CoFM (version 1.1.4)

FanPC_basic: Perform Basic FanPC Factor Analysis

Description

This function performs factor analysis using a principal-component (FanPC) approach. It estimates the factor loading matrix and uniquenesses from the correlation matrix of the input data. Unlike FanPC_CoFM, this function does not calculate error metrics against true parameters, making it suitable for simple estimation tasks.

Usage

FanPC_basic(data, m)

Value

A list containing:

AF

Estimated factor loadings matrix (p x m).

DF

Estimated uniquenesses vector (p).

SigmahatF

The correlation matrix of the input 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 synthetic data using CoFM (toy example)
sim <- CoFM(n = 200, p = 6, m = 2, type = "Clayton", param = 2.0)
obs_data <- sim$data

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

# 3. Inspect estimates
head(fit$AF)  # Estimated loadings
fit$DF        # Estimated uniquenesses