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.
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)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.
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.
fa.graph, omega.graph, sim.structural to create artificial data sets with particular structural properties.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)Run the code above in your browser using DataLab