ram_m: RAM M

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

Description

Mean Structure (\mathbf{M} vector) using the Reticular Action Model notation.

Usage

ram_m(A, F, I, mu)

Arguments

A	Asymmetric paths, such as regression coefficients and factor loadings.
F	Filter matrix used to select the observed variables.
I	Identity matrix.
mu	Vector of expected values (\boldsymbol{μ}).

Details

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

Value

Returns the mean structure (\mathbf{M} vector) derived from the \mathbf{A}, \mathbf{F}, \mathbf{I}, matrices and \boldsymbol{μ} 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_mu(), 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)
mu <- c(100, 100, 100)
ram_m(A = A, F = F, I = I, mu = mu)

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