Effects coding method in CFA
pehkawn
Joined: 05/24/2020
I am trying to run a CFA where one latent variable is regressed on four manifest variables. I am trying to constrain the pattern coefficients ($\lambda$) mean to equal one, according to [Kline's (2015, p. 200)](https://www.guilford.com/books/Principles-and-Practice-of-Structural-Equation-Modeling/Rex-Kline/9781462523344) *effects coding method*:
$$ \frac{\lambda_1 + \lambda_2 + \lambda_3 + \lambda_4}{4} = 1 $$
Below is a code example. I tried adding an `mxConstraint()` function in `mxModel()` but the syntax is not correct. How can I apply the constraints properly?
library(OpenMx)
data <- mxData(observed = mydata, type="raw")
# residual variances
res_var <- mxPath(
from = paste0("x", 1:4),
arrows = 2,
free = TRUE,
values = 1,
labels = paste0("e_x", 1:4)
)
# latent variance
lat_var <- mxPath(
from = "L",
arrows = 2,
free = TRUE,
values = 1,
labels = c("var_L")
)
# factor loadings
fac_loads <- mxPath(
from = "L",
to = paste0("x", 1:4),
free = c(TRUE, TRUE, TRUE, TRUE),
values = 1,
arrows = 1,
label = paste0("L__x", 1:4)
)
# means
means <- mxPath(
from = "one",
to = c(L, paste0("x", 1:4),
arrows = 1,
free = FALSE,
values = 0,
labels = c(paste0("mean_", student_sci_ach), "mean_stSCI")
)
model <- mxModel(
"My Model",
type = "RAM",
manifestVars = paste0("x", 1:4),
latentVars = "L",
data,
var, lat_var,
fac_loads,
means,
# I tried defining a constraint here, but this obviously doesn't work.
mxConstraint(mean(paste0("L__x", 1:4)) == 1)
)
results <- mxRun(model)
summary(results)
<\blockcode>
try something like this
In an MxConstraint, both sides of the comparator (
=in this case) have to be valid MxAlgebra-like expressions, evaluable in the MxModel's namespace; mere character strings aren't accepted. Try something like this:model <- mxModel(
"My Model",
type = "RAM",
manifestVars = paste0("x", 1:4),
latentVars = "L",
data,
var, lat_var,
fac_loads,
means,
mxMatrix(type="Full",nrow=4,ncol=1,free=T,labels=paste0("L__x", 1:4),name="Lmat"),
mxConstraint(mean(Lmat) == 1)
)
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In reply to try something like this by AdminRobK
Thanks, that did the trick!
That worked after I added an extra line `model <- omxAssignFirstParameters(model)` to copy the values in the `A` matrix into `Lmat`.
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In reply to Thanks, that did the trick! by pehkawn
Yep
That is one way to avoid trying to start parameters at different values when they appear in different places in the model. Good solution, although it's a bit less controllable than manually setting parameters to the same value wherever they appear, in this case I think changing the mxMatrix line to
mxMatrix(type="Full",nrow=4,ncol=1,free=T, values=1, labels=paste0("L__x", 1:4),name="Lmat"),
Use of omxAssignFirstParameters() means that you don't actually know which starting value was used. Sometimes that's ok, but, (e.g., to debug bad starting values) it's not always ideal.
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