A benchmarkInsights object

An object containing a benchmark result for evaluating analytical tasks.

Value

A benchmarkInsights object.

Public fields

evalSummary

The evaluation summary is stored by dataframe, where each row is the methodd identifier, each column is the metric used in the evaluation task and related information.

metadata

A dataframe to store metadata for the benchmark.

Methods

Public methods

• benchmarkInsights$new()

• benchmarkInsights$addevalSummary()

• benchmarkInsights$addMetadata()

• benchmarkInsights$getHeatmap()

• benchmarkInsights$getLineplot()

• benchmarkInsights$getScatterplot()

• benchmarkInsights$getBoxplot()

• benchmarkInsights$getCorplot()

• benchmarkInsights$getForestplot()

• benchmarkInsights$clone()

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Method new()

Create a benchmarkInsights object

Usage

benchmarkInsights$new(evalResult = NULL)

Arguments

evalResult

A dataframe containing initial evaluation results with columns such as datasetID, auxData, metric, and result.

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Method addevalSummary()

Add additional evaluation summary to the existing evalSummary

Usage

benchmarkInsights$addevalSummary(additional_evalResult)

Arguments

additional_evalResult

A dataframe containing additional evaluation results to be appended.

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Method addMetadata()

Add metadata to the benchmarkInsights object

Usage

benchmarkInsights$addMetadata(metadata)

Arguments

metadata

A dataframe containing metadata information.

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Method getHeatmap()

Creates a heatmap from the evaluation summary by averaging results across datasets.

Usage

benchmarkInsights$getHeatmap(evalSummary)

Arguments

evalSummary

A dataframe containing the evaluation summary.

Returns

A heatmap object.

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Method getLineplot()

Creates a line plot for the given x and y variables, with an optional grouping and fixed x order.

Usage

benchmarkInsights$getLineplot(evalResult, order = NULL, metricVariable)

Arguments

evalResult

subset of evaluation summary.

order

An optional vector specifying the order of x-axis values.

metricVariable

Specify subset value in metric column.

Returns

A ggplot2 line plot object.

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Method getScatterplot()

Creates a scatter plot for the same auxData, with an two methodd metrics.

Usage

benchmarkInsights$getScatterplot(evalResult, variables)

Arguments

evalResult

subset of evaluation summary, only include two different metrics, all auxData should be same

variables

A character vector of length two specifying the metric names to be used for the x and y axes.

Returns

A ggplot2 line plot object.

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Method getBoxplot()

Creates boxplot plots for the mutiple auxData, different method, one metric.

Usage

benchmarkInsights$getBoxplot(evalResult, metricVariable, auxDataVariable)

Arguments

evalResult

subset of evaluation summary, only include two different metrics, all auxData should be same.

metricVariable

Specify subset value in metric column.

auxDataVariable

Specify subset value in auxData column.

Returns

A ggplot2 line plot object.

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Method getCorplot()

Creates a correlation plot based on the provided evaluation summary and the specified input type (either "auxData", "metric", or "method"). The correlation plot shows the pairwise correlation between results for different categories (auxData, metric, or method).

Usage

benchmarkInsights$getCorplot(evalResult, input_type)

Arguments

evalResult

A subset of the evaluation summary. It must include columns relevant to the input type (auxData, metric, method) and the result values.

input_type

A string that specifies the input type for generating the correlation plot. It must be either "auxData", "metric", or "method".

Returns

A ggplot2 correlation plot object. The plot visualizes the correlation matrix using ggcorrplot with aesthetic enhancements like labeled values and angled axis text.

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Method getForestplot()

This function generates a forest plot using linear models based on the comparison between groups in the provided evaluation summary. The plot is created using dotwhisker and broom packages, with custom grouping and labeling.

Usage

benchmarkInsights$getForestplot(evalResult, input_group, input_model)

Arguments

evalResult

A data frame containing the evaluation summary.

input_group

A string specifying the grouping variable (only "datasetID", "method", or "auxData" allowed).

input_model

A string specifying the model variable (only "datasetID", "method", or "auxData" allowed).

Returns

A forest plot showing the comparison of models across groups.

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Method clone()

The objects of this class are cloneable with this method.

Usage

benchmarkInsights$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

benchmarkInsights$new()
#> <benchmarkInsights>
#>   Public:
#>     addMetadata: function (metadata) 
#>     addevalSummary: function (additional_evalResult) 
#>     clone: function (deep = FALSE) 
#>     evalSummary: data.frame
#>     getBoxplot: function (evalResult, metricVariable, auxDataVariable) 
#>     getCorplot: function (evalResult, input_type) 
#>     getForestplot: function (evalResult, input_group, input_model) 
#>     getHeatmap: function (evalSummary) 
#>     getLineplot: function (evalResult, order = NULL, metricVariable) 
#>     getScatterplot: function (evalResult, variables) 
#>     initialize: function (evalResult = NULL) 
#>     metadata: NULL