ramBLCS: Conduct bivariate latent change score analysis

In RAMpath: Structural Equation Modeling Using the Reticular Action Model (RAM) Notation

Conduct bivariate latent change score analysis

Description

Conduct bivariate latent change score analysis

Usage

ramBLCS(data, y, x, timey, timex, ram.out = FALSE, betax, 
betay, gammax, gammay, mx0, mxs, my0, mys, varex, varey, 
varx0, vary0, varxs, varys, varx0y0, varx0xs, vary0ys, 
varx0ys, vary0xs, varxsys, ...)

Arguments

data	Data
y	Indices for y variables
x	Indices for x variables
timey	Time for y variables
timex	Time for x variables
ram.out	whether print ram matrices
betax	Starting value
betay	Starting value
gammax	Starting value
gammay	Starting value
mx0	Starting value
mxs	Starting value
my0	Starting value
mys	Starting value
varex	Starting value
varey	Starting value
varx0	Starting value
vary0	Starting value
varxs	Starting value
varys	Starting value
varx0y0	Starting value
varx0xs	Starting value
vary0ys	Starting value
varx0ys	Starting value
vary0xs	Starting value
varxsys	Starting value
...	Options can be used for lavaan

Value

model	The lavaan model specification of the bivariate latent change score model
lavaan	The lavaan output
ram	Output in terms of RAM matrices

References

Zhang, Z., Hamagami, F., Grimm, K. J., & McArdle, J. J. (2015). Using R package RAMpath for tracing SEM path diagrams and conducting complex longitudinal data analysis. Structural Equation Modeling, 22(1), 132-147. https://doi.org/10.1080/10705511.2014.935257

Examples

data(ex3)
## Test the bivariate latent change score model ramBLCS

test.blcs<-ramBLCS(ex3, 7:12, 1:6, ram.out=TRUE)
summary(test.blcs$lavaan, fit=TRUE)

bridge<-ramPathBridge(test.blcs$ram, allbridge=FALSE,indirect=FALSE)
## uncomment to plot
## plot(bridge, 'blcs')


## Test the vector field plot
## test.blcs is the output of the ramBLCS function.
ramVF(test.blcs, c(0,80),c(0,80), length=.05, xlab='X', ylab='Y',scale=.5, ninterval=9)

RAMpath documentation built on Aug. 27, 2023, 5:06 p.m.