View source: R/mixture_summaries.R
| class_prob | R Documentation | 
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. | 
The following types are available:
A summary table of the posterior class probabilities; this indicates what proportion of your data contributes to each class.
A summary table of the most likely class membership, based on the highest posterior class probability. Note that this is subject to measurement error.
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.
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.
The posterior probability matrix, with dimensions n (number of cases in the data) x k (number of classes).
A data.frame.
## Not run: 
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)
## End(Not run)
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