Multilevel functional principal component analysis for clustering
A multilevel functional principal component analysis for performing clustering analysis
MFPCA(y, M = NULL, J = NULL, N = NULL)
y |
A data matrix containing functional responses. Each row contains measurements from a function at a set of grid points, and each column contains measurements of all functions at a particular grid point |
M |
Number of countries |
J |
Number of functional responses in each country |
N |
Number of grid points per function |
K1 |
Number of components at level 1 |
K2 |
Number of components at level 2 |
K3 |
Number of components at level 3 |
lambda1 |
A vector containing all level 1 eigenvalues in non-increasing order |
lambda2 |
A vector containing all level 2 eigenvalues in non-increasing order |
lambda3 |
A vector containing all level 3 eigenvalues in non-increasing order |
phi1 |
A matrix containing all level 1 eigenfunctions. Each row contains an eigenfunction evaluated at the same set of grid points as the input data. The eigenfunctions are in the same order as the corresponding eigenvalues |
phi2 |
A matrix containing all level 2 eigenfunctions. Each row contains an eigenfunction evaluated at the same set of grid points as the input data. The eigenfunctions are in the same order as the corresponding eigenvalues |
phi3 |
A matrix containing all level 3 eigenfunctions. Each row contains an eigenfunction evaluated at the same set of grid points as the input data. The eigenfunctions are in the same order as the corresponding eigenvalues |
scores1 |
A matrix containing estimated level 1 principal component scores. Each row corresponds to the level 1 scores for a particular subject in a cluster. The number of rows is the same as that of the input matrix |
scores2 |
A matrix containing estimated level 2 principal component scores. Each row corresponds to the level 2 scores for a particular subject in a cluster. The number of rows is the same as that of the input matrix |
scores3 |
A matrix containing estimated level 3 principal component scores. Each row corresponds to the level 3 scores for a particular subject in a cluster. The number of rows is the same as that of the input matrix |
mu |
A vector containing the overall mean function |
eta |
A matrix containing the deviation from overall mean function to country-specific mean function. The number of rows is the number of countries |
Rj |
Common trend |
Uij |
Country-specific mean function |
Chen Tang, Yanrong Yang and Han Lin Shang
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