## Whereabouts of observations between multiple latent class models. Supplementary plot for LCA with poLCA.

I got the idea for the following plot and some of the code from a Stackoverflow question, where User D.L. Dahly tried to show how observations in „a model with class=(i) are distributed by the model with class = (i+1)“. I contribute through the idea of not using igraph, but the DiagrammeR-package, which generates an appealing plot with little code.

The plot tries to visualize how classifications of observations (persons) in a latent class analysis change over a sequence of LC-models with growing number of classes. I ran five models with one to five classes. The plot starts on top with the loglinear independence model that only has one class. The sample then splits in the 2-class LCA in a class with 146 and a class of 436 observations. Ellipse two and three are the classes 1 and 2 from the latent class model with two classes. In the next line of ellipses (four,five and six) you find the classes 1,2 and 3 of the latent class model with three classes. Ellipses seven, eight, nine, ten are classes 1,2,3 and 4 from the 4-class latent class model. The thickness of the ellipses and the arrows is according to the amount of observations.

Here is the R-code for it:

# first: estimate 5 latent class models f<-with(mydata, cbind(var1:varx)~1) lc1<-poLCA(f, data=mydata, nclass=1, na.rm = FALSE, nrep=30, maxiter=3000) #Loglinear independence model. lc2<-poLCA(f, data=mydata, nclass=2, na.rm = FALSE, nrep=30, maxiter=3000) lc3<-poLCA(f, data=mydata, nclass=3, na.rm = FALSE, nrep=30, maxiter=3000) lc4<-poLCA(f, data=mydata, nclass=4, na.rm = FALSE, nrep=30, maxiter=3000) lc5<-poLCA(f, data=mydata, nclass=5, na.rm = FALSE, nrep=30, maxiter=3000)

#--------------------------------- # PLOT #--------------------------------- library("DiagrammeR") library("V8") # This code stems from D.L. Dahly # build dataframe with predicted class for each observation x1<-rep(1, nrow(lc1$predclass)) x2<-lc2$predclass x3<-lc3$predclass x4<-lc4$predclass x5<-lc5$predclass results <- cbind(x1, x2, x3, x4, x5) results <-as.data.frame(results) results # avoid double naming of classes (because each LCA named their classes 1,2,...,k) N<-ncol(results) n<-0 for(i in 2:N) { results[,i]<- (results[,i])+((i-1)+n) n<-((i-1)+n) } # Make a data frame for the edges and counts # cross-tabulations and their frequencies g1<-plyr::count(results,c("x1","x2")) g2<-plyr::count(results,c("x2","x3")) colnames(g2)<-c("x1","x2","freq") g3<-plyr::count(results, c("x3","x4")) colnames(g3)<-c("x1", "x2","freq") g4<-plyr::count(results,c("x4","x5")) colnames(g4)<-c("x1","x2","freq") edges<-rbind(g1,g2,g3,g4) # Make a data frame for the class sizes h1<-plyr::count(results,c("x1")) h2<-plyr::count(results,c("x2")) colnames(h2)<-c("x1","freq") h3<- plyr::count(results,c("x3")) colnames(h3)<-c("x1","freq") h4<-plyr::count(results,c("x4")) colnames(h4)<-c("x1","freq") h5<-plyr::count(results,c("x5")) colnames(h5)<-c("x1", "freq") nodes<-rbind(h1,h2,h3,h4,h5)

Now, we use the data from edges and counts, as well as class sizes in DiagrammeR:

#dataframe for nodes - columns: node, label, type, attributes (like color and stuff) colnames(nodes)<-c("node","label") #scale nodes nodes <- scale_nodes(nodes_df = nodes, to_scale = nodes$label, node_attr = "penwidth", range = c(2, 5)) #dataframe for edges - columns: edge from, edge to, label, relationship, attributes colnames(edges)<-c("from", "to", "label") edges$relationship<-c("given_to") #scale edges edges <- scale_edges(edges_df = edges, to_scale = edges$label, edge_attr = "penwidth", range = c(1, 5)) nodes <- scale_nodes(nodes_df = nodes, to_scale = nodes$penwidth, node_attr = "alpha:fillcolor", range = c(5, 90)) nodes nodes$label2<-nodes$label nodes$label<-paste0(nodes$node) # Additional label outside of the ellipses # nodes$label<-paste0(nodes$node, "',xlabel=","'",nodes$label2) # Group-number #nodes$xlabel<-paste0("(n=",nodes$label2,")") #plot stuff lca_graph<-create_graph(nodes, edges, node_attrs = c("fontname = Helvetica", "color = darkgrey", "style = filled", "fillcolor = lightgrey", "alpha_fillcolor = 0.5"), edge_attrs = c("fontname = Helvetica", "fontsize=10"), graph_attrs=c("layout=dot", "overlap = false", "fixedsize = true", "directed=TRUE")) render_graph(lca_graph)

That´s it. DiagrammeR uses an algorithm to avoid overlapping. I tried some improvements of the plot, but decided to stick with this solution, because it´s already pretty nice. The only thing i miss in the plot are class-sizes. I tried to attach them with the „xlabel“-attribute in DiagrammeR, but the plot became to messy. You can try it yourself, by uncommenting this part:

nodes$label<-paste0(nodes$node, "',xlabel=","'",nodes$label2)

But i didn´t like it much.