Description Usage Arguments Details Value References See Also Examples
Fit a Growth Curve model. Only useful if all the latent variables in the model
are growth factors. For more complex models, it may be better to use the
lavaan
function.
1 2 3 4 5 
model 
A description of the userspecified model. Typically, the model
is described using the lavaan model syntax. See

data 
An optional data frame containing the observed variables used in the model. If some variables are declared as ordered factors, lavaan will treat them as ordinal variables. 
ordered 
Character vector. Only used if the data is in a data.frame. Treat these variables as ordered (ordinal) variables, if they are endogenous in the model. Importantly, all other variables will be treated as numeric (unless they are declared as ordered in the data.frame.) Since 0.64, ordered can also be logical. If TRUE, all observed endogenous variables are treated as ordered (ordinal). If FALSE, all observed endogenous variables are considered to be numeric (again, unless they are declared as ordered in the data.frame.) 
sampling.weights 
A variable name in the data frame containing
sampling weight information. Currently only available for nonclustered
data. Depending on the 
sample.cov 
Numeric matrix. A sample variancecovariance matrix. The rownames and/or colnames must contain the observed variable names. For a multiple group analysis, a list with a variancecovariance matrix for each group. 
sample.mean 
A sample mean vector. For a multiple group analysis, a list with a mean vector for each group. 
sample.th 
Vector of samplebased thresholds. For a multiple group analysis, a list with a vector of thresholds for each group. 
sample.nobs 
Number of observations if the full data frame is missing and only sample moments are given. For a multiple group analysis, a list or a vector with the number of observations for each group. 
group 
Character. A variable name in the data frame defining the groups in a multiple group analysis. 
cluster 
Character. A (single) variable name in the data frame defining the clusters in a twolevel dataset. 
constraints 
Additional (in)equality constraints not yet included in the
model syntax. See 
WLS.V 
A user provided weight matrix to be used by estimator 
NACOV 
A user provided matrix containing the elements of (N times)
the asymptotic variancecovariance matrix of the sample statistics.
For a multiple group analysis, a list with an asymptotic
variancecovariance matrix for each group. See the 
... 
Many more additional options can be defined, using 'name = value'.
See 
The growth
function is a wrapper for the more general
lavaan
function, using the following default arguments:
meanstructure = TRUE
,
int.ov.free = FALSE
, int.lv.free = TRUE
,
auto.fix.first = TRUE
(unless std.lv = TRUE
),
auto.fix.single = TRUE
, auto.var = TRUE
,
auto.cov.lv.x = TRUE
, auto.efa = TRUE
,
auto.th = TRUE
, auto.delta = TRUE
,
and auto.cov.y = TRUE
.
An object of class lavaan
, for which several methods
are available, including a summary
method.
Yves Rosseel (2012). lavaan: An R Package for Structural Equation Modeling. Journal of Statistical Software, 48(2), 136. URL http://www.jstatsoft.org/v48/i02/.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19  ## linear growth model with a timevarying covariate
model.syntax < '
# intercept and slope with fixed coefficients
i =~ 1*t1 + 1*t2 + 1*t3 + 1*t4
s =~ 0*t1 + 1*t2 + 2*t3 + 3*t4
# regressions
i ~ x1 + x2
s ~ x1 + x2
# timevarying covariates
t1 ~ c1
t2 ~ c2
t3 ~ c3
t4 ~ c4
'
fit < growth(model.syntax, data = Demo.growth)
summary(fit)

This is lavaan 0.63
lavaan is BETA software! Please report any bugs.
lavaan 0.63 ended normally after 31 iterations
Optimization method NLMINB
Number of free parameters 17
Number of observations 400
Estimator ML
Model Fit Test Statistic 26.059
Degrees of freedom 21
Pvalue (Chisquare) 0.204
Parameter Estimates:
Information Expected
Information saturated (h1) model Structured
Standard Errors Standard
Latent Variables:
Estimate Std.Err zvalue P(>z)
i =~
t1 1.000
t2 1.000
t3 1.000
t4 1.000
s =~
t1 0.000
t2 1.000
t3 2.000
t4 3.000
Regressions:
Estimate Std.Err zvalue P(>z)
i ~
x1 0.608 0.060 10.134 0.000
x2 0.604 0.064 9.412 0.000
s ~
x1 0.262 0.029 9.198 0.000
x2 0.522 0.031 17.083 0.000
t1 ~
c1 0.143 0.050 2.883 0.004
t2 ~
c2 0.289 0.046 6.295 0.000
t3 ~
c3 0.328 0.044 7.361 0.000
t4 ~
c4 0.330 0.058 5.655 0.000
Covariances:
Estimate Std.Err zvalue P(>z)
.i ~~
.s 0.075 0.040 1.855 0.064
Intercepts:
Estimate Std.Err zvalue P(>z)
.t1 0.000
.t2 0.000
.t3 0.000
.t4 0.000
.i 0.580 0.062 9.368 0.000
.s 0.958 0.029 32.552 0.000
Variances:
Estimate Std.Err zvalue P(>z)
.t1 0.580 0.080 7.230 0.000
.t2 0.596 0.054 10.969 0.000
.t3 0.481 0.055 8.745 0.000
.t4 0.535 0.098 5.466 0.000
.i 1.079 0.112 9.609 0.000
.s 0.224 0.027 8.429 0.000
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