Package index

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 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