PLS regression graphs

Use the table below to learn more about PLS graphs.

PLS Graph	Definition	Use to...
Model selection plot	Scatterplot of the R² and predicted R² values as a function of the number of components. The vertical line indicates the number of components in the optimal model.	Compare the modeling and predictive powers of models with different numbers of components.
Response plot	Scatterplot of the fitted and cross-validated responses versus the actual responses.	Show how well the model fits and predicts. Large differences in fitted and cross-validated values identify leverage points.
Coefficient plot	Projected scatterplot of the unstandardized regression coefficients.	View the sign and the magnitude of the relationship between predictors and responses.
Standardized coefficient plot	Projected scatterplot of the standardized regression coefficients	View the sign and the magnitude of the relationship between predictors and responses when predictors are not on the same scale.
Distance plot	Scatterplot of each observation's distance from the x-model and distance from y-model.	Identify leverage points and outliers.
Residual histogram	Histogram of the standardized residuals.	Verify the normality of your residuals. Histograms should show a bell-shaped distribution.
Residual normal probability plot	Scatterplot of the standardized residuals versus the normal scores.	Verify the normality of your residuals. Points should follow a straight line.
Residual versus fit plot	Scatterplot of the standardized residuals versus the fitted responses.	Identify outliers and check for patterns in the residuals.
Residual versus leverage plot	Scatterplot of the standardized residuals versus leverages.	Identify outliers and leverage points at the same time.
Residual fourpack	Arrangement of histogram of the residuals, normal probability plot of the residuals, residual versus fit plot, and residual versus order plot on one page.	View residual plots at the same time.
Score plot	Scatterplot of the x-scores from the first and second components.	Display the overall arrangement of the data using the first two components to identify leverage points or clusters of points.
3D score plot	3D scatterplot of the x-scores from the first, second, and third components.	Display the overall arrangement of the data using the first three components to identify leverage points or clusters of points.
Loading plot	Connected scatterplot of the x-loadings from the first and second components.	Display the correlation between the loadings of each predictor on the first and second components. Compare the importance of predictors to the model.
Residual X plot	Connected scatterplot of the x-residuals, in which each line represents an observation and has as many points as predictors.	Identify observations or predictors that are not well explained by the model.
Calculated X plot	Connected scatterplot of the x-calculated values, in which each line represents an observation and has as many points as predictors.	Identify observations or predictors that are not well explained by the model.