predict.sjSDM {sjSDM} | R Documentation |
Predict from a fitted sjSDM model
Description
Predict from a fitted sjSDM model
Usage
## S3 method for class 'sjSDM'
predict(
object,
newdata = NULL,
SP = NULL,
Y = NULL,
type = c("link", "raw"),
dropout = FALSE,
...
)
Arguments
object |
a model fitted by |
newdata |
newdata for predictions |
SP |
spatial predictors (e.g. X and Y coordinates) |
Y |
Known occurrences of species, must be a matrix of the original size, species to be predicted must consist of NAs |
type |
raw or link |
dropout |
use dropout for predictions or not, only supported for DNNs |
... |
optional arguments for compatibility with the generic function, no function implemented |
Value
Matrix of predictions (sites by species)
Examples
## Not run:
## Conditional predictions based on focal species
com = simulate_SDM(sites = 200L)
## first 100 observations are the training data
model = sjSDM(com$response[1:100, ], com$env_weights[1:100,])
## Assume that for the other 100 observations, only the first species is missing
## and we want to use the other 4 species to improve the predictions:
Y_focal = com$response[101:200, ]
Y_focal[,1] = NA # set to NA because occurrences are unknown
pred_conditional = predict(model, newdata = com$env_weights[101:200,], Y = Y_focal)
pred_unconditional = predict(model, newdata = com$env_weights[101:200,])[,1]
## Compare performance:
Metrics::auc(com$response[101:200, 1], pred_conditional)
Metrics::auc(com$response[101:200, 1], pred_unconditional)
## Conditional predictions are better, however, it only works if occurrences of
## other species for new sites are known!
## End(Not run)
[Package sjSDM version 1.0.6 Index]