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"))
} # }
```