Multivariate analysis (MVA) is based on the statistical principle of multivariate statistics, which involves observation and analysis of more than one statistical outcome variable at a time.
Partial least squares regression (PLS regression) is a particular type of MVA. PLS provides quantitative multivariate modelling methods, with inferential possibilities similar to multiple regression, t-tests and ANOVA. It reduces the predictors to a smaller set of uncorrelated components and performs least squares regression on these components.
Regress and analyze
- Open a table
- Run from the top menu:
ML | Analyze | Multivariate Analysis...
- Select a table that contains features
- Select feature columns
- Select column with sample names
- Select prediction column
- Select the number of extracted PLS components
- Press OK
The loadings plot shows correlations between variables. Comparing the correlation loadings to the scores shows how the variables relate to the observations.
Reference vs. predicted
A scatter plot and a regression line indicate how the model fits and predicts.
The scores plot shows object similarities and dissimilarities.
A bar chart illustrates features influence on the predicted value.