Effects coding method in CFA

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)
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