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Type:Package
Title:Univariate and Bivariate Latent Change Score Modelling
Date:2023-02-25
Version:0.3.2
Description:Helper functions to implement univariate and bivariate latent change score models in R using the 'lavaan' package. For details about Latent Change Score Modeling (LCSM) see McArdle (2009) <doi:10.1146/annurev.psych.60.110707.163612> and Grimm, An, McArdle, Zonderman and Resnick (2012) <doi:10.1080/10705511.2012.659627>. The package automatically generates 'lavaan' syntax for different model specifications and varying timepoints. The 'lavaan' syntax generated by this package can be returned and further specifications can be added manually. Longitudinal plots as well as simplified path diagrams can be created to visualise data and model specifications. Estimated model parameters and fit statistics can be extracted as data frames. Data for different univariate and bivariate LCSM can be simulated by specifying estimates for model parameters to explore their effects. This package combines the strengths of other R packages like 'lavaan', 'broom', and 'semPlot' by generating 'lavaan' syntax that helps these packages work together.
Depends:R (≥ 3.5.0)
License:MIT + file LICENSE
Encoding:UTF-8
URL:https://milanwiedemann.github.io/lcsm/
BugReports:https://github.com/milanwiedemann/lcsm/issues
LazyData:true
Imports:lavaan (≥ 0.6.2), dplyr (≥ 0.7.4), tibble (≥ 1.4.2),magrittr (≥ 1.5), rlang (≥ 0.1.6), tidyr (≥ 0.8.0), ggplot2(≥ 2.2.1), broom (≥ 0.5.1), semPlot (≥ 1.1), stats (≥3.5.2), stringr (≥ 1.4.0), purrr (≥ 0.3.4), cli
RoxygenNote:7.2.3
Suggests:testthat (≥ 3.0.0), knitr (≥ 1.22), rmarkdown (≥ 1.12),shiny (≥ 1.4.0)
VignetteBuilder:knitr
Config/testthat/edition:3
NeedsCompilation:no
Packaged:2023-02-25 23:21:58 UTC; milanwiedemann
Author:Milan WiedemannORCID iD [aut, cre], Graham M ThewORCID iD [ctb], Urška KoširORCID iD [ctb], Anke EhlersORCID iD [ths], Mental Health Research UK [fnd]
Maintainer:Milan Wiedemann <milan.wiedemann@gmail.com>
Repository:CRAN
Date/Publication:2023-02-25 23:40:02 UTC

Pipe operator

Description

Pipe operator

Usage

lhs %>% rhs

Longitudinal dataset with repeated measures of two constructs

Description

Example dataset with repeated measures of two constructs to illustrate how the package works.

Usage

data(data_bi_lcsm)

Format

A longitudinal dataset in wide format:

Examples

# Load data into global environmentdata(data_bi_lcsm)

Longitudinal dataset with repeated measures of one constructs

Description

Example dataset with repeated measures of one constructs to illustrate how the package works.

Usage

data(data_uni_lcsm)

Format

A longitudinal dataset in wide format:

Examples

# Load data into global environmentdata(data_uni_lcsm)

Extract fit statistics of lavaan objects

Description

Extract fit statistics of lavaan objects

Usage

extract_fit(..., details = FALSE)

Arguments

...

lavaan object(s)

details

Logical, if TRUE return all fit statistics.By default this is set to FALSE, a selection (chisq, npar, aic, bic, cfi, rmsea, srmr) of fit statistics is returned.

Value

This function returns a tibble.

References

David Robinson and Alex Hayes (2019). broom: Convert Statistical Analysis Objects into Tidy Tibbles. R package version 0.5.2.https://CRAN.R-project.org/package=broom/.

Examples

# First create a lavaan object## Not run: bi_lcsm_01 <- fit_bi_lcsm(data = data_bi_lcsm,                           var_x = names(data_bi_lcsm)[2:4],                           var_y = names(data_bi_lcsm)[12:14],                          model_x = list(alpha_constant = TRUE,                                          beta = TRUE,                                          phi = FALSE),                          model_y = list(alpha_constant = TRUE,                                          beta = TRUE,                                          phi = TRUE),                          coupling = list(delta_lag_xy = TRUE,                                           xi_lag_yx = TRUE)                                          )# Now extract fit statistics  extract_fit(bi_lcsm_01)## End(Not run)

Extract labelled parameters of lavaan objects

Description

Extract labelled parameters of lavaan objects

Usage

extract_param(lavaan_object, printp = FALSE)

Arguments

lavaan_object

lavaan object.

printp

If TRUE convert into easily readable p values.

Value

This function returns a tibble with labelled parameters.

References

David Robinson and Alex Hayes (2019). broom: Convert Statistical Analysis Objects into Tidy Tibbles. R package version 0.5.2.https://CRAN.R-project.org/package=broom/

Examples

# First create a lavaan objectbi_lcsm_01 <- fit_bi_lcsm(data = data_bi_lcsm,                           var_x = names(data_bi_lcsm)[2:4],                           var_y = names(data_bi_lcsm)[12:14],                          model_x = list(alpha_constant = TRUE,                                          beta = TRUE,                                          phi = FALSE),                          model_y = list(alpha_constant = TRUE,                                          beta = TRUE,                                          phi = TRUE),                          coupling = list(delta_lag_xy = TRUE,                                           xi_lag_yx = TRUE)                                          )# Now extract parameter estimates              extract_param(bi_lcsm_01)

Fit bivariate latent change score models

Description

Fit bivariate latent change score models.

Usage

fit_bi_lcsm(  data,  var_x,  var_y,  model_x,  model_y,  coupling,  add = NULL,  mimic = "Mplus",  estimator = "MLR",  missing = "FIML",  return_lavaan_syntax = FALSE,  ...)

Arguments

data

Wide dataset.

var_x

List of variables measuring one construct of the model.

var_y

List of variables measuring another construct of the model.

model_x

List of model specifications (logical) for variables specified invar_x.

  • alpha_constant (Constant change factor),

  • alpha_piecewise (Piecewise constant change factors),

  • alpha_piecewise_num (Changepoint of piecewise constant change factors.In an example with 10 repeated measurements, settingalpha_piecewise_num to 5 would estimate two seperate constant change factors, a first one for changes up to timepoint 5, and a second one for changes from timepoint 5 onwards (in this example timepoint 10).,

  • alpha_linear (Linear change factor),

  • beta (Proportional change factor),

  • phi (Autoregression of change scores).

model_y

List of model specifications for variables specified invar_y.

  • alpha_constant (Constant change factor),

  • alpha_piecewise (Piecewise constant change factors),

  • alpha_piecewise_num (Changepoint of piecewise constant change factors.In an example with 10 repeated measurements, settingalpha_piecewise_num to 5 would estimate two seperate constant change factors, a first one for changes up to timepoint 5, and a second one for changes from timepoint 5 onwards (in this example timepoint 10).,

  • alpha_linear (Linear change factor),

  • beta (Proportional change factor),

  • phi (Autoregression of change scores).

coupling

List of model specifications (logical) for coupling parameters.

  • coupling_piecewise (Piecewise coupling parameters),

  • coupling_piecewise_num (Changepoint of piecewise coupling parameters),

  • delta_xy (True score y predicting subsequent change score x),

  • delta_yx (True score x predicting subsequent change score y),

  • xi_xy (Change score y predicting subsequent change score x),

  • xi_yx (Change score x predicting subsequent change score y).

add

String, lavaan syntax to be added to the model

mimic

Seemimic argument inlavOptions.

estimator

Seeestimator argument inlavOptions.

missing

Seemissing argument inlavOptions.

return_lavaan_syntax

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

...

Additional arguments to be passed tolavOptions.

Value

This function returns a lavaan class object.

References

Ghisletta, P., & McArdle, J. J. (2012). Latent Curve Models and Latent Change Score Models Estimated in R. Structural Equation Modeling: A Multidisciplinary Journal, 19(4), 651–682.doi:10.1146/annurev.psych.60.110707.163612.

Grimm, K. J., Ram, N., & Estabrook, R. (2017). Growth Modeling—Structural Equation and Multilevel Modeling Approaches. New York: The Guilford Press.

McArdle, J. J. (2009). Latent variable modeling of differences and changes with longitudinal data. Annual Review of Psychology, 60(1), 577–605.doi:10.1146/annurev.psych.60.110707.163612.

Yves Rosseel (2012). lavaan: An R Package for Structural Equation Modeling. Journal of Statistical Software, 48(2), 1-36.doi:10.18637/jss.v048.i02.

Examples

# Fit fit_bi_lcsm(data = data_bi_lcsm,             var_x = names(data_bi_lcsm)[2:4],             var_y = names(data_bi_lcsm)[12:14],            model_x = list(alpha_constant = TRUE,                            beta = TRUE,                            phi = FALSE),            model_y = list(alpha_constant = TRUE,                            beta = TRUE,                            phi = TRUE),            coupling = list(delta_lag_xy = TRUE,                             xi_lag_yx = TRUE)                            )

Fit univariate latent change score models

Description

Fit univariate latent change score models.

Usage

fit_uni_lcsm(  data,  var,  model,  add = NULL,  mimic = "Mplus",  estimator = "MLR",  missing = "FIML",  return_lavaan_syntax = FALSE,  ...)

Arguments

data

A data frame in "wide" format, i.e. one column for each measurement point and one row for each observation.

var

Vector, specifying the variable names of each measurement point sequentially.

model

List of model specifications (logical) for variables specified invar.

  • alpha_constant (Constant change factor)

  • alpha_piecewise (Piecewise constant change factors)

  • alpha_piecewise_num (Changepoint of piecewise constant change factors.In an example with 10 repeated measurements, settingalpha_piecewise_num to 5 would estimate two seperate constant change factors, a first one for changes up to timepoint 5, and a second one for changes from timepoint 5 onwards (in this example timepoint 10).,

  • alpha_linear (Linear change factor)

  • beta (Proportional change factor)

  • phi (Autoregression of change scores)

add

String, lavaan syntax to be added to the model

mimic

Seemimic argument inlavOptions.

estimator

Seeestimator argument inlavOptions.

missing

Seemissing argument inlavOptions.

return_lavaan_syntax

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

...

Additional arguments to be passed tolavOptions.

Value

This function returns a lavaan class object.

References

Ghisletta, P., & McArdle, J. J. (2012). Latent Curve Models and Latent Change Score Models Estimated in R. Structural Equation Modeling: A Multidisciplinary Journal, 19(4), 651–682.doi:10.1080/10705511.2012.713275.

Grimm, K. J., Ram, N., & Estabrook, R. (2017). Growth Modeling—Structural Equation and Multilevel Modeling Approaches. New York: The Guilford Press.

McArdle, J. J. (2009). Latent variable modeling of differences and changes with longitudinal data. Annual Review of Psychology, 60(1), 577–605.doi:10.1146/annurev.psych.60.110707.163612.

Yves Rosseel (2012). lavaan: An R Package for Structural Equation Modeling. Journal of Statistical Software, 48(2), 1-36.doi:10.18637/jss.v048.i02.

Examples

# Fit univariate latent change score modelfit_uni_lcsm(data = data_uni_lcsm,              var = names(data_uni_lcsm)[2:4],             model = list(alpha_constant = TRUE,                           beta = FALSE,                           phi = FALSE))

Longitudinal dataset with repeated measures of two constructs

Description

Example dataset with 5 repeated measures of two constructs to illustrate how the package works.

Usage

data(lcsm_data)

Format

A longitudinal dataset in wide format:

Examples

# Load data into global environmentdata(lcsm_data)

Plot simplified path diagram of univariate and bivariate latent change score models

Description

Note that the following three arguments are needed to create a plot (see below for more details):

Usage

plot_lcsm(  lavaan_object,  layout = NULL,  lavaan_syntax = NULL,  return_layout_from_lavaan_syntax = FALSE,  lcsm = c("univariate", "bivariate"),  lcsm_colours = FALSE,  curve_covar = 0.5,  what = "path",  whatLabels = "est",  edge.width = 1,  node.width = 1,  border.width = 1,  fixedStyle = 1,  freeStyle = 1,  residuals = FALSE,  label.scale = FALSE,  sizeMan = 3,  sizeLat = 5,  intercepts = FALSE,  fade = FALSE,  nCharNodes = 0,  nCharEdges = 0,  edge.label.cex = 0.5,  ...)

Arguments

lavaan_object

lavaan object of a univariate or bivariate latent change score model.

layout

Matrix, specifying number and location of manifest and latent variables of LCS model specified inlavaan_object.

lavaan_syntax

String, lavaan syntax of the lavaan object specified inlavaan_object.Iflavaan_syntax is provided a layout matrix will be generated automatically.

return_layout_from_lavaan_syntax

Logical, if TRUE andlavaan_syntax is provided, the layout matrix generated forsemPaths will be returned for inspection of further customisation.

lcsm

String, specifying whether lavaan_object represent a "univariate" or "bivariate" LCS model.

lcsm_colours

Logical, if TRUE the following colours will be used to highlight different parts of the model: Observed variables (White); Latent true scores (Green); Latent change scores (Blue) ; Change factors (Yellow).

curve_covar

SeesemPaths.

what

SeesemPlot. "path" to show unweighted grey edges, "par" to show parameter estimates as weighted (green/red) edges

whatLabels

SeesemPaths. "label" to show edge names as label, "est" for parameter estimates, "hide" to hide edge labels.

edge.width

SeesemPaths.

node.width

SeesemPaths.

border.width

SeesemPaths.

fixedStyle

SeesemPaths.

freeStyle

SeesemPaths.

residuals

SeesemPaths.

label.scale

SeesemPaths.

sizeMan

SeesemPaths.

sizeLat

SeesemPaths.

intercepts

SeesemPaths.

fade

SeesemPaths.

nCharNodes

SeesemPaths.

nCharEdges

SeesemPaths.

edge.label.cex

SeesemPaths.

...

Other arguments passed on tosemPaths.

Value

Plot

References

Sacha Epskamp (2019). semPlot: Path Diagrams and Visual Analysis of Various SEM Packages' Output. R package version 1.1.1.https://CRAN.R-project.org/package=semPlot/

Examples

# Simplified plot of univariate lcsmlavaan_syntax_uni <- fit_uni_lcsm(  data = data_bi_lcsm,  var = c("x1", "x2", "x3", "x4", "x5"),  model = list(    alpha_constant = TRUE,    beta = TRUE,    phi = TRUE  ),  return_lavaan_syntax = TRUE,  return_lavaan_syntax_string = TRUE)lavaan_object_uni <- fit_uni_lcsm(  data = data_bi_lcsm,  var = c("x1", "x2", "x3", "x4", "x5"),  model = list(    alpha_constant = TRUE,    beta = TRUE,    phi = TRUE  ))plot_lcsm(  lavaan_object = lavaan_object_uni,  what = "cons", whatLabels = "invisible",  lavaan_syntax = lavaan_syntax_uni,  lcsm = "univariate")## Not run: # Simplified plot of bivariate lcsmlavaan_syntax_bi <- fit_bi_lcsm(  data = data_bi_lcsm,  var_x = c("x1", "x2", "x3", "x4", "x5"),  var_y = c("y1", "y2", "y3", "y4", "y5"),  model_x = list(    alpha_constant = TRUE,    beta = TRUE,    phi = TRUE  ),  model_y = list(    alpha_constant = TRUE,    beta = TRUE,    phi = TRUE  ),  coupling = list(    delta_lag_xy = TRUE,    delta_lag_yx = TRUE  ),  return_lavaan_syntax = TRUE,  return_lavaan_syntax_string = TRUE)lavaan_object_bi <- fit_bi_lcsm(  data = data_bi_lcsm,  var_x = c("x1", "x2", "x3", "x4", "x5"),  var_y = c("y1", "y2", "y3", "y4", "y5"),  model_x = list(    alpha_constant = TRUE,    beta = TRUE,    phi = TRUE  ),  model_y = list(    alpha_constant = TRUE,    beta = TRUE,    phi = TRUE  ),  coupling = list(    delta_lag_xy = TRUE,    delta_lag_yx = TRUE  ))plot_lcsm(  lavaan_object = lavaan_object_bi,  what = "cons", whatLabels = "invisible",  lavaan_syntax = lavaan_syntax_bi,  lcsm = "bivariate")## End(Not run)

Plot individual trajectories

Description

Plot individual trajectories

Usage

plot_trajectories(  data,  id_var,  var_list,  line_colour = "blue",  group_var = NULL,  point_colour = "black",  line_alpha = 0.2,  point_alpha = 0.2,  point_size = 1,  smooth = FALSE,  smooth_method = "loess",  smooth_se = FALSE,  xlab = "X",  ylab = "Y",  scale_x_num = FALSE,  scale_x_num_start = 1,  random_sample_frac = 1,  seed = 1234,  title_n = FALSE,  connect_missing = TRUE)

Arguments

data

Dataset in wide format.

id_var

String, specifying id variable.

var_list

Vector, specifying variable names to be plotted in sequential order.

line_colour

String, specifying colour of lines.

group_var

String, specifying variable name of group, each group will get individual colour lines. This overwrites the line_colour argument.Also consider other options to look at trajectories likefacet_wrap which may be more appropriate.

point_colour

String, specifying, colour of points.

line_alpha

Numeric, specifying alpha of lines.

point_alpha

Numeric, specifying alpha of points.

point_size

Numeric, size of point

smooth

Logical, add smoothed conditional means usinggeom_smooth.

smooth_method

String, specifying method to be used for calculating average line, seegeom_smooth.

smooth_se

Logical, specifying whether to add standard error of average line or not.

xlab

String for x axis label.

ylab

String for y axis label.

scale_x_num

Logical, ifTRUE print sequential numbers starting from 1 as x axis labels, ifFALSE use variable names.

scale_x_num_start

Numeric, ifscale_x_num = TRUE this is the starting value of the x axis.

random_sample_frac

The fraction of rows to select (from wide dataset), default is set to 1 (100 percent) of the sample.

seed

Set seed for random sample ifrandom_sample_frac argument is used.

title_n

Logical, specifying whether to print title with number and percentage of cases used for the plot.

connect_missing

Logical, specifying whether to connect points byid_var across missing values.

Value

ggplot2 object

Examples

# Create plot for construct xplot_trajectories(data = data_bi_lcsm,                  id_var = "id",                   var_list = c("x1", "x2", "x3", "x4", "x5",                                "x6", "x7", "x8", "x9", "x10"))# Create plot for construct y specifying some ather argumentsplot_trajectories(data = data_bi_lcsm,                  id_var = "id",                   var_list = c("y1", "y2", "y3", "y4", "y5",                                "y6", "y7", "y8", "y9", "y10"),                  xlab = "Time", ylab = "Y Score",                  connect_missing = FALSE, random_sample_frac = 0.5)

Rename variables for univariate and bivariate latent change score models

Description

Rename variables for univariate and bivariate latent change score models

Usage

rename_lcsm_vars(data, var_x, var_y)

Arguments

data

Dataset in wide format

var_x

List of variables measuring first construct

var_y

List of variables measuring second construct

Value

Dataset in wide format with renamed variables

Select cases based on minimum number of available session scores on two longitudinal measures

Description

Select cases based on minimum number of available session scores on two longitudinal measures

Usage

select_bi_cases(data, id_var, var_list_x, var_list_y, min_count_x, min_count_y)

Arguments

data

A data frame in "wide" format, i.e. one column for each measurement point and one row for each observation.

id_var

String, specifying id variable.

var_list_x

Vector, specifying variable names of construct X in sequential order.

var_list_y

Vector, specifying variable names of construct Y in sequential order.

min_count_x

Numeric, specifying minimum number of available scores for construct X.

min_count_y

Numeric, specifying minimum number of available scores for construct Y.

Value

tibble

Examples

select_bi_cases(data_bi_lcsm,  id_var = "id",  var_list_x = names(data_bi_lcsm)[2:11],  var_list_y = names(data_bi_lcsm)[12:21],  min_count_x = 7,  min_count_y = 7)

Select cases based on minimum number of available session scores on one longitudinal measure

Description

Select cases based on minimum number of available session scores on one longitudinal measure

Usage

select_uni_cases(data, id_var, var_list, min_count, return_id_only = FALSE)

Arguments

data

Dataset in wide format.

id_var

String, specifying id variable.

var_list

Vector, specifying variable names in sequential order.

min_count

Numeric, specifying minimum number of available scores

return_id_only

Logical, if TRUE only return ID. This is needed for select_bi_cases

Value

tibble

Examples

select_uni_cases(data_uni_lcsm,  id_var = "id",  var_list = names(data_uni_lcsm)[-1],  min_count = 7)

Simulate data from bivariate latent change score model parameter estimates

Description

This function simulate data from bivariate latent change score model parameter estimates usingsimulateData.

Usage

sim_bi_lcsm(  timepoints,  model_x,  model_x_param = NULL,  model_y,  model_y_param = NULL,  coupling,  coupling_param = NULL,  sample.nobs = 500,  na_x_pct = 0,  na_y_pct = 0,  seed = NULL,  ...,  var_x = "x",  var_y = "y",  change_letter_x = "g",  change_letter_y = "j",  return_lavaan_syntax = FALSE)

Arguments

timepoints

Seespecify_bi_lcsm

model_x

Seespecify_bi_lcsm

model_x_param

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

  • 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 change factor (g2).

  • sigma2_g3: Variance of change factor (g3),

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

  • sigma_g3lx1: Covariance of 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.

model_y

Seespecify_bi_lcsm

model_y_param

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

  • gamma_ly1: Mean of latent true scores y (Intercept),

  • sigma2_ly1: Variance of latent true scores y,

  • sigma2_uy: Variance of observed scores y,

  • alpha_j2: Mean of change factor (j2),

  • alpha_j3: Mean of change factor (j3),

  • sigma2_j2: Variance of change factor (j2).

  • sigma2_j3: Variance of change factor (j3),

  • sigma_j2ly1: Covariance of change factor (j2) with the initial true score x (ly1),

  • sigma_j3ly1: Covariance of change factor (j3) with the initial true score x (ly1),

  • sigma_j2j3: Covariance of change factors (j2 and j2),

  • phi_y: Autoregression of change scores y.

coupling

Seespecify_bi_lcsm

coupling_param

List, specifying parameter estimates coupling parameters that have been specified in the argument 'coupling':

  • sigma_su: Covariance of residuals x and y,

  • sigma_ly1lx1: Covariance of intercepts x and y,

  • sigma_g2ly1: Covariance of change factor x (g2) with the initial true score y (ly1),

  • sigma_g3ly1: Covariance of change factor x (g3) with the initial true score y (ly1),

  • sigma_j2lx1: Covariance of change factor y (j2) with the initial true score x (lx1),

  • sigma_j3lx1: Covariance of change factor y (j3) with the initial true score x (lx1),

  • sigma_j2g2: Covariance of change factors y (j2) and x (g2),

  • sigma_j2g3: Covariance of change factors y (j2) and x (g3),

  • sigma_j3g2: Covariance of change factors y (j3) and x (g2),,

  • delta_con_xy: Change score x (t) determined by true score y (t),

  • delta_con_yx: Change score y (t) determined by true score x (t),

  • delta_lag_xy: Change score x (t) determined by true score y (t-1),

  • delta_lag_yx: Change score y (t) determined by true score x (t-1),

  • xi_con_xy: Change score x (t) determined by change score y (t),

  • xi_con_yx: Change score y (t) determined by change score x (t),

  • xi_lag_xy: Change score x (t) determined by change score y (t-1),

  • xi_lag_yx: Change score y (t) determined by change score x (t-1)

sample.nobs

Numeric, number of cases to be simulated, seespecify_uni_lcsm

na_x_pct

Numeric, percentage of random missing values in the simulated dataset (0 to 1)

na_y_pct

Numeric, percentage of random missing values in the simulated dataset (0 to 1)

seed

Set seed for data simulation, seesimulateData

...

Arguments to be passed on tosimulateData

var_x

Seespecify_bi_lcsm

var_y

Seespecify_bi_lcsm

change_letter_x

Seespecify_bi_lcsm

change_letter_y

Seespecify_bi_lcsm

return_lavaan_syntax

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

Value

tibble

References

Ghisletta, P., & McArdle, J. J. (2012). Latent Curve Models and Latent Change Score Models Estimated in R. Structural Equation Modeling: A Multidisciplinary Journal, 19(4), 651–682.doi:10.1080/10705511.2012.713275.

Grimm, K. J., Ram, N., & Estabrook, R. (2017). Growth Modeling—Structural Equation and Multilevel Modeling Approaches. New York: The Guilford Press.

Kievit, R. A., Brandmaier, A. M., Ziegler, G., van Harmelen, A.-L., de Mooij, S. M. M., Moutoussis, M., … Dolan, R. J. (2018). Developmental cognitive neuroscience using latent change score models: A tutorial and applications. Developmental Cognitive Neuroscience, 33, 99–117.doi:10.1016/j.dcn.2017.11.007.

McArdle, J. J. (2009). Latent variable modeling of differences and changes with longitudinal data. Annual Review of Psychology, 60(1), 577–605.doi:10.1146/annurev.psych.60.110707.163612.

Yves Rosseel (2012). lavaan: An R Package for Structural Equation Modeling. Journal of Statistical Software, 48(2), 1-36.doi:10.18637/jss.v048.i02.

Examples

# Simulate data from bivariate LCSM parameters sim_bi_lcsm(timepoints = 12,             na_x_pct = .05,            na_y_pct = .1,            model_x = list(alpha_constant = TRUE, beta = TRUE, phi = FALSE),            model_x_param = list(gamma_lx1 = 21,                                 sigma2_lx1 = .5,                                 sigma2_ux = .2,                                 alpha_g2 = -.4,                                 sigma2_g2 = .4,                                 sigma_g2lx1 = .2,                                 beta_x = -.1),            model_y = list(alpha_constant = TRUE, beta = TRUE, phi = TRUE),            model_y_param = list(gamma_ly1 = 5,                                 sigma2_ly1 = .2,                                 sigma2_uy = .2,                                 alpha_j2 = -.2,                                 sigma2_j2 = .1,                                 sigma_j2ly1 = .02,                                 beta_y = -.2,                                 phi_y = .1),            coupling = list(delta_lag_xy = TRUE,                             xi_lag_yx = TRUE),            coupling_param =list(sigma_su = .01,                                 sigma_ly1lx1 = .2,                                 sigma_g2ly1 = .1,                                 sigma_j2lx1 = .1,                                 sigma_j2g2 = .01,                                 delta_lag_xy = .13,                                 xi_lag_yx = .4),            return_lavaan_syntax = FALSE)

Simulate data from univariate latent change score model parameter estimates

Description

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

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

Seespecify_uni_lcsm

model

Seespecify_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

Seespecify_uni_lcsm

change_letter

Seespecify_uni_lcsm

sample.nobs

Numeric, number of cases to be simulated, seespecify_uni_lcsm

na_pct

Numeric, percentage of random missing values in the simulated dataset (0 to 1)

seed

Set seed for data simulation, seesimulateData

...

Arguments to be passed on tosimulateData

return_lavaan_syntax

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

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)

Specify lavaan model for bivariate latent change score models

Description

Specify lavaan model for bivariate latent change score models

Usage

specify_bi_lcsm(  timepoints,  var_x,  model_x,  var_y,  model_y,  coupling,  add = NULL,  change_letter_x = "g",  change_letter_y = "j")

Arguments

timepoints

Number of timepoints.

var_x

Vector, specifying variables measuring one construct of the model.

model_x

List, specifying model specifications (logical) for variables specified invar_x.

  • alpha_constant (Constant change factor),

  • alpha_piecewise (Piecewise constant change factors),

  • alpha_piecewise_num (Changepoint of piecewise constant change factors),

  • alpha_linear (Linear change factor),

  • beta (Proportional change factor),

  • phi (Autoregression of change scores).

var_y

Vector, specifying variables measuring another construct of the model.

model_y

List, specifying model specifications (logical) for variables specified invar_y.

  • alpha_constant (Constant change factor),

  • alpha_piecewise (Piecewise constant change factors),

  • alpha_piecewise_num (Changepoint of piecewise constant change factors),

  • alpha_linear (Linear change factor),

  • beta (Proportional change factor),

  • phi (Autoregression of change scores).

coupling

List, specifying coupling parameters.

  • coupling_piecewise (Piecewise coupling parameters),

  • coupling_piecewise_num (Changepoint of piecewise coupling parameters),

  • delta_con_xy (True score y predicting concurrent change score x),

  • delta_lag_xy (True score y predicting subsequent change score x),

  • delta_con_yx (True score x predicting concurrent change score y),

  • delta_lag_yx (True score x predicting subsequent change score y),

  • xi_con_xy (Change score y predicting concurrent change score x),

  • xi_lag_xy (Change score y predicting subsequent change score x),

  • xi_con_yx (Change score x predicting concurrent change score y),

  • xi_lag_yx (Change score x predicting subsequent change score y).

add

String, lavaan syntax to be added to the model

change_letter_x

String, specifying letter to be used as change factor for construct x in lavaan syntax.

change_letter_y

String, specifying letter to be used as change factor for construct y in lavaan syntax.

Value

Lavaan model syntax including comments.

References

Ghisletta, P., & McArdle, J. J. (2012). Latent Curve Models and Latent Change Score Models Estimated in R. Structural Equation Modeling: A Multidisciplinary Journal, 19(4), 651–682.doi:doi.org/10.1080/10705511.2012.713275.

Grimm, K. J., Ram, N., & Estabrook, R. (2017). Growth Modeling—Structural Equation and Multilevel Modeling Approaches. New York: The Guilford Press.

McArdle, J. J. (2009). Latent variable modeling of differences and changes with longitudinal data. Annual Review of Psychology, 60(1), 577–605.doi:10.1146/annurev.psych.60.110707.163612.

Yves Rosseel (2012). lavaan: An R Package for Structural Equation Modeling. Journal of Statistical Software, 48(2), 1-36.doi:10.18637/jss.v048.i02.

Examples

# Specify bivariate LCSMlavaan_bi_lcsm_01 <- specify_bi_lcsm(timepoints = 10,                                      var_x = "x",                                     model_x = list(alpha_constant = TRUE,                                                     beta = TRUE,                                                     phi = TRUE),                                     var_y = "y",                                       model_y = list(alpha_constant = TRUE,                                                     beta = TRUE,                                                     phi = TRUE),                                       coupling = list(delta_lag_xy = TRUE,                                                      delta_lag_yx = TRUE),                                     change_letter_x = "g",                                     change_letter_y = "j")# To look at string simply return the object                                    lavaan_bi_lcsm_01# To get a readable output use cat() functioncat(lavaan_bi_lcsm_01)

Specify lavaan model for univariate latent change score models

Description

Specify lavaan model for univariate latent change score models

Usage

specify_uni_lcsm(timepoints, var, model, add = NULL, change_letter = "g")

Arguments

timepoints

Number if timepoints.

var

String, specifying letter to be used for of variables (Usually x or y).

model

List of model specifications (logical) for the variables specified invariable.

  • alpha_constant: Constant change factor,

  • alpha_piecewise: Piecewise constant change factors,

  • alpha_piecewise_num: Changepoint of piecewise constant change factors,

  • alpha_linear: Linear change factor,

  • beta: Proportional change factor,

  • phi: Autoregression of change scores.

add

String, lavaan syntax to be added to the model

change_letter

String, specifying letter to be used for change factor (Usually g or j).

Value

Lavaan model syntax including comments.

References

Ghisletta, P., & McArdle, J. J. (2012). Latent Curve Models and Latent Change Score Models Estimated in R. Structural Equation Modeling: A Multidisciplinary Journal, 19(4), 651–682.doi:10.1080/10705511.2012.713275.

Grimm, K. J., Ram, N., & Estabrook, R. (2017). Growth Modeling—Structural Equation and Multilevel Modeling Approaches. New York: The Guilford Press.

McArdle, J. J. (2009). Latent variable modeling of differences and changes with longitudinal data. Annual Review of Psychology, 60(1), 577–605.doi:10.1146/annurev.psych.60.110707.163612.

Yves Rosseel (2012). lavaan: An R Package for Structural Equation Modeling. Journal of Statistical Software, 48(2), 1-36.doi:10.18637/jss.v048.i02.

Examples

# Specify univariate LCSMlavaan_uni_lcsm_01 <- specify_uni_lcsm(timepoints = 10,                                        model = list(alpha_constant = TRUE,                                                     beta = TRUE,                                                     phi = TRUE),                                        var = "x",                                         change_letter = "g")                 #' # To look at string simply return the object                                    lavaan_uni_lcsm_01# To get a readable output use cat() functioncat(lavaan_uni_lcsm_01)
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