sem | R Documentation |
It fits a structural equation model by creating a mxModel from a RAM object.
## Depreciated in the future
create.mxModel(model.name="sem", RAM=NULL, data=NULL,
Cov=NULL, means=NULL, numObs,
intervals.type=c("z", "LB"), startvalues=NULL,
replace.constraints=FALSE, mxModel.Args=NULL,
run=TRUE, silent=TRUE, ...)
sem(model.name="sem", RAM=NULL, data=NULL,
Cov=NULL, means=NULL, numObs,
intervals.type=c("z", "LB"), startvalues=NULL,
lbound=NULL, ubound=NULL, replace.constraints=FALSE,
mxModel.Args=NULL, run=TRUE, silent=TRUE, ...)
model.name |
A string for the model name in |
RAM |
A RAM object including a list of matrices of the model
returned from |
data |
A data frame or matrix of data. |
Cov |
A covariance matrix may also be used if |
means |
A named vector of means (options) if |
numObs |
If |
intervals.type |
Either |
startvalues |
A list of named starting values of the free parameters, e.g., list(a=1, b=2) |
lbound |
A list of lower bound of the free parameters. If it is
not provided, all free parameters are assumed |
ubound |
A list of upper bound of the free parameters. If it is
not provided, all free parameters are assumed |
replace.constraints |
Logical. If |
mxModel.Args |
A list of arguments passed to |
run |
Logical. If |
silent |
Logical. An argument is passed to either
|
... |
Further arguments will be passed to either
|
An object of class mxsem
when there are constraints with replace.constraints=TRUE
and intervals.type="LB"
, it returns an error because some parameters in the model are replaced with the new parameters in the constraints. However, the names of these new parameters are not captured in the CI object. Users are advised to use intervals.type="z"
before it is fixed.
Mike W.-L. Cheung <mikewlcheung@nus.edu.sg>
## Generate data
set.seed(100)
n <- 100
x <- rnorm(n)
y <- 0.5*x + rnorm(n, mean=0, sd=sqrt(1-0.5^2))
my.df <- data.frame(y=y, x=x)
## A regression model
model <- "y ~ x # Regress y on x
y ~ 1 # Intercept of y
x ~ 1 # Mean of x"
plot(model)
RAM <- lavaan2RAM(model, obs.variables=c("y", "x"))
my.fit <- sem(RAM=RAM, data=my.df)
summary(my.fit)
## A meta-analysis
model <- "f =~ 1*yi
f ~ mu*1 ## Average effect
f ~~ tau2*f ## Heterogeneity variance
yi ~~ data.vi*yi ## Known sampling variance"
plot(model)
## Do not standardize the latent variable (f): std.lv=FALSE
RAM <- lavaan2RAM(model, obs.variables="yi", std.lv=FALSE)
## Use likelihood-based CI
my.fit <- sem(RAM=RAM, data=Hox02, intervals="LB")
summary(my.fit)
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