumulative-product summaries the series view draws, one row per time point and statistic, with series naming the summary in the words the key uses. It needs a time-varying input.

For a check_eta_bias() result:

"bootstrap" returns one row per bootstrap draw, keyed on the estimand term, on the truncation level, and on the label its facet strip reads. The view bins the estimate. Each draw also carries the truth its term was aimed at, which the view draws as a reference line, one per term rather than one per draw, since a term's truth is the same in every draw.
"sweep" returns one row per term and truncation level, holding the truncation the sweep is drawn against, the bias, and the lower and upper ends of the band two Monte Carlo standard errors either side of it. It needs a sweep of more than one level.

For a check_hat_values() result:

"null" returns the null replicates in phi. The three numbers the figure marks beside them, null_quantile, phi_hat, and conf_level, ride as repeated columns.
"profile" returns one row per exposure percentile the candidates were built at, prob and the fraction of that percentile's candidates reading as high leverage. The results hold one row per candidate, so the aggregation is what this view adds.

For a check_port() or check_port_seq() result there is one view, so the method takes low_support_only where a menu would take type. It returns one row per bar: the reported subgroups, with a leaf that more than one tree reported collapsed to a single row, the display label, the width proportional to subgroup size, and the pair of thresholds the row's prevalence was judged against in beta_lower and beta_upper.

For a check_hdr() or check_hdr_seq() result there is one view, and the frame is the results with time as a factor where a sequential run carries one, because the wave separates the curves rather than measuring anything.

For a check_extrapolation() result:

"distribution" returns the results with exposure as a factor, which is what panels the histograms.
"hull" returns the same rows with membership, the reading of in_hull that the stacked bars and the key both use. It needs the hull test to have run.

A positivity_check holds one frame per child rather than a frame of its own, so name the diagnostic to pull: pull_plot_data(check, "port"). The frame is that child's, unchanged, and a type passed alongside reaches it.

Examples

set.seed(1)
n <- 100
x1 <- rnorm(n)
dose <- rnorm(n, mean = x1)
df <- data.frame(dose = dose, x1 = x1)
result <- check_edp(df, dose, x1, values = c(0, 1), exposure_type = "continuous")

pull_plot_data(result)
#> # A tibble: 2 × 8
#>   intervention     n  ymin lower middle upper  ymax outliers  
#>   <fct>        <int> <dbl> <dbl>  <dbl> <dbl> <dbl> <list>    
#> 1 0              100  5.59  16.6   22.7  25.5  26.7 <dbl [10]>
#> 2 1              100  4.43  14.4   20.9  24.4  25.0 <dbl [10]>
pull_plot_data(result, type = "ecdf")
#> # A tibble: 200 × 4
#>      .id intervention value   edp
#>    <int> <fct>        <dbl> <dbl>
#>  1     1 0                0  17.9
#>  2     2 0                0  26.7
#>  3     3 0                0  14.6
#>  4     4 0                0  11.4
#>  5     5 0                0  26.8
#>  6     6 0                0  14.8
#>  7     7 0                0  26.1
#>  8     8 0                0  24.0
#>  9     9 0                0  25.5
#> 10    10 0                0  22.7
#> # ℹ 190 more rows