Type: | Package |
Title: | Structural Equation Modeling Using the Reticular Action Model (RAM) Notation |
Version: | 0.5.1 |
Date: | 2023-08-26 |
Author: | Zhiyong Zhang, Jack McArdle, Aki Hamagami, & Kevin Grimm |
Maintainer: | Zhiyong Zhang <zzhang4@nd.edu> |
Description: | We rewrite of RAMpath software developed by John McArdle and Steven Boker as an R package. In addition to performing regular SEM analysis through the R package lavaan, RAMpath has unique features. First, it can generate path diagrams according to a given model. Second, it can display path tracing rules through path diagrams and decompose total effects into their respective direct and indirect effects as well as decompose variance and covariance into individual bridges. Furthermore, RAMpath can fit dynamic system models automatically based on latent change scores and generate vector field plots based upon results obtained from a bivariate dynamic system. Starting version 0.4, RAMpath can conduct power analysis for both univariate and bivariate latent change score models. |
Depends: | R (≥ 2.0), lavaan, ellipse, MASS |
License: | GPL-2 |
URL: | https://nd.psychstat.org |
NeedsCompilation: | no |
Repository: | CRAN |
Packaged: | 2023-08-27 13:07:17 UTC; zzhang4 |
Date/Publication: | 2023-08-27 13:30:02 UTC |
RAMpath for SEM analysis
Description
We rewrite of RAMpath software developed by John McArdle and Steven Boker as an R package. In addition to performing regular SEM analysis through the R package lavaan, RAMpath has unique features. First, it can generate path diagrams according to a given model. Second, it can display path tracing rules through path diagrams and decompose total effects into their respective direct and indirect effects as well as decompose variance and covariance into individual bridges. Furthermore, RAMpath can fit dynamic system models automatically based on latent change scores and generate vector field plots based upon results obtained from a bivariate dynamic system. Starting version 0.4, RAMpath can conduct power analysis for both univariate and bivariate latent change score models.
Details
Package: | RAMpath |
Type: | Package |
License: | GPL |
Author(s)
Zhiyong Zhang, Jack McArdle, Aki Hamagami, and Kevin Grimm Maintainer: Zhiyong Zhang <zhiyongzhang@nd.edu>
References
Boker, S. M., McArdle, J. J. & Neale, M. C. (2002) An algorithm for the hierarchical organization of path diagrams and calculation of components of covariance between variables. Structural Equation Modeling, 9(2), 174-194
Yves Rosseel (2012). lavaan: An R Package for Structural Equation Modeling. Journal of Statistical Software, 48(2), 1-36. URL https://www.jstatsoft.org/v48/i02/.
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
Example data set 1
Description
Three variables in the data set:
age:
hvlt: Hopkins Verbal Learning Test
ept: Everyday problem solving test
Usage
data(ex1)
Example data set 2
Description
Five variables in the data set:
edu
gender
word sets (ws1)
letter set (ls1)
letter series (lt1)
Usage
data(ex2)
Example data set 3
Description
12 variables in the data set:
X1-X6: data for variable X from time 1 to time 6.
Y1-Y6: data for variable X from time 1 to time 6.
Usage
data(ex3)
Is the input a numeric variable
Description
Check whether the input is a numeric variable
Usage
isNumeric(constant)
Arguments
constant |
A variable to check |
Value
TRUE or FALSE
Convert lavaan output to RAM matrices
Description
Convert lavaan output to RAM matrices
Usage
lavaan2ram(fitModel, digits = 2, zero.print = "0", ram.out = TRUE, fit = FALSE)
Arguments
fitModel |
A lavaan object generated by the function |
digits |
Digits for number print |
zero.print |
Format zeros in the matrix |
ram.out |
Whether print RAM matrices |
fit |
Whether print fit statistics |
Value
A and Ase |
A matrix and its standard errors |
S and Sse |
S matrix and its standard errors |
fit |
model fit |
lavaan |
The lavaan input, the same as fitModel |
Generate all bridges
Description
Generate all bridges based on Boker, McArdle, & Neale (2002)
Usage
makeBridgeList(pathList, spanList)
Arguments
pathList |
A path list from the function |
spanList |
A span list from the function |
References
Boker, S. M., McArdle, J. J. & Neale, M. C. (2002) An algorithm for the hierarchical organization of path diagrams and calculation of components of covariance between variables. Structural Equation Modeling, 9(2), 174-194
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
Make a list of effects
Description
Make a list of effects
Usage
makePathList(AMatrix, Ase, indirect = TRUE)
Arguments
AMatrix |
A matrix from the ram matrices |
Ase |
Standard error matrix for A matrix from the ram matrices |
indirect |
Whether to generate all indirect effects |
References
Boker, S. M., McArdle, J. J. & Neale, M. C. (2002) An algorithm for the hierarchical organization of path diagrams and calculation of components of covariance between variables. Structural Equation Modeling, 9(2), 174-194
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
Make a list of spans
Description
Make a list of spans
Usage
makeSpanList(SMatrix, Sse)
Arguments
SMatrix |
S matrix from the ram matrices |
Sse |
Standard error matrix for S matrix from the ram matrices |
References
Boker, S. M., McArdle, J. J. & Neale, M. C. (2002) An algorithm for the hierarchical organization of path diagrams and calculation of components of covariance between variables. Structural Equation Modeling, 9(2), 174-194
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
Plot the path diagram according to RAM path and bridges or Plot the vector field for the bivariate latent change score model
Description
Plot the path diagram according to RAM path and bridges or Plot the vector field for the bivariate latent change score model
Usage
## S3 method for class 'RAMpath'
plot(x, file, from, to, type = c("path", "bridge"),
size = c(8, 8), node.font = c("Helvetica", 14), edge.font = c("Helvetica", 10),
rank.direction = c("LR", "TB"), digits = 2, output.type = c("graphics", "dot"),
graphics.fmt = "pdf", dot.options = NULL, ...)
## S3 method for class 'blcs'
plot(x, ylim, xlim, ninterval=10, scale=.1, length=.25,
scatter=TRUE, n=20, alpha=.95, ...)
Arguments
x |
Output from the |
file |
File name for the generated figures |
from |
from variable: path starts from this variable |
to |
to variable: path ends on this variable |
type |
|
size |
The size of the plot in inches |
node.font |
The size of the text for the variables |
edge.font |
The size of the text on the pahts |
rank.direction |
LR: from left to right; TB: from top to bottom. |
digits |
Digits of numbers to plot |
output.type |
If "graphics", the default, both a ".dot" file and a graphics file will be created. |
graphics.fmt |
a graphics format recognized by the dot program; the default is "pdf"; graphics.fmt is also used for the extension of the graphics file that is created. |
dot.options |
options to be passed to the dot program, given as a character string. |
ylim |
Range of y data, for example, c(0,80) from 0 to 80 |
xlim |
Range of x data, for example, c(0,80) from 0 to 80 |
ninterval |
Number of intervals for plotting. The default is 10. |
scale |
Time interval to calculate vector fields. |
length |
The length of arrows to plot |
scatter |
Whether to plot the data points |
n |
The number of data points to be plotted |
alpha |
The confidence level to calculate the ellipse |
... |
Options for plot and arrows function. |
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.blcs<-ramBLCS(ex3, 1:6, 7:12, ram.out=TRUE)
ramVF(test.blcs, c(0,80),c(0,80), length=.05, xlab='X', ylab='Y',scale=.5, ninterval=9)
plot(test.blcs, c(0,80),c(0,80), length=.05, xlab='X', ylab='Y',scale=.5, ninterval=9)
Plot the power curve for each specified parameter
Description
Plot the power curve for each specified parameter
Usage
## S3 method for class 'lcs.power'
plot(x, parameter, ...)
Arguments
x |
Output from the |
parameter |
parameter to be plotted. |
... |
Options for the plot function. |
References
Zhang, Z., & Liu, H. (2018). Sample size and measurement occasion planning for latent change score models through Monte Carlo simulation. In E. Ferrer, S. M. Boker, and K. J. Grimm (Eds.), Advances in longitudinal models for multivariate psychology: A festschrift for Jack McArdle (pp. 189-211). New York, NY: Routledge.
Power analysis for bivariate latent change score models
Description
Calculate power for bivariate latent change score models based on Monte Carlo simulation.
Usage
powerBLCS(N=100, T=5, R=1000, betay=0, my0=0, mys=0, varey=1,
vary0=1, varys=1, vary0ys=0, alpha=0.05, betax=0, mx0=0,
mxs=0, varex=1, varx0=1, varxs=1, varx0xs=0, varx0y0=0,
varx0ys=0, vary0xs=0, varxsys=0, gammax=0, gammay=0, ...)
Arguments
N |
Sample size, can be a scalar or a vector. For better performance, make sure N is at least two times of T |
T |
Number of times, occasions or waves of measurements, can be a scalar or a vector |
R |
Number of replications to run in Monte Carlo simulation. Recommended 1000 or more |
betay |
Population parameter values |
my0 |
Population parameter values |
mys |
Population parameter values |
varey |
Population parameter values |
vary0 |
Population parameter values |
varys |
Population parameter values |
vary0ys |
Population parameter values |
betax |
Population parameter values |
mx0 |
Population parameter values |
mxs |
Population parameter values |
varex |
Population parameter values |
varx0 |
Population parameter values |
varxs |
Population parameter values |
varx0xs |
Population parameter values |
gammax |
Population parameter values |
gammay |
Population parameter values |
varx0y0 |
Population parameter values |
varx0ys |
Population parameter values |
vary0xs |
Population parameter values |
varxsys |
Population parameter values |
alpha |
Significance level |
... |
Options can be used for lavaan |
Value
A matrix with power for each parameter.
References
Zhang, Z., & Liu, H. (2018). Sample size and measurement occasion planning for latent change score models through Monte Carlo simulation. In E. Ferrer, S. M. Boker, and K. J. Grimm (Eds.), Advances in longitudinal models for multivariate psychology: A festschrift for Jack McArdle (pp. 189-211). New York, NY: Routledge.
Examples
## Not run:
powerBLCS(R=1000)
## End(Not run)
Power analysis for univariate latent change score models
Description
Calculate power for univariate latent change score models based on Monte Carlo simulation.
Usage
powerLCS(N=100, T=5, R=1000, betay=0, my0=0, mys=0,
varey=1, vary0=1, varys=1, vary0ys=0, alpha=0.05, ...)
Arguments
N |
Sample size, can be a scalar or a vector. For better performance, make sure N is at least two times of T |
T |
Number of times, occasions or waves of measurements, can be a scalar or a vector |
R |
Number of replications to run in Monte Carlo simulation. Recommended 1000 or more |
betay |
Population parameter values |
my0 |
Population parameter values |
mys |
Population parameter values |
varey |
Population parameter values |
vary0 |
Population parameter values |
varys |
Population parameter values |
vary0ys |
Population parameter values |
alpha |
Significance level |
... |
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., & Liu, H. (2018). Sample size and measurement occasion planning for latent change score models through Monte Carlo simulation. In E. Ferrer, S. M. Boker, and K. J. Grimm (Eds.), Advances in longitudinal models for multivariate psychology: A festschrift for Jack McArdle (pp. 189-211). New York, NY: Routledge.
Examples
## Not run:
powerLCS(R=1000)
## End(Not run)
RAM model to lavaan model
Description
Convert RAM matrix specification to a lavaan model
Usage
ram2lavaan(model)
Arguments
model |
An ram model |
References
Yves Rosseel (2012). lavaan: An R Package for Structural Equation Modeling. Journal of Statistical Software, 48(2), 1-36. URL http://www.jstatsoft.org/v48/i02/.
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
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 |
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)
Sobel standard error for a given effect
Description
Sobel standard error for a given effect
Usage
ramEffectSE(object, effect, path=TRUE)
Arguments
object |
An RAM path bridge output |
effect |
The effect to calculate se for. It is in the form a > b > c. |
path |
se for the direct and indirect effect. |
Fit a model using lavaan based on ram input
Description
Fit a model using lavaan based on ram input
Usage
ramFit(ramModel, data, type=c('ram','lavaan'), digits = 3, zero.print = "0", ...)
Arguments
ramModel |
An ram model |
data |
data |
type |
ram: specify a ram model; lavaan: specify a lavaan model |
digits |
Digits for print |
zero.print |
Format of zeros |
... |
Options for lavaan |
Value
A and Ase |
A matrix and its standard error |
S and Sse |
S matrix and its standard error |
lavaan |
Original lavaan output |
fit |
Model fit statistics and indices |
References
Yves Rosseel (2012). lavaan: An R Package for Structural Equation Modeling. Journal of Statistical Software, 48(2), 1-36. URL http://www.jstatsoft.org/v48/i02/.
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
## Example 1. A path model
data(ex1)
m1<-'
manifest=3
label=age,hvlt,ept
arrow(2,1)=?
arrow(3,1)=?
arrow(3,2)=?
sling(1,1)=?
sling(2,2)=?
sling(3,3)=?
'
## Fit the model
res1<-ramFit(m1, ex1)
## More output from Lavaan
summary(res1$lavaan, fit=TRUE)
## Effects and variance decomposition
bridge<-ramPathBridge(res1, allbridge=TRUE, indirect=TRUE)
summary(bridge)
summary(bridge, type='bridge')
## plot the path diagram
## uncomment to plot
## plot(bridge, 'ex1')
## plot the effects from age to ept
## uncomment to plot
## plot(bridge, 'ex1effect', 'age','ept')
## plot the bridges for ept
## uncomment to plot
## plot(bridge, 'ex1bridge', 'ept','hvlt', type='bridge')
## summarize
summary(bridge)
summary(bridge, type='bridge')
## Example 2: An SEM model (MIMIC model)
data(ex2)
## Using lavaan directly for model estimation and specification
mimic<-'
R =~ ws1 + ls1 + lt1
R ~ edu + gender
'
mimic.res<-sem(mimic, data=ex2)
mimic.ram<-lavaan2ram(mimic.res)
## plot the path diagram
bridge<-ramPathBridge(mimic.ram, allbridge=FALSE, indirect=FALSE)
## uncomment to plot
## plot(bridge, 'mimic')
Flip the ram path
Description
Flip the ram path
Usage
ramFlip(input)
Arguments
input |
An ram path |
To be added
Description
To be added
Usage
ramIndex(input)
Arguments
input |
To be added |
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$linear)
## uncomment to plot
## plot(bridge3, 'gcmlinear')
Univariate latent change score model
Description
Univariate latent change score model
Usage
ramLCS(data, y, timey, ram.out = FALSE, betay, my0, mys,
varey, vary0, varys, vary0ys, ...)
Arguments
data |
data |
y |
y data |
timey |
time of y |
ram.out |
Whether print ram matrices |
betay |
Starting value |
my0 |
Starting value |
mys |
Starting value |
varey |
Starting value |
vary0 |
Starting value |
varys |
Starting value |
vary0ys |
Starting value |
... |
Options can be used for lavaan |
Value
model |
The lavaan model specification of the bivariate latent change score model |
lavaan |
The lavaan output |
ram |
Output in terms of RAM matrices |
References
Zhang, Z., Hamagami, F., Grimm, K. J., & McArdle, J. J. (2015). Using R package RAMpath for tracing SEM path diagrams and conducting complex longitudinal data analysis. Structural Equation Modeling, 22(1), 132-147. https://doi.org/10.1080/10705511.2014.935257
Examples
data(ex3)
test.lcs<-ramLCS(ex3, 7:12)
summary(test.lcs$lavaan, fit=TRUE)
bridge<-ramPathBridge(test.lcs$ram, allbridge=FALSE, indirect=FALSE)
## uncomment to plot
## plot(bridge, 'lcs')
Generate ram matrices based on ram input
Description
Generate ram matrices based on ram input
Usage
ramMatrix(model)
Arguments
model |
An ram model |
lavaan to ram
Description
lavaan to ram matrices
Usage
ramParseLavaan(input, manifest, type = 0)
Arguments
input |
lavaan input |
manifest |
observed variables |
type |
0: single headed arrow, ... |
Generate path and bridges
Description
Generate path and bridges
Usage
ramPathBridge(rammatrix, allbridge = FALSE, indirect = TRUE)
Arguments
rammatrix |
RAM matrices |
allbridge |
Generate all bridges |
indirect |
Generate all indirect effects |
Refit a model with additional paths
Description
Generate a vector field plot based on the bivariate lcsm
Usage
ramReFit(object, add, ram.out=FALSE, ...)
Arguments
object |
Output from any data analysis |
add |
Additional paths to be added, e.g., add='X1~~X2'. |
ram.out |
Whether to print the RAM matrices |
... |
Options for plot and arrows function. |
Examples
data(ex3)
gcm.l<-ramLCM(ex3, 1:6, model='linear', ram.out=TRUE)
## Add correlated errors
ramReFit(gcm.l, add='X1~~X2')
Internal function
Description
Internal function
Usage
ramRmOne(input)
Arguments
input |
Internal function |
Show the model using Lavvan model syntax
Description
Show the model using Lavvan model syntax
Usage
ramShowModel(object)
Arguments
object |
Output from any data analysis |
References
Yves Rosseel (2012). lavaan: An R Package for Structural Equation Modeling. Journal of Statistical Software, 48(2), 1-36. URL http://www.jstatsoft.org/v48/i02/.
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)
gcm.l<-ramLCM(ex3, 1:6, model='linear', ram.out=TRUE)
## Add correlated errors
ramShowModel(gcm.l)
Get the uniques paths
Description
Get the uniques paths
Usage
ramUniquePath(tPathlist)
Arguments
tPathlist |
The path list. |
Generate a vector field plot based on the bivariate lcsm
Description
Generate a vector field plot based on the bivariate lcsm
Usage
ramVF(ramout, ylim, xlim, ninterval=10, scale=.1, length=.25,
scatter=TRUE, n=20, alpha=.95, ...)
Arguments
ramout |
Output from the ramBLCS function |
ylim |
Range of y data, for example, c(0,80) from 0 to 80 |
xlim |
Range of x data, for example, c(0,80) from 0 to 80 |
ninterval |
Number of intervals for plotting. The default is 10. |
scale |
Time interval to calculate vector fields. |
length |
The length of arrows to plot |
scatter |
Whether to plot the data points |
n |
The number of data points to be plotted |
alpha |
The confidence level to calculate the ellipse |
... |
Options for plot and arrows function. |
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.blcs<-ramBLCS(ex3, 1:6, 7:12, ram.out=TRUE)
ramVF(test.blcs, c(0,80),c(0,80), length=.05, xlab='X', ylab='Y',scale=.5, ninterval=9)
Calculate the total and individual contribution for each path and bridge
Description
Calculate the total and individual contribution for each path and bridge
Usage
## S3 method for class 'RAMpath'
summary(object, from, to, type = c("path", "bridge"), se=FALSE, ...)
Arguments
object |
Output from the ramPathBridge function |
from |
from variable: starting from this variable |
to |
to variable: end on this variable |
type |
path: to calculate the effect; bridge: to calculate the bridges |
se |
Whether to generate se for direct and indirect effects. |
... |
Other options |