ramLCM: Conduct growth curve analysis

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

Conduct growth curve analysis

Description

Conduct growth curve analysis

Usage

ramLCM(data, outcome, model = c("all", "no", "linear", "quadratic", "latent"), 
basis = 0:(length(outcome) - 1), predictor, equal.var = TRUE, digits = 3, 
ram.out = FALSE, ...)

Arguments

data	Data
outcome	Outcome variable indices
model	Models to fit
basis	Basis coefficients
predictor	Covariates as predictors
equal.var	Set residual variances to be equal
digits	Print digits
ram.out	Print ram matrices
...	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
fit	Model fit

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)
## Example 3. Growth curve models
gcm.all<-ramLCM(ex3, 1:6, ram.out=TRUE)
## plot the path diagram
bridge<-ramPathBridge(gcm.all$ram$latent, FALSE, FALSE)
## uncomment to plot
## plot(bridge, 'latent')

##unequal variance
gcm.all<-ramLCM(ex3, 1:6, ram.out=TRUE, equal.var=FALSE)

## missing data
gcm.all<-ramLCM(ex3, c(1,2,4,6), basis=c(1,2,4,6), ram.out=TRUE)

gcm.l<-ramLCM(ex3, 1:6, model='linear', ram.out=TRUE)

## with a predictor
gcm.pred<-ramLCM(ex3, c(1,2,4,6), model='linear', basis=c(1,2,4,6), 
                 predictor=c(3,5), ram.out=TRUE)
bridge3<-ramPathBridge(gcm.pred$ram$linear)
## uncomment to plot
## plot(bridge3, 'gcmlinear')

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