```
corPlot(Thurstone,main="9 cognitive variables from Thurstone")
#just blue implies positive manifold
#select just some variables to plot
corPlot(Thurstone, zlim=c(0,1),main="9 cognitive variables from Thurstone",select=c(1:3,7:9))
#now show a non-symmetric plot
corPlot(Thurstone[4:9,1:3], zlim=c(0,1),main="9 cognitive variables
from Thurstone",numbers=TRUE,symmetric=FALSE)
#Two ways of including stars to show significance
#From the raw data
corPlot(sat.act,numbers=TRUE,stars=TRUE)
#from a correlation matrix with pvals
cp <- corTest(sat.act) #find the correlations and pvals
r<- cp$r
p <- cp$p
corPlot(r,numbers=TRUE,diag=FALSE,stars=TRUE, pval = p,main="Correlation plot
with Holm corrected 'significance'")
#now red means less than .5
corPlot(mat.sort(Thurstone),TRUE,zlim=c(0,1),
main="9 cognitive variables from Thurstone (sorted by factor loading) ")
simp <- sim.circ(24)
corPlot(cor(simp),main="24 variables in a circumplex")
#scale by raw and adjusted probabilities
rs <- corTest(sat.act[1:200,] ) #find the probabilities of the correlations
corPlot(r=rs$r,numbers=TRUE,pval=rs$p,main="Correlations scaled by probability values")
#Show the upper and lower confidence intervals
corPlotUpperLowerCi(R=rs,numbers=TRUE)
#now do this again, but with lighter colors
gr <- colorRampPalette(c("#B52127", "white", "#2171B5"))
corPlot(r=rs$r,numbers=TRUE,pval=rs$p,main="Correlations scaled by probability values",gr=gr)
corPlotUpperLowerCi(R=rs,numbers=TRUE,gr=gr)
#do multiple plots
#Also show the xaxis option
op <- par(mfrow=c(2,2))
corPlot(psychTools::ability,show.legend=FALSE,keep.par=FALSE,upper=FALSE)
f4 <- fa(psychTools::ability,4)
corPlot(f4,show.legend=FALSE,keep.par=FALSE,numbers=TRUE,xlas=3)
om <- omega(psychTools::ability,4)
corPlot(om,show.legend=FALSE,keep.par=FALSE,numbers=TRUE,xaxis=3)
par(op)
corPlotUpperLowerCi(rs,adjust=TRUE,main="Holm adjusted confidence intervals",gr=gr)
```

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