CoFM (version 1.1.4)

PPC_basic: Perform Basic Projected PCA (PPC) Estimation

Description

This function performs Projected Principal Component Analysis (PPC) to estimate factor loadings and specific variances. It projects the data onto a specific subspace before performing eigen decomposition. Unlike PPC_CoFM, this function does not calculate error metrics against true parameters.

Usage

PPC_basic(data, m)

Value

A list containing:

Apro

Estimated projected factor loadings matrix (p x m).

Dpro

Estimated projected uniquenesses vector (p).

Sigmahatpro

The covariance matrix of the projected data.

Arguments

data

A matrix or data frame of input data (n x p).

m

Integer. The number of principal components (factors) to extract.

Examples

# Examples should be fast and reproducible for CRAN checks
set.seed(123)

# 1. Generate toy data using CoFM
sim <- CoFM(n = 200, p = 6, m = 2, type = "Clayton", param = 2.0)
obs_data <- sim$data

# 2. Apply PPC method (extract 2 factors)
fit <- PPC_basic(data = obs_data, m = 2)

# 3. Inspect estimates
head(fit$Apro)
fit$Dpro