Predict counts of a new sample conditionally on a (set of) observed variables

```
predict_cond(
object,
newdata,
cond_responses,
type = c("link", "response"),
var_par = FALSE
)
# S3 method for PLNfit
predict_cond(
object,
newdata,
cond_responses,
type = c("link", "response"),
var_par = FALSE
)
```

## Arguments

- object
an R6 object with class `PLNfit`

- newdata
A data frame in which to look for variables and offsets with which to predict

- cond_responses
a data frame containing the counts of the observed variables (matching the names provided as data in the PLN function)

- type
The type of prediction required. The default is on the scale of the linear predictors (i.e. log average count)

- var_par
Boolean. Should new estimations of the variational parameters of mean and variance be sent back, as attributes of the matrix of predictions. Default to `FALSE`

.

## Value

A list containing:

- pred
A matrix of predicted log-counts (if `type = "link"`

) or predicted counts (if `type = "response"`

)

- M
A matrix containing E(Z_uncond | Y_c) for each given site.

- S
A matrix containing Var(Z_uncond | Y_c) for each given site (sites are the third dimension of the array)

## Examples

```
data(trichoptera)
trichoptera_prep <- prepare_data(trichoptera$Abundance, trichoptera$Covariate)
myPLN <- PLN(Abundance ~ Temperature + Wind, trichoptera_prep)
#>
#> Initialization...
#> Adjusting a full covariance PLN model with nlopt optimizer
#> Post-treatments...
#> DONE!
#Condition on the set of the first two species in the dataset (Hym, Hys) at the ten first sites
Yc <- trichoptera$Abundance[1:10, c(1, 2), drop=FALSE]
newX <- cbind(1, trichoptera$Covariate[1:10, c("Temperature", "Wind")])
pred <- predict_cond(myPLN, newX, Yc, type = "response")
```