The lavPredict()
function can be used to compute (or `predict')
estimated values for latent variables, and given these values, the model-implied
values for the indicators of these latent variables. NOTE: the goal of this
function is NOT to predict future values of dependent variables as in the
regression framework!
lavPredict(object, type = "lv", newdata = NULL, method = "EBM",
se = "none", label = TRUE, fsm = FALSE, level = 1L,
optim.method = "bfgs", ETA = NULL)
A character string. If "lv"
, estimated values for the latent
variables in the model are computed. If "ov"
, model predicted values for
the indicators of the latent variables in the model are computed. If
"yhat"
, the estimated value for the observed indicators, given
user-specified values for the latent variables provided by de ETA
argument. If "fy"
, densities (or probabilities) for each observed
indicator, given user-specified values for the latent variables provided by de
ETA
argument.
An optional data.frame, containing the same variables as the data.frame used when fitting the model in object.
A character string. In the linear case (when the indicators are
continuous), the possible options are "regression"
or "Bartlett"
.
In the categorical case, the two options are "EBM"
for
the Empirical Bayes Modal approach, and "ML"
for the maximum
likelihood approach.
Character. If "none"
, no standard errors are computed.
If "standard"
, naive standard errors are computed (assuming the
parameters of the measurement model are known). The standard errors are
returned as an attribute.
Logical. If TRUE, the columns are labeled.
Logical. If TRUE, return the factor score matrix as an attribute. Only for numeric data.
Integer. Only used in a multilevel SEM.
If level = 1
, only factor scores for latent variable
defined at the first (within) level are computed; if level = 2
,
only factor scores for latent variables defined at the second (between) level
are computed.
Character string. Only used in the categorical case.
If "nlminb"
(the default in 0.5), the "nlminb()"
function is used
for the optimization. If "bfgs"
or "BFGS"
(the default in 0.6),
the "optim()"
function is used with the BFGS method.
An optional matrix or list, containing latent variable values
for each observation. Used for computations when type = "ov"
.
The predict()
function calls the lavPredict()
function
with its default options.
If there are no latent variables in the model, type = "ov"
will
simply return the values of the observed variables. Note that this function
can not be used to `predict' values of dependent variables, given the
values of independent values (in the regression sense). In other words,
the structural component is completely ignored (for now).
# NOT RUN {
# fit model
HS.model <- ' visual =~ x1 + x2 + x3
textual =~ x4 + x5 + x6
speed =~ x7 + x8 + x9 '
fit <- cfa(HS.model, data=HolzingerSwineford1939)
head(lavPredict(fit))
head(lavPredict(fit, type = "ov"))
# }
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