CFA_data: CFA example data

In gspcr: Generalized Supervised Principal Component Regression

CFA example data

Description

Contains a data set used to develop and test the main features of the gspcr package. The data contains 50 predictors generated based on true number of principal components.

Format

CFA_data is a list containing two objects:

• X: A data.frame with 5000 rows (observations) and 30 columns (possible predictors.) This data was generated based on a CFA model describing 10 independent latent variables measured by 3 items each, and a factor loading matrix describing simple structure.

• y: A numeric vector of length 1000. This variable was genearted as a linear combination of 5 latent variables used to generate X.

Details

A supervised PCA approach should identify that only 5 components are useful for the prediction of y and that only the first 15 variables should be used to compute them.

Examples

# Check out the first 6 rows of the predictors
head(CFA_data$X)

# Check out first 6 elements of the dependent variable
head(CFA_data$y)

gspcr documentation built on May 29, 2024, 2:44 a.m.