Reorder latent classes in poLCA
A helper function to simplify the reordering of latent classes returned by poLCA.
poLCA.reorder(probs, o.new)
probs |
a list of class-conditional response probabilities previously used as start values to estimate a particular latent class model using |
o.new |
a vector of length equal to the number of latent classes in |
Because the latent classes outputted by poLCA are unordered categories, the numerical order of the classes is arbitrary, and is determined solely by the initial values of the EM algorithm. If probs.start is set to NULL (the default) when calling poLCA, then the function generates the starting values randomly in each run, typically rearranging the latent class labels. The poLCA.reorder function is a convenient way to manually adjust the order of the latent classes, by changing the order of the probs.start. Refitting the latent class model using these reordered start values will produce a model having the desired category labels.
The function returns a list of matrices containing the rearranged (by row) class-conditional response probabilities.
##
## Using the "cheating" sample data set, make the larger
## non-cheater class the first ("reference") class in a
## latent class regression model. The coefficient on GPA
## now maintains a consistent interpretation.
##
data(cheating)
f2 <- cbind(LIEEXAM,LIEPAPER,FRAUD,COPYEXAM)~GPA
lc.ch <- poLCA(f2,cheating,nclass=2,verbose=FALSE)
probs.start.new <- poLCA.reorder(lc.ch$probs.start,order(lc.ch$P,decreasing=TRUE))
lc.ch <- poLCA(f2,cheating,nclass=2,probs.start=probs.start.new)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.