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"),
...
)
```

- 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.

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).

```
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)
}
```