Skip to contents

Simulate data from univariate latent change score model parameter estimates

Source: R/sim_lcsm_data.R
sim_uni_lcsm.Rd

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

Usage

sim_uni_lcsm(
  timepoints,
  model,
  model_param = NULL,
  var = "x",
  change_letter = "g",
  sample.nobs = 500,
  na_pct = 0,
  seed = NULL,
  ...,
  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 to 1)

seed

Set seed for data simulation, see simulateData

...

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  21.7  NA    22.0  NA    20.4  NA    NA   19.5  18.4  17.9 
#>  2     2  19.5  19.1  NA    17.9  16.8  14.9  15.6 14.3  12.9  12.1 
#>  3     3  20.0  19.8  18.2  18.5  17.1  15.2  14.0 NA    12.9  12.7 
#>  4     4  22.3  21.2  21.2  NA    18.2  17.3  15.8 NA    NA    NA   
#>  5     5  21.2  21.3  NA    19.3  NA    18.1  17.0 16.3  15.4  14.3 
#>  6     6  21.0  NA    21.3  NA    19.7  19.4  20.0 19.4  NA    17.9 
#>  7     7  20.7  19.3  17.0  NA    14.3  12.8  11.0  8.54  7.47  5.66
#>  8     8  21.9  NA    NA    NA    15.8  12.6  12.2 11.3   9.99  8.49
#>  9     9  22.1  21.2  20.5  18.8  19.7  18.6  17.6 NA    16.7  NA   
#> 10    10  NA    NA    20.3  19.1  18.3  17.2  14.9 NA    14.4  NA   
#> # … with 990 more rows