ram_mu: RAM mu

In jeksterslabds/jeksterslabRds: Jekster's Lab Data Science and Statistics

Description

Model-implied \boldsymbol{μ} vector using the Reticular Action Model notation.

Usage

ram_mu(A, F, I, M)

Arguments

A	Asymmetric paths, such as regression coefficients and factor loadings.
F	Filter matrix used to select the observed variables.
I	Identity matrix.
M	Vector of means and intercepts.

Details

\boldsymbol{μ} ≤ft( \boldsymbol{θ} \right) = \mathbf{F} ≤ft( \mathbf{I} - \mathbf{A} \right)^{-1} \mathbf{M}

Value

Returns the expected values (\boldsymbol{μ}) derived from the \mathbf{A}, \mathbf{F}, \mathbf{I}, matrices and \mathbf{M} vector.

Author(s)

Ivan Jacob Agaloos Pesigan

References

McArdle, J. J. (2013). The development of the RAM rules for latent variable structural equation modeling. In A. Maydeu-Olivares & J. J. McArdle (Eds.), Contemporary Psychometrics: A festschrift for Roderick P. McDonald (pp. 225–273). Lawrence Erlbaum Associates.

McArdle, J. J., & McDonald, R. P. (1984). Some algebraic properties of the Reticular Action Model for moment structures. British Journal of Mathematical and Statistical Psychology, 37(2), 234–251.

See Also

Other SEM notation functions: eqs_mu(), eqs(), lisrel_fa(), lisrel_obs_xy(), lisrel_obs_yx(), lisrel_obs_yy(), lisrel_obs(), lisrel_xx(), lisrel_xy(), lisrel_yx(), lisrel_yy(), lisrel(), ram_m(), ram_s(), ram(), sem_fa(), sem_lat(), sem_obs()

Examples

A <- matrix(
  data = c(
    0, 0.26^(1 / 2), 0,
    0, 0, 0.26^(1 / 2),
    0, 0, 0
  ),
  ncol = 3
)
F <- I <- diag(3)
M <- c(100, 49.0098049, 49.0098049)
ram_mu(A = A, F = F, I = I, M = M)

jeksterslabds/jeksterslabRds documentation built on July 16, 2020, 3:41 p.m.