plot_data_components() is a companion to plot_data(). It returns a
compact table that tells users which plot-data components are available,
what shape they have, and which ones are most useful for custom graphics,
dashboards, or report assembly.
Arguments
- x
An
mfrm_plot_dataobject, or a fitted/report/review object with aplot(..., draw = FALSE)method.- type
Optional plot type passed to
plot()whenxis not already anmfrm_plot_dataobject.- ...
Additional arguments passed to
plot(..., draw = FALSE)whenxis not already anmfrm_plot_dataobject.
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"
)
# Discover which tables the default Wright map provides without drawing it
plot_data_components(fit)
#> PlotName Component Role ObjectType Rows
#> 1 wright_map wright_style style character NA
#> 2 wright_map renderer scalar_or_vector character NA
#> 3 wright_map visual_contract scalar_or_vector character NA
#> 4 wright_map person table_data data.frame 48
#> 5 wright_map person_exclusions table_data data.frame 0
#> 6 wright_map person_hist metadata list:histogram NA
#> 7 wright_map person_stats table_data data.frame 1
#> 8 wright_map locations table_data data.frame 12
#> 9 wright_map label_points table_data data.frame 12
#> 10 wright_map group_summary summary_or_guidance data.frame 3
#> 11 wright_map group_levels settings character NA
#> 12 wright_map y_range settings double NA
#> 13 wright_map display_settings settings data.frame 1
#> 14 wright_map label_limit scalar_or_vector integer NA
#> 15 wright_map retention table_data data.frame 3
#> 16 wright_map retention_note summary_or_guidance character NA
#> 17 wright_map title scalar_or_vector character NA
#> 18 wright_map subtitle scalar_or_vector character NA
#> 19 wright_map show_ci scalar_or_vector logical NA
#> 20 wright_map uncertainty_display scalar_or_vector character NA
#> 21 wright_map group scalar_or_vector NULL NA
#> 22 wright_map preset settings character NA
#> 23 wright_map legend style data.frame 5
#> 24 wright_map reference_lines annotation data.frame 1
#> 25 wright_map scale_contract table_data data.frame 1
#> 26 wright_map plot_name scalar_or_vector character NA
#> 27 wright_map fit_readiness fit_review data.frame 6
#> 28 wright_map interpretation_status summary_or_guidance character NA
#> 29 wright_map interpretation_note summary_or_guidance character NA
#> 30 wright_map display metadata list:list NA
#> 31 wright_map notes summary_or_guidance data.frame 3
#> Columns Length IsTabular Accessor
#> 1 NA 1 FALSE plot_data(x, component = "wright_style")
#> 2 NA 1 FALSE plot_data(x, component = "renderer")
#> 3 NA 1 FALSE plot_data(x, component = "visual_contract")
#> 4 22 22 TRUE plot_data(x, component = "person")
#> 5 22 22 TRUE plot_data(x, component = "person_exclusions")
#> 6 6 6 FALSE plot_data(x, component = "person_hist")
#> 7 7 7 TRUE plot_data(x, component = "person_stats")
#> 8 38 38 TRUE plot_data(x, component = "locations")
#> 9 44 44 TRUE plot_data(x, component = "label_points")
#> 10 16 16 TRUE plot_data(x, component = "group_summary")
#> 11 NA 3 FALSE plot_data(x, component = "group_levels")
#> 12 NA 2 FALSE plot_data(x, component = "y_range")
#> 13 8 8 TRUE plot_data(x, component = "display_settings")
#> 14 NA 1 FALSE plot_data(x, component = "label_limit")
#> 15 6 6 TRUE plot_data(x, component = "retention")
#> 16 NA 1 FALSE plot_data(x, component = "retention_note")
#> 17 NA 1 FALSE plot_data(x, component = "title")
#> 18 NA 1 FALSE plot_data(x, component = "subtitle")
#> 19 NA 1 FALSE plot_data(x, component = "show_ci")
#> 20 NA 1 FALSE plot_data(x, component = "uncertainty_display")
#> 21 NA 0 FALSE plot_data(x, component = "group")
#> 22 NA 1 FALSE plot_data(x, component = "preset")
#> 23 4 4 TRUE plot_data(x, component = "legend")
#> 24 5 5 TRUE plot_data(x, component = "reference_lines")
#> 25 15 15 TRUE plot_data(x, component = "scale_contract")
#> 26 NA 1 FALSE plot_data(x, component = "plot_name")
#> 27 2 2 TRUE plot_data(x, component = "fit_readiness")
#> 28 NA 1 FALSE plot_data(x, component = "interpretation_status")
#> 29 NA 1 FALSE plot_data(x, component = "interpretation_note")
#> 30 2 2 FALSE plot_data(x, component = "display")
#> 31 2 2 TRUE plot_data(x, component = "notes")
#> Notes
#> 1
#> 2
#> 3
#> 4
#> 5
#> 6
#> 7
#> 8
#> 9
#> 10 Use for captions, QA checks, or report text.
#> 11
#> 12
#> 13
#> 14
#> 15
#> 16 Use for captions, QA checks, or report text.
#> 17
#> 18
#> 19
#> 20
#> 21
#> 22
#> 23 Use to reproduce color, line-type, or legend mappings.
#> 24 Use with primary data to draw thresholds, labels, and reference lines.
#> 25
#> 26
#> 27
#> 28 Use for captions, QA checks, or report text.
#> 29 Use for captions, QA checks, or report text.
#> 30
#> 31 Use for captions, QA checks, or report text.
#> ColumnNames
#> 1
#> 2
#> 3
#> 4 Person, Estimate, SD, PosteriorSD, SE, Extreme, PrimaryEstimate, OptimizerEstimate, DisplayEstimate, DisplayAdjustment, ParameterStatus, BoundaryDirection, ResponseExtreme, ResponseRows, WeightedResponseTotal, PrimaryEstimateBasis, OptimizerEstimateUse, ReasonCodes, ReadinessContractVersion, SourceFitReadiness, SourceInferenceReady, EstimateUse
#> 5 Person, Estimate, SD, PosteriorSD, SE, Extreme, PrimaryEstimate, OptimizerEstimate, DisplayEstimate, DisplayAdjustment, ParameterStatus, BoundaryDirection, ResponseExtreme, ResponseRows, WeightedResponseTotal, PrimaryEstimateBasis, OptimizerEstimateUse, ReasonCodes, ReadinessContractVersion, SourceFitReadiness, SourceInferenceReady, EstimateUse
#> 6 breaks, counts, density, mids, xname, equidist
#> 7 N, ReviewExcludedN, FiniteN, BoundaryExcludedN, Mean, Median, SD
#> 8 Group, Label, PlotType, Estimate, SE, Fixed, CI_Level, SE_Method, PrecisionTier, SupportsFormalInference, SEUse, CIBasis, CIUse, CIEligible, CILabel, Measure_Source, CI_Lower, CI_Upper, Step, StepIndex, BoundarySeparated, XBase, X, OriginalEstimate, BelowRange, AboveRange, DisplayEstimate, DisplayLabel, OriginalCI_Lower, OriginalCI_Upper, DisplayCI_Lower, DisplayCI_Upper, CIClippedLower, CIClippedUpper, CIClipped, BoundaryEnd, CISuppressed, CIDisplayStatus
#> 9 Group, Label, PlotType, Estimate, SE, Fixed, CI_Level, SE_Method, PrecisionTier, SupportsFormalInference, SEUse, CIBasis, CIUse, CIEligible, CILabel, Measure_Source, CI_Lower, CI_Upper, Step, StepIndex, BoundarySeparated, XBase, X, OriginalEstimate, BelowRange, AboveRange, DisplayEstimate, DisplayLabel, OriginalCI_Lower, OriginalCI_Upper, DisplayCI_Lower, DisplayCI_Upper, CIClippedLower, CIClippedUpper, CIClipped, BoundaryEnd, CISuppressed, CIDisplayStatus, LabelY, LabelSide, LabelX, LabelHjust, LabelText, LabelDisplaced
#> 10 Group, PlotType, Min, Q1, Median, Q3, Max, DisplayMin, DisplayQ1, DisplayMedian, DisplayQ3, DisplayMax, N, XBase, TargetGap, DisplayTargetGap
#> 11
#> 12
#> 13 Renderer, LowerLogit, UpperLogit, AutoRangePolicy, BoundaryLevelsAtEnds, CIClippedCount, BoundaryCIEndpointCount, CIDisplayPolicy
#> 14
#> 15 Component, Shown, Total, Omitted, RequestedTopN, Complete
#> 16
#> 17
#> 18
#> 19
#> 20
#> 21
#> 22
#> 23 label, role, aesthetic, value
#> 24 axis, value, label, linetype, role
#> 25 Model, Method, CoordinateBasis, PopulationSD, SlopeBasis, GpcmModelFamily, GpcmSlopeAction, GpcmSlopeComposition, GpcmLatentDimensionCount, GpcmMmlIdentification, GpcmEstimatorFamily, GpcmStatisticalPenalty, GpcmFiniteParameterBox, GpcmExtremePersonPolicy, FixedLatentSDSlopeField
#> 26
#> 27 Domain, Status
#> 28
#> 29
#> 30 show_title, show_notes
#> 31 Type, Text
# Extract one of the listed components
locations <- plot_data(fit, 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>, …
# A different plot type has different reusable tables
plot_data_components(fit, type = "ccc")
#> PlotName Component Role
#> 1 category_characteristic_curves probabilities curve_data
#> 2 category_characteristic_curves curve_basis curve_data
#> 3 category_characteristic_curves title scalar_or_vector
#> 4 category_characteristic_curves subtitle scalar_or_vector
#> 5 category_characteristic_curves preset settings
#> 6 category_characteristic_curves legend style
#> 7 category_characteristic_curves reference_lines annotation
#> 8 category_characteristic_curves scale_contract table_data
#> 9 category_characteristic_curves plot_name scalar_or_vector
#> 10 category_characteristic_curves fit_readiness fit_review
#> 11 category_characteristic_curves interpretation_status summary_or_guidance
#> 12 category_characteristic_curves interpretation_note summary_or_guidance
#> 13 category_characteristic_curves display metadata
#> 14 category_characteristic_curves notes summary_or_guidance
#> ObjectType Rows Columns Length IsTabular
#> 1 data.frame 964 13 13 TRUE
#> 2 data.frame 1 3 3 TRUE
#> 3 character NA NA 1 FALSE
#> 4 character NA NA 1 FALSE
#> 5 character NA NA 1 FALSE
#> 6 data.frame 4 4 4 TRUE
#> 7 data.frame 1 5 5 TRUE
#> 8 data.frame 1 15 15 TRUE
#> 9 character NA NA 1 FALSE
#> 10 data.frame 6 2 2 TRUE
#> 11 character NA NA 1 FALSE
#> 12 character NA NA 1 FALSE
#> 13 list:list NA 2 2 FALSE
#> 14 data.frame 3 2 2 TRUE
#> Accessor
#> 1 plot_data(x, component = "probabilities")
#> 2 plot_data(x, component = "curve_basis")
#> 3 plot_data(x, component = "title")
#> 4 plot_data(x, component = "subtitle")
#> 5 plot_data(x, component = "preset")
#> 6 plot_data(x, component = "legend")
#> 7 plot_data(x, component = "reference_lines")
#> 8 plot_data(x, component = "scale_contract")
#> 9 plot_data(x, component = "plot_name")
#> 10 plot_data(x, component = "fit_readiness")
#> 11 plot_data(x, component = "interpretation_status")
#> 12 plot_data(x, component = "interpretation_note")
#> 13 plot_data(x, component = "display")
#> 14 plot_data(x, component = "notes")
#> Notes
#> 1
#> 2
#> 3
#> 4
#> 5
#> 6 Use to reproduce color, line-type, or legend mappings.
#> 7 Use with primary data to draw thresholds, labels, and reference lines.
#> 8
#> 9
#> 10
#> 11 Use for captions, QA checks, or report text.
#> 12 Use for captions, QA checks, or report text.
#> 13
#> 14 Use for captions, QA checks, or report text.
#> ColumnNames
#> 1 Theta, Probability, ExpectedScore, ScoreVariance, Information, CategoryInformation, CategoryInformationShare, Slope, Model, Category, CurveGroup, CurveBasis, PredictorOffset
#> 2 CurveBasis, PredictorOffset, Description
#> 3
#> 4
#> 5
#> 6 label, role, aesthetic, value
#> 7 axis, value, label, linetype, role
#> 8 Model, Method, CoordinateBasis, PopulationSD, SlopeBasis, GpcmModelFamily, GpcmSlopeAction, GpcmSlopeComposition, GpcmLatentDimensionCount, GpcmMmlIdentification, GpcmEstimatorFamily, GpcmStatisticalPenalty, GpcmFiniteParameterBox, GpcmExtremePersonPolicy, FixedLatentSDSlopeField
#> 9
#> 10 Domain, Status
#> 11
#> 12
#> 13 show_title, show_notes
#> 14 Type, Text
# }