Predict group of new samples

# S3 method for class 'PLNLDAfit'
predict(
  object,
  newdata,
  type = c("posterior", "response", "scores"),
  scale = c("log", "prob"),
  prior = NULL,
  control = PLN_param(backend = "nlopt"),
  ...
)

Arguments

object: an R6 object with class PLNLDAfit
newdata: A data frame in which to look for variables, offsets and counts with which to predict.
type: The type of prediction required. The default are posterior probabilities for each group (in either unnormalized log-scale or natural probabilities, see "scale" for details), "response" is the group with maximal posterior probability and "scores" is the average score along each separation axis in the latent space, with weights equal to the posterior probabilities.
scale: The scale used for the posterior probability. Either log-scale ("log", default) or natural probabilities summing up to 1 ("prob").
prior: User-specified prior group probabilities in the new data. If NULL (default), prior probabilities are computed from the learning set.
control: a list for controlling the optimization. See PLN() for details.
...: additional parameters for S3 compatibility. Not used

Value

A matrix of posterior probabilities for each group (if type = "posterior"), a matrix of (average) scores in the latent space (if type = "scores") or a vector of predicted groups (if type = "response").

Examples

data(trichoptera)
trichoptera <- prepare_data(trichoptera$Abundance, trichoptera$Covariate)
myLDA <- PLNLDA(Abundance ~ 0 + offset(log(Offset)),
                grouping = Group,
                data = trichoptera)
#> 
#>  Performing discriminant Analysis...
#>  DONE!
if (FALSE) { # \dontrun{
post_probs <- predict(myLDA, newdata = trichoptera, type = "posterior", scale = "prob")
head(round(post_probs, digits = 3))
predicted_group <- predict(myLDA, newdata = trichoptera, type = "response")
table(predicted_group, trichoptera$Group, dnn = c("predicted", "true"))
} # }