### Correlations ###
# Load big5 dataset:
data(big5)
data(big5groups)
# Compute correlation matrix:
big5_cors <- cor_auto(big5, detectOrdinal = FALSE)
# Correlations:
big5Graph <- qgraph(cor(big5),minimum=0.25,groups=big5groups,
legend=TRUE,borders=FALSE, title = "Big 5 correlations")
# Same graph with spring layout:
qgraph(big5Graph,layout="spring")
# Same graph with different color scheme:
qgraph(big5Graph,posCol="blue",negCol="purple")
### Network analysis ###
### Using bfi dataset from psych ###
library("psych")
data(bfi)
# Compute correlations:
CorMat <- cor_auto(bfi[,1:25])
# Compute graph with tuning = 0 (BIC):
BICgraph <- qgraph(CorMat, graph = "glasso", sampleSize = nrow(bfi),
tuning = 0, layout = "spring", title = "BIC", details = TRUE)
# Compute graph with tuning = 0.5 (EBIC)
EBICgraph <- qgraph(CorMat, graph = "glasso", sampleSize = nrow(bfi),
tuning = 0.5, layout = "spring", title = "BIC", details = TRUE)
# Compare centrality and clustering:
centralityPlot(list(BIC = BICgraph, EBIC = EBICgraph))
clusteringPlot(list(BIC = BICgraph, EBIC = EBICgraph))
# Compute centrality and clustering:
centrality_auto(BICgraph)
clustcoef_auto(BICgraph)
### Directed unweighted graphs ###
set.seed(1)
adj=matrix(sample(0:1,10^2,TRUE,prob=c(0.8,0.2)),nrow=10,ncol=10)
qgraph(adj)
title("Unweighted and directed graphs",line=2.5)
# Save plot to nonsquare pdf file:
qgraph(adj,filetype='pdf',height=5,width=10)
#### EXAMPLES FOR EDGES UNDER DIFFERENT ARGUMENTS ###
# Create edgelist:
dat.3 <- matrix(c(1:15*2-1,1:15*2),,2)
dat.3 <- cbind(dat.3,round(seq(-0.7,0.7,length=15),1))
# Create grid layout:
L.3 <- matrix(1:30,nrow=2)
# Different esize:
qgraph(dat.3,layout=L.3,directed=FALSE,edge.labels=TRUE,esize=14)
# Different esize, strongest edges omitted (note how 0.4 edge is now
# just as wide as 0.7 edge in previous graph):
qgraph(dat.3[-c(1:3,13:15),],layout=L.3,nNodes=30,directed=FALSE,
edge.labels=TRUE,esize=14)
# Different esize, with maximum:
qgraph(dat.3,layout=L.3,directed=FALSE,edge.labels=TRUE,esize=14,maximum=1)
title("maximum=1",line=2.5)
qgraph(dat.3[-c(1:3,13:15),],layout=L.3,nNodes=30,directed=FALSE,edge.labels=TRUE,
esize=14,maximum=1)
title("maximum=1",line=2.5)
# Different minimum
qgraph(dat.3,layout=L.3,directed=FALSE,edge.labels=TRUE,esize=14,minimum=0.1)
title("minimum=0.1",line=2.5)
# With cutoff score:
qgraph(dat.3,layout=L.3,directed=FALSE,edge.labels=TRUE,esize=14,cut=0.4)
title("cut=0.4",line=2.5)
# With details:
qgraph(dat.3,layout=L.3,directed=FALSE,edge.labels=TRUE,esize=14,minimum=0.1,
maximum=1,cut=0.4,details=TRUE)
title("details=TRUE",line=2.5)
# Trivial example of manually specifying edge color and widths:
E <- as.matrix(data.frame(from=rep(1:3,each=3),to=rep(1:3,3),width=1:9))
qgraph(E,mode="direct",edge.color=rainbow(9))
### Input based on other R objects ###
## Exploratory factor analysis:
big5efa <- factanal(big5,factors=5,rotation="promax",scores="regression")
qgraph(big5efa,groups=big5groups,layout="circle",minimum=0.2,
cut=0.4,vsize=c(1.5,10),borders=FALSE,vTrans=200,title="Big 5 EFA")
## Principal component analysis:
library("psych")
big5pca <- principal(cor(big5),5,rotate="promax")
qgraph(big5pca,groups=big5groups,layout="circle",rotation="promax",minimum=0.2,
cut=0.4,vsize=c(1.5,10),borders=FALSE,vTrans=200,title="Big 5 PCA")
## pcalg
# Example from pcalg vignette:
library("pcalg")
data(gmI)
suffStat <- list(C = cor(gmI$x), n = nrow(gmI$x))
pc.fit <- pc(suffStat, indepTest=gaussCItest,
p = ncol(gmI$x), alpha = 0.01)
qgraph(pc.fit)
## glasso:
# Using bfi dataset from psych:
library("psych")
data(bfi)
cor_bfi <- cor_auto(bfi[,1:25])
# Run qgraph:
library("glasso")
bfi_glasso <- glasso(cor_bfi, 0.1)
# Plot:
qgraph(bfi_glasso, layout = "spring")
## Huge (glasso):
library("huge")
bfi_huge <- huge(huge.npn(bfi[,1:25]), method="glasso")
# Auto select optimal and plot:
qgraph(bfi_huge, layout = "spring")
# Manual select and plot:
sel <- huge.select(bfi_huge)
qgraph(sel, layout = "spring")Run the code above in your browser using DataLab