Comprehensive missing data analysis and multiple imputation using mice and ggmice packages. Analyze missing data patterns, perform multiple imputation by chained equations (MICE), and visualize imputation results. Includes diagnostic plots for convergence assessment, comparison of observed vs imputed data, and quality evaluation. Perfect for clinical research data preprocessing where missing data is common and proper handling is critical for valid statistical inference.
Usage
missingdata(
data,
analysis_vars,
analysis_type = "pattern",
n_imputations = 5,
max_iterations = 5,
imputation_method = "auto",
seed_value = 123,
convergence_check = TRUE,
show_pattern_plot = TRUE,
show_pattern_table = TRUE,
show_correlation_plot = TRUE,
show_flux_plot = TRUE,
show_trace_plot = TRUE,
show_density_plot = TRUE,
show_stripplot = TRUE,
show_scatterplot = FALSE,
show_imputation_summary = TRUE,
show_interpretation = TRUE
)Arguments
- data
The data as a data frame.
- analysis_vars
Variables to include in the missing data analysis and imputation. Can include numeric, factor, and ID variables.
- analysis_type
Type of missing data analysis to perform. Pattern analysis explores missing data structure, imputation performs MICE, complete does both.
- n_imputations
Number of imputed datasets to generate using MICE algorithm. Typically 5-20 imputations are sufficient for most applications.
- max_iterations
Maximum number of MICE iterations per imputation. Default is 5, increase if convergence issues occur.
- imputation_method
Default imputation method. Auto-select chooses appropriate methods based on variable types. PMM is robust for continuous variables.
- seed_value
Random seed for reproducible imputation results.
- convergence_check
Perform convergence diagnostics to ensure MICE algorithm stability.
- show_pattern_plot
Display visual representation of missing data patterns.
- show_pattern_table
Display tabular summary of missing data patterns.
- show_correlation_plot
Display correlation matrix between incomplete variables.
- show_flux_plot
Display influx and outflux of missing data patterns.
- show_trace_plot
Display convergence trace plots for imputation diagnostics.
- show_density_plot
Compare distributions of observed vs imputed data.
- show_stripplot
Display strip plots comparing observed and imputed values.
- show_scatterplot
Display scatter plots of observed vs imputed data relationships.
- show_imputation_summary
Display summary statistics and information about the imputation process.
- show_interpretation
Display guidance on missing data analysis and imputation best practices for clinical research.
Value
A results object containing:
results$todo | a html | ||||
results$pattern_plot | an image | ||||
results$pattern_table | a html | ||||
results$correlation_plot | an image | ||||
results$flux_plot | an image | ||||
results$trace_plot | an image | ||||
results$density_plot | an image | ||||
results$stripplot | an image | ||||
results$scatterplot | an image | ||||
results$imputation_summary | a html | ||||
results$interpretation | a html |