Obtain latent class probabilities for an object for which a method exists. See Details.

class_prob(
x,
type = c("sum.posterior", "sum.mostlikely", "mostlikely.class", "avg.mostlikely",
"individual"),
...
)

## Arguments

x

An object for which a method exists.

type

Character vector, indicating which types of probabilities to extract. See Details.

...

Further arguments to be passed to or from other methods.

A data.frame.

## Details

The following types are available:

• "sum.posterior"A summary table of the posterior class probabilities; this indicates what proportion of your data contributes to each class.

• "sum.mostlikely"A summary table of the most likely class membership, based on the highest posterior class probability. Note that this is subject to measurement error.

• "mostlikely.class"If C is the true class of an observation, and N is the most likely class based on the model, then this table shows the probability P(N==i|C==j). The diagonal represents the probability that observations in each class will be correctly classified.

• "avg.mostlikely"Average posterior probabilities for each class, for the subset of observations with most likely class of 1:k, where k is the number of classes.

• "individual"The posterior probability matrix, with dimensions n (number of cases in the data) x k (number of classes).

## Examples

if (FALSE) {
df <- iris[, 1, drop = FALSE]
names(df) <- "x"
res <- mx_mixture(model = "x ~ m{C}*1
x ~~ v{C}*x", classes = 1, data = df)
class_prob(res)
}