Comprehensive feature quality assessment for clinical and pathology research data. Provides essential data quality control including missing data analysis, outlier detection, distribution analysis, correlation assessment, and variance evaluation. Implements multiple quality metrics with clinical interpretation and actionable recommendations. Critical for ensuring data integrity before statistical analysis and meeting regulatory standards for clinical research.
Usage
featurequality(
data,
features,
group_var = NULL,
analysis_scope = "comprehensive",
missing_data_analysis = TRUE,
distribution_analysis = TRUE,
outlier_detection = TRUE,
outlier_method = "multiple",
outlier_threshold = 3,
correlation_analysis = TRUE,
correlation_threshold = 0.8,
variance_analysis = TRUE,
low_variance_threshold = 0.01,
normality_testing = TRUE,
normality_method = "multiple",
skewness_analysis = TRUE,
feature_importance = FALSE,
importance_method = "random_forest",
data_transformation = TRUE,
quality_score = TRUE,
detailed_plots = TRUE,
plot_distributions = TRUE,
plot_correlations = TRUE,
plot_outliers = TRUE,
plot_missing = TRUE,
export_recommendations = FALSE,
clinical_context = TRUE,
batch_processing = TRUE,
confidence_level = 0.95,
random_seed = 123
)Arguments
- data
.
- features
.
- group_var
.
- analysis_scope
.
- missing_data_analysis
.
- distribution_analysis
.
- outlier_detection
.
- outlier_method
.
- outlier_threshold
.
- correlation_analysis
.
- correlation_threshold
.
- variance_analysis
.
- low_variance_threshold
.
- normality_testing
.
- normality_method
.
- skewness_analysis
.
- feature_importance
.
- importance_method
.
- data_transformation
.
- quality_score
.
- detailed_plots
.
- plot_distributions
.
- plot_correlations
.
- plot_outliers
.
- plot_missing
.
- export_recommendations
.
- clinical_context
.
- batch_processing
.
- confidence_level
.
- random_seed
.
Value
A results object containing:
results$instructions | a html | ||||
results$progress | a html | ||||
results$quality_summary | a table | ||||
results$missing_data_analysis | a table | ||||
results$distribution_analysis | a table | ||||
results$outlier_analysis | a table | ||||
results$correlation_analysis | a table | ||||
results$variance_analysis | a table | ||||
results$normality_analysis | a table | ||||
results$feature_importance | a table | ||||
results$transformation_recommendations | a table | ||||
results$overall_recommendations | a html | ||||
results$clinical_interpretation | a html | ||||
results$distribution_plot | an image | ||||
results$correlation_plot | an image | ||||
results$outlier_plot | an image | ||||
results$missing_plot | an image | ||||
results$quality_dashboard | an image |
Tables can be converted to data frames with asDF or as.data.frame. For example:
results$quality_summary$asDF
as.data.frame(results$quality_summary)