Predict counts conditionally — predict

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 class '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)

Methods (by class)

predict_cond(PLNfit): Predict counts of a new sample conditionally on a (set of) observed variables for a PLNfit

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")