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Extract reusable data from an mfrmr plot object

Source: R/api-plotting.R
plot_data.Rd

plot_data() is a small accessor for users who want to build custom base-R, ggplot2, plotly, or table-based displays from mfrmr plot helpers. It accepts an existing mfrm_plot_data object, or any mfrmr object whose plot() method supports draw = FALSE. Use plot_data_components() first when you want to inspect which components are available before extracting one.

Usage

plot_data(x, component = NULL, type = NULL, ...)

Arguments

x

An mfrm_plot_data object, or a fitted/report/review object with a plot(..., draw = FALSE) method.

component

Optional single component name inside the reusable plot data. When NULL, the full plot-data list is returned.

type

Optional plot type passed to plot() when x is not already an mfrm_plot_data object.

...

Additional arguments passed to plot(..., draw = FALSE) when x is not already an mfrm_plot_data object.

Value

The full reusable plot-data list, or the selected component.

Examples

# \donttest{
# Load the package and example ratings
library(mfrmr)
toy <- load_mfrmr_data("example_operational")

# Fit the model
fit <- fit_mfrm(
  data = toy,
  person = "Person",
  facets = c("Rater", "Criterion"),
  score = "Score",
  method = "MML",
  model = "RSM"
)

# Draw the Wright map and keep its reusable data
wright <- plot(fit)


# Extract the plotted locations as a table
locations <- plot_data(wright, component = "locations")
head(locations)
#> # A tibble: 6 × 38
#>   Group Label PlotType    Estimate    SE Fixed CI_Level SE_Method  PrecisionTier
#>   <fct> <chr> <chr>          <dbl> <dbl> <lgl>    <dbl> <chr>      <chr>        
#> 1 Rater R01   Facet level   -0.606 0.181 FALSE     0.95 Observati… exploratory  
#> 2 Rater R02   Facet level   -0.382 0.166 FALSE     0.95 Observati… exploratory  
#> 3 Rater R04   Facet level    0.180 0.185 FALSE     0.95 Observati… exploratory  
#> 4 Rater R05   Facet level    0.184 0.199 FALSE     0.95 Observati… exploratory  
#> 5 Rater R03   Facet level    0.212 0.179 FALSE     0.95 Observati… exploratory  
#> 6 Rater R06   Facet level    0.412 0.219 FALSE     0.95 Observati… exploratory  
#> # ℹ 29 more variables: SupportsFormalInference <lgl>, SEUse <chr>,
#> #   CIBasis <chr>, CIUse <chr>, CIEligible <lgl>, CILabel <chr>,
#> #   Measure_Source <chr>, CI_Lower <dbl>, CI_Upper <dbl>, Step <chr>,
#> #   StepIndex <int>, BoundarySeparated <lgl>, XBase <dbl>, X <dbl>,
#> #   OriginalEstimate <dbl>, BelowRange <lgl>, AboveRange <lgl>,
#> #   DisplayEstimate <dbl>, DisplayLabel <chr>, OriginalCI_Lower <dbl>,
#> #   OriginalCI_Upper <dbl>, DisplayCI_Lower <dbl>, DisplayCI_Upper <dbl>, …

# For table extraction alone, no graphics device is needed
locations_only <- plot_data(fit, component = "locations")
head(locations_only)
#> # A tibble: 6 × 38
#>   Group Label PlotType    Estimate    SE Fixed CI_Level SE_Method  PrecisionTier
#>   <fct> <chr> <chr>          <dbl> <dbl> <lgl>    <dbl> <chr>      <chr>        
#> 1 Rater R01   Facet level   -0.606 0.181 FALSE     0.95 Observati… exploratory  
#> 2 Rater R02   Facet level   -0.382 0.166 FALSE     0.95 Observati… exploratory  
#> 3 Rater R04   Facet level    0.180 0.185 FALSE     0.95 Observati… exploratory  
#> 4 Rater R05   Facet level    0.184 0.199 FALSE     0.95 Observati… exploratory  
#> 5 Rater R03   Facet level    0.212 0.179 FALSE     0.95 Observati… exploratory  
#> 6 Rater R06   Facet level    0.412 0.219 FALSE     0.95 Observati… exploratory  
#> # ℹ 29 more variables: SupportsFormalInference <lgl>, SEUse <chr>,
#> #   CIBasis <chr>, CIUse <chr>, CIEligible <lgl>, CILabel <chr>,
#> #   Measure_Source <chr>, CI_Lower <dbl>, CI_Upper <dbl>, Step <chr>,
#> #   StepIndex <int>, BoundarySeparated <lgl>, XBase <dbl>, X <dbl>,
#> #   OriginalEstimate <dbl>, BelowRange <lgl>, AboveRange <lgl>,
#> #   DisplayEstimate <dbl>, DisplayLabel <chr>, OriginalCI_Lower <dbl>,
#> #   OriginalCI_Upper <dbl>, DisplayCI_Lower <dbl>, DisplayCI_Upper <dbl>, …
# }