Package index • BAQM

All functions

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 Studentized Residuals vs. Sequence with Influence Identification

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(<summary.lm>): Print a Summary for Linear Model Objects

print(<summary.regsubsets>): Print Summary for Subset Selection (regsubsets) Objects

print(<table.summary.lm>): Print a Table from Linear Model Summary

stat_desc(): Summary Descriptive Statistics for List or Data Frame

summary(<lm>): Summary Method for Linear Model (lm) Objects