psych (version 1.3.2)

structure.diagram: Draw a structural equation model specified by two measurement models and a structural model

Description

Graphic presentations of structural equation models are a very useful way to conceptualize sem and confirmatory factor models. Given a measurement model on x (xmodel) and on y (ymodel) as well as a path model connecting x and y (phi), draw the graph. If ymodel is not specified, just draw the measurement model (xmodel + phi). If the Rx or Ry matrices are specified, show the correlations between the x variables, or y variables.

Perhaps even more usefully, the function returns a model appropriate for running directly in the sem package written by John Fox. For this option to work directly, it is necessary to specfy that errrors=TRUE.

Input can be specified as matrices or the output from fa, factor.pa, factanal, or a rotation package such as GPArotation.

For symbolic graphs, the input matrices can be character strings or mixtures of character strings and numeric vectors.

As an option, for those without Rgraphviz installed, structure.sem will just create the sem model and skip the graph. (This functionality is now included in structure.diagram.)

structure.diagram will draw the diagram without using Rgraphviz and is probably the preferred option. structure.graph will be removed eventually.

lavaan.diagram will draw either cfa or sem results from the lavaan package (> .4.0)

Usage

structure.diagram(fx, Phi=NULL,fy=NULL,labels=NULL,cut=.3,errors=FALSE,simple=TRUE,regression=FALSE,lr=TRUE,Rx=NULL,Ry=NULL,digits=1,e.size=.1,main="Structural model", ...)
structure.graph(fx,  Phi = NULL,fy = NULL, out.file = NULL, labels = NULL, cut = 0.3, errors=TRUE, simple=TRUE,regression=FALSE, size = c(8, 6), node.font = c("Helvetica", 14), edge.font = c("Helvetica", 10), rank.direction = c("RL", "TB", "LR", "BT"), digits = 1, title = "Structural model", ...)
structure.sem(fx,  Phi = NULL, fy = NULL,out.file = NULL, labels = NULL, cut = 0.3, errors=TRUE, simple=TRUE,regression=FALSE)
lavaan.diagram(fit,title,...)

Arguments

fx
a factor model on the x variables.
Phi
A matrix of directed relationships. Lower diagonal values are drawn. If the upper diagonal values match the lower diagonal, two headed arrows are drawn. For a single, directed path, just the value may be specified.
fy
a factor model on the y variables (can be empty)
Rx
The correlation matrix among the x variables
Ry
The correlation matrix among the y variables
out.file
name a file to send dot language instructions.
labels
variable labels if not specified as colnames for the matrices
cut
Draw paths for values > cut
fit
The output from a lavaan cfa or sem
errors
draw an error term for observerd variables
simple
Just draw one path per x or y variable
regression
Draw a regression diagram (observed variables cause Y)
lr
Direction of diagram is from left to right (lr=TRUE, default) or from bottom to top (lr=FALSE)
e.size
size of the ellipses in structure.diagram
main
main title of diagram
size
page size of graphic
node.font
font type for graph
edge.font
font type for graph
rank.direction
Which direction should the graph be oriented
digits
Number of digits to draw
title
Title of graphic
...
other options to pass to Rgraphviz

Value

  • sem(invisible) a model matrix (partially) ready for input to John Fox's sem package. It is of class ``mod" for prettier output.
  • dotfileIf out.file is specified, a dot language file suitable for using in a dot graphics program such as graphviz or Omnigraffle.
  • A graphic structural diagram in the graphics window

Details

The recommended function is structure.diagram which does not use Rgraphviz but which does not produce dot code either.

All three function return a matrix of commands suitable for using in the sem package. (Specify errors=TRUE to get code that will run directly in the sem package.)

The structure.graph output can be directed to an output file for post processing using the dot graphic language but requires that Rgraphviz is installed.

The figure is organized to show the appropriate paths between:

The correlations between the X variables (if Rx is specified) The X variables and their latent factors (if fx is specified) The latent X and the latent Y (if Phi is specified) The latent Y and the observed Y (if fy is specified) The correlations between the Y variables (if Ry is specified) A confirmatory factor model would specify just fx and Phi, a structural model would include fx, Phi, and fy. The raw correlations could be shown by just including Rx and Ry.

lavaan.diagram may be called from the diagram function which also will call fa.diagram, omega.diagram or iclust.diagram, depending upon the class of the fit.

Other diagram functions include fa.diagram, omega.diagram. All of these functions use the various dia functions such as dia.rect, dia.ellipse, dia.arrow, dia.curve, dia.curved.arrow, and dia.shape.

See Also

fa.graph, omega.graph, sim.structural to create artificial data sets with particular structural properties.

Examples

Run this code
fx <- matrix(c(.9,.8,.6,rep(0,4),.6,.8,-.7),ncol=2)
fy <- matrix(c(.6,.5,.4),ncol=1)
Phi <- matrix(c(1,0,0,0,1,0,.7,.7,1),ncol=3,byrow=TRUE)
f1 <- structure.diagram(fx,Phi,fy,main="A structural path diagram")

#symbolic input
X2 <- matrix(c("a",0,0,"b","e1",0,0,"e2"),ncol=4)
colnames(X2) <- c("X1","X2","E1","E2")
phi2 <- diag(1,4,4)
phi2[2,1] <- phi2[1,2] <- "r"
f2 <- structure.diagram(X2,Phi=phi2,errors=FALSE,main="A symbolic model") 

#symbolic input with error 
X2 <- matrix(c("a",0,0,"b"),ncol=2)
colnames(X2) <- c("X1","X2")
phi2 <- diag(1,2,2)
phi2[2,1] <- phi2[1,2] <- "r"
f3 <- structure.diagram(X2,Phi=phi2,main="an alternative representation")

#and yet another one
X6 <- matrix(c("a","b","c",rep(0,6),"d","e","f"),nrow=6)
colnames(X6) <- c("L1","L2")
rownames(X6) <- c("x1","x2","x3","x4","x5","x6")
Y3 <- matrix(c("u","w","z"),ncol=1)
colnames(Y3) <- "Y"
rownames(Y3) <- c("y1","y2","y3")
phi21 <- matrix(c(1,0,"r1",0,1,"r2",0,0,1),ncol=3)
colnames(phi21) <- rownames(phi21) <-  c("L1","L2","Y")
f4 <- structure.diagram(X6,phi21,Y3)



# and finally, a regression model
X7 <- matrix(c("a","b","c","d","e","f"),nrow=6)
f5 <- structure.diagram(X7,regression=TRUE)

#and a really messy regession model
x8 <- c("b1","b2","b3")
r8 <- matrix(c(1,"r12","r13","r12",1,"r23","r13","r23",1),ncol=3)
f6<- structure.diagram(x8,Phi=r8,regression=TRUE)

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