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olsrr offers tools for detecting violation of standard regression assumptions. Here we take a look at residual diagnostics. The standard regression assumptions include the following about residuals/errors:
Graph for detecting violation of normality assumption.
Test for detecting violation of normality assumption.
## -----------------------------------------------
## Test Statistic pvalue
## -----------------------------------------------
## Shapiro-Wilk 0.9366 0.0600
## Kolmogorov-Smirnov 0.1152 0.7464
## Cramer-von Mises 2.8122 0.0000
## Anderson-Darling 0.5859 0.1188
## -----------------------------------------------
Correlation between observed residuals and expected residuals under normality.
## [1] 0.970066
It is a scatter plot of residuals on the y axis and fitted values on the x axis to detect non-linearity, unequal error variances, and outliers.
Characteristics of a well behaved residual vs fitted plot:
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