sem_obs: Structural Equations with Observed Variables

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

Description

Model-implied variance-covariance matrix (\boldsymbol{Σ} ≤ft( θ \right)) for structural equations with observed variables.

Usage

sem_obs(BE, I, PS)

Arguments

BE	\mathbf{B}_{m \times m} coefficient matrix.
I	\mathbf{I}_{m \times m} identity matrix.
PS	\boldsymbol{Ψ}_{m \times m} variance-covariance matrix (variance-covariance of exogenous variables, and residual variance-covariance of endogenous variables).

Details

\boldsymbol{Σ} ≤ft( θ \right) = ≤ft( \mathbf{I} - \mathbf{B} \right)^{-1} \boldsymbol{Ψ} ≤ft[ ≤ft( \mathbf{I} - \mathbf{B} \right)^{-1} \right]^{T}

Value

Returns the model-implied variance-covariance matrix (\boldsymbol{Σ} ≤ft( \boldsymbol{θ} \right)) derived from the \mathbf{B}, \mathbf{I}, and \boldsymbol{Ψ} matrices.

Author(s)

Ivan Jacob Agaloos Pesigan

References

Bollen, K. A. (1989). Structural equations with latent variables. New York: Wiley.

Jöreskog, K. G., & Sörbom, D. (1996). Lisrel 8: User's reference guide (2nd ed.). Scientific Software.

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_m(), ram_s(), ram(), sem_fa(), sem_lat()

Examples

BE <- matrix(
  data = c(
    0, 0.26^(1 / 2), 0,
    0, 0, 0.26^(1 / 2),
    0, 0, 0
  ),
  ncol = 3
)
PS <- F <- I <- diag(3)
PS[1, 1] <- 225
PS[2, 2] <- 166.5
PS[3, 3] <- 166.5
sem_obs(BE = BE, I = I, PS = PS)

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