Enhanced Statistical Visualization

Usage

jggstats(
  data,
  analysis_type = "ggcoef_model",
  dependent_var,
  independent_vars,
  grouping_var,
  weight_var,
  model_formula = "",
  model_type = "lm",
  family = "gaussian",
  confidence_level = 0.95,
  likert_levels = 5,
  show_intercept = FALSE,
  sort_coefficients = TRUE,
  standardized = FALSE,
  show_statistics = TRUE,
  show_significance = TRUE,
  color_palette = "default",
  theme_style = "default",
  plot_title = "",
  plot_subtitle = "",
  x_label = "",
  y_label = "",
  facet_var,
  facet_type = "wrap",
  show_model_summary = TRUE,
  show_interpretation = TRUE,
  output_format = "plot_only"
)

Arguments

data: .
analysis_type: .
dependent_var: Main outcome variable for analysis
independent_vars: Predictor variables for model
grouping_var: Variable for grouping/stratification
weight_var: Variable containing survey weights
model_formula: Custom model formula (optional)
model_type: .
family: .
confidence_level: Confidence level for intervals
likert_levels: Number of levels in Likert scale
show_intercept: Include intercept in coefficient plots
sort_coefficients: Sort coefficients by magnitude
standardized: Display standardized coefficients
show_statistics: Display statistical values
show_significance: Add significance stars to plot
color_palette: .
theme_style: .
plot_title: .
plot_subtitle: .
x_label: .
y_label: .
facet_var: Variable for creating plot facets
facet_type: .
show_model_summary: Display model fit statistics
show_interpretation: Generate interpretation text
output_format: .

Value

A results object containing:

`results$plot`					an image
`results$modeltab`					a table
`results$summary`					a table
`results$interpretation`					a html

Tables can be converted to data frames with asDF or as.data.frame. For example:

results$modeltab$asDF

as.data.frame(results$modeltab)