growth: Fit Growth Curve Models

Description Usage Arguments Details Value References See Also Examples

View source: R/xxx_lavaan.R

Description

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.

Usage

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growth(model = NULL, data = NULL, ordered = NULL, sampling.weights = NULL,
       sample.cov = NULL, sample.mean = NULL, sample.th = NULL,
       sample.nobs = NULL, group = NULL, cluster = NULL, 
       constraints = "", WLS.V = NULL, NACOV = NULL,
       ...) 

Arguments

model

A description of the user-specified model. Typically, the model is described using the lavaan model syntax. See model.syntax for more information. Alternatively, a parameter table (eg. the output of the lavaanify() function) is also accepted.

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 0.6-4, 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 non-clustered data. Depending on the sampling.weights.normalization option, these weights may be rescaled (or not) so that their sum equals the number of observations (total or per group). Currently only available if estimator is ML in combination with robust standard errors and a robust test statistic. By default, the estimator will be "MLR".

sample.cov

Numeric matrix. A sample variance-covariance matrix. The rownames and/or colnames must contain the observed variable names. For a multiple group analysis, a list with a variance-covariance 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 sample-based 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 two-level dataset.

constraints

Additional (in)equality constraints not yet included in the model syntax. See model.syntax for more information.

WLS.V

A user provided weight matrix to be used by estimator "WLS"; if the estimator is "DWLS", only the diagonal of this matrix will be used. For a multiple group analysis, a list with a weight matrix for each group. The elements of the weight matrix should be in the following order (if all data is continuous): first the means (if a meanstructure is involved), then the lower triangular elements of the covariance matrix including the diagonal, ordered column by column. In the categorical case: first the thresholds (including the means for continuous variables), then the slopes (if any), the variances of continuous variables (if any), and finally the lower triangular elements of the correlation/covariance matrix excluding the diagonal, ordered column by column.

NACOV

A user provided matrix containing the elements of (N times) the asymptotic variance-covariance matrix of the sample statistics. For a multiple group analysis, a list with an asymptotic variance-covariance matrix for each group. See the WLS.V argument for information about the order of the elements.

...

Many more additional options can be defined, using 'name = value'. See lavOptions for a complete list.

Details

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.

Value

An object of class lavaan, for which several methods are available, including a summary method.

References

Yves Rosseel (2012). lavaan: An R Package for Structural Equation Modeling. Journal of Statistical Software, 48(2), 1-36. URL http://www.jstatsoft.org/v48/i02/.

See Also

lavaan

Examples

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## linear growth model with a time-varying 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

  # time-varying covariates
    t1 ~ c1
    t2 ~ c2
    t3 ~ c3
    t4 ~ c4
'

fit <- growth(model.syntax, data = Demo.growth)
summary(fit)

Example output

This is lavaan 0.6-3
lavaan is BETA software! Please report any bugs.
lavaan 0.6-3 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
  P-value (Chi-square)                           0.204

Parameter Estimates:

  Information                                 Expected
  Information saturated (h1) model          Structured
  Standard Errors                             Standard

Latent Variables:
                   Estimate  Std.Err  z-value  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  z-value  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  z-value  P(>|z|)
 .i ~~                                                
   .s                 0.075    0.040    1.855    0.064

Intercepts:
                   Estimate  Std.Err  z-value  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  z-value  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

lavaan documentation built on March 10, 2021, 5:05 p.m.

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