PPC2_TFM {TFM} | R Documentation |
Apply the PPC method to the Truncated factor model
Description
This function performs Projected Principal Component Analysis (PPC) on a given data set to reduce dimensionality. It calculates the estimated values for the loadings, specific variances, and the covariance matrix.
Usage
PPC2_TFM(data, m, A, D)
Arguments
data |
The total data set to be analyzed. |
m |
The number of principal components. |
A |
The true factor loadings matrix. |
D |
The true uniquenesses matrix. |
Value
A list containing:
Ap2 |
Estimated factor loadings. |
Dp2 |
Estimated uniquenesses. |
MSESigmaA |
Mean squared error for factor loadings. |
MSESigmaD |
Mean squared error for uniquenesses. |
LSigmaA |
Loss metric for factor loadings. |
LSigmaD |
Loss metric for uniquenesses. |
Examples
## Not run:
library(SOPC)
library(relliptical)
library(MASS)
results <- PPC2_TFM(data, m, A, D)
print(results)
## End(Not run)
[Package TFM version 0.5.2 Index]