Open YichiZhang2024 opened 2 years ago
Changes for multidimensional analysis:
Assume we have n factors measured by m items.
weights_item
weights_latent
alpha_r
alpha_f
psi_r
psi_f
lambda_r
lambda_f
nu_r
nu_f
Theta_r
Theta_f
Illustrative example:
lambda_matrix <- matrix(c(.3, .5, 0, 0, 0, 0, 0, .9, .7, .7), ncol = 2, nrow = 5) PartInvMulti_we(propsel = .05, weights_item = c(1/4, 1/4, 1/6, 1/6, 1/6), weights_latent = c(0.5, 0.5), alpha_r = c(0, 0), alpha_f = c(-0.3, 0.1), psi_r = matrix(c(1, 0.5, 0.5, 1), nrow = 2), lambda_r = lambda_matrix, nu_r = c(.225, .025, .010, .240, .125), nu_f = c(.225, -.05, .240, -.025, .125), Theta_r = diag(1, 5), Theta_f = diag(c(1, .95, .80, .75, 1)))
Changes for multidimensional analysis:
Assume we have n factors measured by m items.
weights_item
) is a vector with length m.weights_latent
) is a vector with length n.alpha_r
andalpha_f
) is now a vector instead of a single number. Note these two parameters should have the same length (n).psi_r
andpsi_f
) is now a matrix instead of a single number. Note these two parameters should have the same dimension (n x n).lambda_r
andlambda_f
) are matrices instead of vectors. The dimension of these two parameters is m x n.nu_r
andnu_f
) are vectors with dimension m x 1.Theta_r
andTheta_f
) are matrices with dimension m x m.Illustrative example:
lambda_matrix <- matrix(c(.3, .5, 0, 0, 0, 0, 0, .9, .7, .7), ncol = 2, nrow = 5) PartInvMulti_we(propsel = .05, weights_item = c(1/4, 1/4, 1/6, 1/6, 1/6), weights_latent = c(0.5, 0.5), alpha_r = c(0, 0), alpha_f = c(-0.3, 0.1), psi_r = matrix(c(1, 0.5, 0.5, 1), nrow = 2), lambda_r = lambda_matrix, nu_r = c(.225, .025, .010, .240, .125), nu_f = c(.225, -.05, .240, -.025, .125), Theta_r = diag(1, 5), Theta_f = diag(c(1, .95, .80, .75, 1)))