Package 'lcsm'

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:

• id: ID variable, unique identifier for each person

• x1: x value at time point 1

• x2: x value at time point 2

• x3: x value at time point 3

• x4: x value at time point 4

• x5: x value at time point 5

• x6: x value at time point 6

• x7: x value at time point 7

• x8: x value at time point 8

• x9: x value at time point 9

• x10: x value at time point 10

• y1: y value at time point 1

• y2: y value at time point 2

• y3: y value at time point 3

• y4: y value at time point 4

• y5: y value at time point 5

• y6: y value at time point 6

• y7: y value at time point 7

• y8: y value at time point 8

• y9: y value at time point 9

• y10: y value at time point 10

Examples

# Load data into global environment
data(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:

• id: ID variable, unique identifier for each person

• x1: x value at time point 1

• x2: x value at time point 2

• x3: x value at time point 3

• x4: x value at time point 4

• x5: x value at time point 5

• x6: x value at time point 6

• x7: x value at time point 7

• x8: x value at time point 8

• x9: x value at time point 9

• x10: x value at time point 10

Examples

# Load data into global environment
data(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 object
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 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 in var_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, setting alpha_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 in var_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, setting alpha_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	See mimic argument in lavOptions.
estimator	See estimator argument in lavOptions.
missing	See missing argument in lavOptions.
return_lavaan_syntax	Logical, if TRUE return the lavaan syntax used for simulating data. To make it look beautiful use the function cat.
...	Additional arguments to be passed to lavOptions.

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 in var. 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, setting alpha_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	See mimic argument in lavOptions.
estimator	See estimator argument in lavOptions.
missing	See missing argument in lavOptions.
return_lavaan_syntax	Logical, if TRUE return the lavaan syntax used for simulating data. To make it look beautiful use the function cat.
...	Additional arguments to be passed to lavOptions.

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 model
fit_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:

• id: ID variable, unique identifier for each person

• x1: x value at time point 1

• x2: x value at time point 2

• x3: x value at time point 3

• x4: x value at time point 4

• x5: x value at time point 5

• y1: y value at time point 1

• y2: y value at time point 2

• y3: y value at time point 3

• y4: y value at time point 4

• y5: y value at time point 5

Examples

# Load data into global environment
data(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):

• lavaan_object: the lavaan fit object needs to be specified together with a

• lcsm: a string indicating whether the latent change score model is "univariate" or "bivariate", and

• lavaan_syntax: a separate object with the lavaan syntax as a string

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 in lavaan_object.
lavaan_syntax	String, lavaan syntax of the lavaan object specified in lavaan_object. If lavaan_syntax is provided a layout matrix will be generated automatically.
return_layout_from_lavaan_syntax	Logical, if TRUE and lavaan_syntax is provided, the layout matrix generated for semPaths 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	See semPaths.
what	See semPlot. "path" to show unweighted grey edges, "par" to show parameter estimates as weighted (green/red) edges
whatLabels	See semPaths. "label" to show edge names as label, "est" for parameter estimates, "hide" to hide edge labels.
edge.width	See semPaths.
node.width	See semPaths.
border.width	See semPaths.
fixedStyle	See semPaths.
freeStyle	See semPaths.
residuals	See semPaths.
label.scale	See semPaths.
sizeMan	See semPaths.
sizeLat	See semPaths.
intercepts	See semPaths.
fade	See semPaths.
nCharNodes	See semPaths.
nCharEdges	See semPaths.
edge.label.cex	See semPaths.
...	Other arguments passed on to semPaths.

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 lcsm
lavaan_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 lcsm
lavaan_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 like facet_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 using geom_smooth.
smooth_method	String, specifying method to be used for calculating average line, see geom_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, if TRUE print sequential numbers starting from 1 as x axis labels, if FALSE use variable names.
scale_x_num_start	Numeric, if scale_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 if random_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 by id_var across missing values.

Value

ggplot2 object

Examples

# Create plot for construct x
plot_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 arguments
plot_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 using simulateData.

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	See specify_bi_lcsm
model_x	See specify_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	See specify_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	See specify_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, see specify_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, see simulateData
...	Arguments to be passed on to simulateData
var_x	See specify_bi_lcsm
var_y	See specify_bi_lcsm
change_letter_x	See specify_bi_lcsm
change_letter_y	See specify_bi_lcsm
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

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 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)

---

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 in var_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 in var_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 LCSM
lavaan_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() function
cat(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 in variable. 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 LCSM
lavaan_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() function
cat(lavaan_uni_lcsm_01)

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