```
set.seed(42)
x <- sim.item(12,500)
f2 <- fa(x[1:250,],2,scores="regression") # a two factor solution
p2 <- principal(x[1:250,],2,scores=TRUE) # a two component solution
round(cor(f2$scores,p2$scores),2) #correlate the components and factors from the A set
#find the predicted scores (The B set)
pf2 <- predict(f2,x[251:500,],x[1:250,])
#use the original data for standardization values
pp2 <- predict(p2,x[251:500,],x[1:250,])
#standardized based upon the first set
round(cor(pf2,pp2),2) #find the correlations in the B set
#test how well these predicted scores match the factor scores from the second set
fp2 <- fa(x[251:500,],2,scores=TRUE)
round(cor(fp2$scores,pf2),2)
pf2.n <- predict(f2,x[251:500,]) #Standardized based upon the new data set
round(cor(fp2$scores,pf2.n))
#predict factors of set two from factors of set 1, factor order is arbitrary
#note that the signs of the factors in the second set are arbitrary
# \donttest{
#predictions from bestScales
#the derivation sample
bs <- bestScales(psychTools::bfi[1:1400,], cs(gender,education,age),folds=10,p.keyed=.5)
pred <- predict(bs,psychTools::bfi[1401:2800,]) #The prediction sample
cor2(pred,psychTools::bfi[1401:2800,26:28] ) #the validity of the prediction
summary(bs) #compare with bestScales cross validations
# }
```

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