Simulate data from univariate latent change score model parameter estimates

Source: R/sim_lcsm_data.R

This function simulate data from univariate latent change score model parameter estimates using simulateData.

sim_uni_lcsm(
  timepoints,
  model,
  model_param = NULL,
  var = "x",
  change_letter = "g",
  sample.nobs = 500,
  na_pct = 0,
  ...,
  return_lavaan_syntax = FALSE
)

Arguments

timepoints

See specify_uni_lcsm
model

See specify_uni_lcsm
model_param

List, specifying parameter estimates for the LCSM that has been specified in the argument 'model'

  • gamma_lx1: Mean of latent true scores x (Intercept),

  • sigma2_lx1: Variance of latent true scores x,

  • sigma2_ux: Variance of observed scores x,

  • alpha_g2: Mean of change factor (g2),

  • alpha_g3: Mean of change factor (g3),

  • sigma2_g2: Variance of constant change factor (g2).

  • sigma2_g3: Variance of constant change factor (g3),

  • sigma_g2lx1: Covariance of constant change factor (g2) with the initial true score x (lx1),

  • sigma_g3lx1: Covariance of constant change factor (g3) with the initial true score x (lx1),

  • sigma_g2g3: Covariance of change factors (g2 and g2),

  • phi_x: Autoregression of change scores x.
var

See specify_uni_lcsm
change_letter

See specify_uni_lcsm
sample.nobs

Numeric, number of cases to be simulated, see specify_uni_lcsm
na_pct

Numeric, percentage of random missing values in the simulated dataset [0,1]
...

Arguments to be passed on to simulateData
return_lavaan_syntax

Logical, if TRUE return the lavaan syntax used for simulating data. To make it look beautiful use the function cat.

Value

tibble

Examples

# Simulate data from univariate LCSM parameters 
sim_uni_lcsm(timepoints = 10, 
             model = list(alpha_constant = TRUE, beta = FALSE, phi = TRUE), 
             model_param = list(gamma_lx1 = 21, 
                                sigma2_lx1 = 1.5,
                                sigma2_ux = .2, 
                                alpha_g2 = -.93,
                                sigma2_g2 = .1,
                                sigma_g2lx1 = .2,
                                phi_x = .2),
             return_lavaan_syntax = FALSE, 
             sample.nobs = 1000,
             na_pct = .3)

#> Parameter estimates for the data simulation are taken from the argument 'model_param'.
#> All parameter estimates for the LCSM have been specified in the argument 'model_param'.
#> # A tibble: 1,000 × 11
#>       id    x1    x2    x3    x4    x5    x6    x7    x8    x9   x10
#>    <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#>  1     1  23.0  NA    22.3  NA    20.1  NA    NA    17.9 16.4  15.5 
#>  2     2  22.2  21.1  NA    17.3  16.4  13.9  13.9  11.9  9.84  8.44
#>  3     3  21.8  21.1  19.0  17.0  16.9  14.7  13.2  NA   11.4  10.9 
#>  4     4  21.0  20.4  20.7  NA    18.4  17.8  16.6  NA   NA    NA   
#>  5     5  21.9  21.8  NA    19.6  NA    17.8  16.4  15.6 14.5  13.2 
#>  6     6  23.6  NA    22.3  NA    19.2  18.2  18.0  16.7 NA    13.8 
#>  7     7  19.4  18.4  16.7  NA    14.7  13.6  12.3  10.2  9.59  8.21
#>  8     8  19.4  NA    NA    NA    16.1  13.4  13.5  13.1 12.3  11.4 
#>  9     9  22.4  21.3  20.4  20.8  19.5  18.2  17.0  NA   15.7  NA   
#> 10    10  NA    NA    19.6  19.1  18.6  17.9  16.1  NA   16.5  NA   
#> # … with 990 more rows