Description Usage Arguments Details Value Author(s) References See Also Examples
Derives the matrix of symmetric paths (double-headed arrows) \mathbf{S} using the Reticular Action Model (RAM) notation.
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A |
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C |
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check |
Logical.
If |
... |
... |
exe |
Logical.
If |
R |
Logical.
If |
format |
Character string.
Only used when |
simplify |
Logical. Simplify symbolic results. |
The matrix of symmetric paths (double-headed arrows) \mathbf{S} as a function of Reticular Action Model (RAM) matrices is given by
\mathbf{S} = ≤ft( \mathbf{I} - \mathbf{A} \right) \mathbf{C} ≤ft( \mathbf{I} - \mathbf{A} \right)^{\mathsf{T}} \\ = \mathbf{E}^{-1} \mathbf{C} ≤ft( \mathbf{E}^{-1} \right)^{\mathsf{T}}
where
\mathbf{A}_{t \times t} represents asymmetric paths (single-headed arrows), such as regression coefficients and factor loadings,
\mathbf{C}_{t \times t} represents the model-implied variance-covariance matrix, and
\mathbf{I}_{t \times t} represents an identity matrix.
\mathbf{S} = \mathbf{E}^{-1} \mathbf{C} ≤ft( \mathbf{E}^{-1} \right)^{\mathsf{T}}
Ivan Jacob Agaloos Pesigan
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. https://doi.org/10.1111/j.2044-8317.1984.tb00802.x
Other RAM matrices functions:
C()
,
Expectations()
,
E()
,
IminusA()
,
M()
,
RAMScaled()
,
g()
,
u()
,
v()
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 | # Numeric -----------------------------------------------------------
# This is a numerical example for the model
# y = alpha + beta * x + e
# y = 0 + 1 * x + e
#--------------------------------------------------------------------
A <- matrixR::ZeroMatrix(3)
A[1, ] <- c(0, 1, 1)
C <- matrix(
data = c(
1.25, 0.25, 1.00,
0.25, 0.25, 0.00,
1.00, 0.00, 1.00
),
nrow = dim(A)[1]
)
colnames(A) <- rownames(A) <- c("y", "x", "e")
S(A, C)
# Symbolic ----------------------------------------------------------
# This is a symbolic example for the model
# y = alpha + beta * x + e
# y = 0 + 1 * x + e
#--------------------------------------------------------------------
A <- matrixR::ZeroMatrix(3)
A[1, ] <- c(0, "beta", 1)
C <- matrix(
data = c(
"sigmax2*beta^2+sigmae2", "sigmax2*beta", "sigmae2",
"sigmax2*beta", "sigmax2", 0,
"sigmae2", 0, "sigmae2"
),
nrow = dim(A)[1]
)
S(Ryacas::ysym(A), C)
S(Ryacas::ysym(A), C, format = "str")
S(Ryacas::ysym(A), C, format = "tex")
S(Ryacas::ysym(A), C, R = TRUE)
# Assigning values to symbols
beta <- 1
sigmax2 <- 0.25
sigmae2 <- 1
S(Ryacas::ysym(A), C)
S(Ryacas::ysym(A), C, format = "str")
S(Ryacas::ysym(A), C, format = "tex")
S(Ryacas::ysym(A), C, R = TRUE)
eval(S(Ryacas::ysym(A), C, R = TRUE))
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