Principal component analysis (PCA)
Multivariate analysis · Method and input reference
Numeric complete observations. PCA standardizes variables; EFA uses maximum likelihood without rotation. CFA accepts measurement lines only. Discriminant analysis uses first column as class; reports leave-one-out accuracy. Canonical correlation splits columns into two sets. Synthetic example: 180 people with three correlated continuous health measures on different scales.
Open calculator · Compare methods in the decision matrix
When to consider it
- Summarize numeric variation using orthogonal principal components.
Assumptions and scope
- Observations must be independent between subjects; repeated rows from one subject are not independent samples.
- Use the outcome and predictor coding required by the calculator; review missing data and model diagnostics.
Required inputs
- Data matrix (one observation per line; comma-separated columns) — Numeric observations; preserve the row and column structure described above.
- Components / factors — Enter the method parameter described above.
Review missing values and data coding before running. Sample inputs are synthetic teaching examples.
Implementation
Lifescio validates inputs and formats results; listed functions are the principal calculation routines, not an exhaustive transitive dependency list.
- scikit-learn 1.7.2: sklearn.decomposition.PCA
These are pinned library versions; saved results record the versions actually used at runtime.