| lm_plot.4way |
Create a Four-Panel Regression Assumptions Plot |
| lm_plot.ac |
Plot Residuals vs. Observation Order (Autocorrelation Check) |
| lm_plot.df |
Augment Model Data for Diagnostic Plots |
| lm_plot.fit |
Plot Observed vs. Fitted Values to Check Linearity |
| lm_plot.infl |
Plot Leverage vs. Fitted Values to Visualize Inflential Observations |
| lm_plot.lev |
Plot Standard Residuals vs. Leverage with Cook's Distance Contours |
| lm_plot.parms |
Set or Retrieve Default Plot Parameters for Model Diagnostic Plots |
| lm_plot.qq |
Q-Q Plot of Residuals to Assess Normality |
| lm_plot.var |
Plot Residuals vs. Fitted Values to Assess Homoskedasticity |
| outlier |
Identify Outliers Using Boxplot Heuristic |
| print.sumry.lm |
Print a 'sumry' Summarization for Linear Model Objects |
| print.sumry.regsubsets |
Print Method for Best Subset Selection ('regsubsets') Objects |
| print.table.sumry.lm |
Print a Table from Linear Model Summary |
| sumry |
Summary Descriptive Statistics for BAQM |
| sumry.default |
Summary Descriptive Statistics for List or Data Frame |
| sumry.lm |
Method 'sumry' to Summarize Linear Model ('lm') Objects |
| sumry.regsubsets |
Summary for Subset Selection ('regsubsets') Objects |
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