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Residual Plots

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A residual plot is a statistical representation of data used to analyze correlation and regression results. It helps verify the requirements for drawing specific conclusions about correlation and regression. To obtain the residual plot, first, the residual for each data value is calculated, which is simply the vertical distance between the observed and the predicted value obtained from the regression equation.
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The vertical distance between the actual value of y and the estimated value of y. In other words, it measures the vertical distance between the actual data point and the predicted point on the line
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Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery

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Published on: September 27, 2024

PResiduals: An R Package for Residual Analysis Using Probability-Scale Residuals.

Qi Liu¹, Bryan Shepherd², Chun Li³

¹Merck & CO., Inc.

Journal of Statistical Software

|October 7, 2024

Summary

This summary is machine-generated.

The PResiduals R package offers probability-scale residuals for robust model diagnostics across diverse data types. This tool enhances statistical analysis by providing reliable methods for model evaluation and association testing.

Keywords:

association correlation covariate-adjustment diagnostics rank statistics residual

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Area of Science:

Statistical modeling
Data analysis software

Background:

Traditional residual analysis methods have limitations with various outcome types and models.
Existing diagnostics may not be applicable in complex statistical scenarios.

Purpose of the Study:

Introduce the PResiduals R package for advanced residual analysis.
Provide a versatile tool for model diagnostics and association testing.

Main Methods:

Utilize probability-scale residuals, applicable to a wide range of outcome types and models.
Implement tests for conditional associations and covariate adjustment for Spearman's rank correlation.
Develop methods that are robust and efficient for orderable variables without requiring score assignment or transformation.

Main Results:

The PResiduals package offers a flexible approach to residual analysis.
Probability-scale residuals are well-defined even when other residuals fail.
The package facilitates robust and efficient conditional association tests.

Conclusions:

The PResiduals R package provides a valuable tool for statistical modeling and diagnostics.
Its probability-scale residuals offer advantages in robustness and applicability.
The package facilitates advanced data analysis for researchers.