Residual vs Fitted Values Plot
Plot to detect non-linearity, unequal error variances, and outliers.
The olsrr package provides following tools for teaching and learning OLS regression using R:
This document is a quickstart guide to the tools offered by olsrr. Other vignettes provide more details on specific topics:
Residual Diagnostics: Includes plots to examine residuals to validate OLS assumptions
Variable selection: Differnt variable selection procedures such as all possible regression, best subset regression, stepwise regression, stepwise forward regression and stepwise backward regression
Heteroskedasticity: Tests for heteroskedasticity include bartlett test, breusch pagan test, score test and f test
Measures of influence: Includes 10 different plots to detect and identify influential observations
Collinearity diagnostics: VIF, Tolerance and condition indices to detect collinearity and plots for assessing mode fit and contributions of variables
## Model Summary
## --------------------------------------------------------------
## R 0.914 RMSE 2.622
## R-Squared 0.835 Coef. Var 13.051
## Adj. R-Squared 0.811 MSE 6.875
## Pred R-Squared 0.771 MAE 1.858
## --------------------------------------------------------------
## RMSE: Root Mean Square Error
## MSE: Mean Square Error
## MAE: Mean Absolute Error
##
## ANOVA
## --------------------------------------------------------------------
## Sum of
## Squares DF Mean Square F Sig.
## --------------------------------------------------------------------
## Regression 940.412 4 235.103 34.195 0.0000
## Residual 185.635 27 6.875
## Total 1126.047 31
## --------------------------------------------------------------------
##
## Parameter Estimates
## ----------------------------------------------------------------------------------------
## model Beta Std. Error Std. Beta t Sig lower upper
## ----------------------------------------------------------------------------------------
## (Intercept) 27.330 8.639 3.164 0.004 9.604 45.055
## disp 0.003 0.011 0.055 0.248 0.806 -0.019 0.025
## hp -0.019 0.016 -0.212 -1.196 0.242 -0.051 0.013
## wt -4.609 1.266 -0.748 -3.641 0.001 -7.206 -2.012
## qsec 0.544 0.466 0.161 1.166 0.254 -0.413 1.501
## ----------------------------------------------------------------------------------------In the presence of interaction terms in the model, the predictors are scaled and centered before computing the standardized betas. ols_regress() will detect interaction terms automatically but in case you have created a new variable instead of using the inline function *, you can indicate the presence of interaction terms by setting iterm to TRUE.
Plot to detect non-linearity, unequal error variances, and outliers.
DFBETAs measure the difference in each parameter estimate with and without the influential observation. dfbetas_panel creates plots to detect influential observations using DFBETAs.
Plot to detect non-linearity, influential observations and outliers.
Breusch Pagan test is used to test for herteroskedasticity (non-constant error variance). It tests whether the variance of the errors from a regression is dependent on the values of the independent variables. It is a \(\chi^{2}\) test.
##
## Breusch Pagan Test for Heteroskedasticity
## -----------------------------------------
## Ho: the variance is constant
## Ha: the variance is not constant
##
## Data
## -------------------------------
## Response : mpg
## Variables: fitted values of mpg
##
## Test Summary
## ---------------------------
## DF = 1
## Chi2 = 1.429672
## Prob > Chi2 = 0.231818## Tolerance and Variance Inflation Factor
## ---------------------------------------
## Variables Tolerance VIF
## 1 disp 0.1252279 7.985439
## 2 hp 0.1935450 5.166758
## 3 wt 0.1445726 6.916942
## 4 qsec 0.3191708 3.133119
##
##
## Eigenvalue and Condition Index
## ------------------------------
## Eigenvalue Condition Index intercept disp hp
## 1 4.721487187 1.000000 0.000123237 0.001132468 0.001413094
## 2 0.216562203 4.669260 0.002617424 0.036811051 0.027751289
## 3 0.050416837 9.677242 0.001656551 0.120881424 0.392366164
## 4 0.010104757 21.616057 0.025805998 0.777260487 0.059594623
## 5 0.001429017 57.480524 0.969796790 0.063914571 0.518874831
## wt qsec
## 1 0.0005253393 0.0001277169
## 2 0.0002096014 0.0046789491
## 3 0.0377028008 0.0001952599
## 4 0.7017528428 0.0024577686
## 5 0.2598094157 0.9925403056Build regression model from a set of candidate predictor variables by entering and removing predictors based on p values, in a stepwise manner until there is no variable left to enter or remove any more.
##
## Stepwise Selection Summary
## ------------------------------------------------------------------------------------------
## Added/ Adj.
## Step Variable Removed R-Square R-Square C(p) AIC RMSE
## ------------------------------------------------------------------------------------------
## 1 liver_test addition 0.455 0.444 62.5120 771.8753 296.2992
## 2 alc_heavy addition 0.567 0.550 41.3680 761.4394 266.6484
## 3 enzyme_test addition 0.659 0.639 24.3380 750.5089 238.9145
## 4 pindex addition 0.750 0.730 7.5370 735.7146 206.5835
## 5 bcs addition 0.781 0.758 3.1920 730.6204 195.4544
## ------------------------------------------------------------------------------------------Build regression model from a set of candidate predictor variables by removing predictors based on Akaike Information Criteria, in a stepwise manner until there is no variable left to remove any more.
# stepwise aic backward regression
model <- lm(y ~ ., data = surgical)
k <- ols_step_backward_aic(model)
k##
##
## Backward Elimination Summary
## ---------------------------------------------------------------------------
## Variable AIC RSS Sum Sq R-Sq Adj. R-Sq
## ---------------------------------------------------------------------------
## Full Model 736.390 1825905.713 6543614.824 0.78184 0.74305
## alc_mod 734.407 1826477.828 6543042.709 0.78177 0.74856
## gender 732.494 1829435.617 6540084.920 0.78142 0.75351
## age 730.620 1833716.447 6535804.090 0.78091 0.75808
## ---------------------------------------------------------------------------