Obtains predictions from a fitted generalized linear latent variable model object.
# S3 method for gllvm
predict(
object,
newX = NULL,
newTR = NULL,
newLV = NULL,
type = "link",
level = 1,
offset = TRUE,
...
)an object of class 'gllvm'.
A new data frame of environmental variables. If omitted, the original matrix of environmental variables is used.
A new data frame of traits for each response taxon. If omitted, the original matrix of traits is used.
A new matrix of latent variables. If omitted, the original matrix of latent variables is used. Note that number of rows/sites must be the same for newX (if X covariates are included in the model).
the type of prediction required. The default ("link") is on the scale of the linear predictors; the alternative "response" is on the scale of the response variable. that is, the predictions for the binomial model are predicted probabilities. In case of ordinal data, type = "response" gives predicted probabilities for each level of ordinal variable.
specification for how to predict. Level one (level = 1) attempts to use the predicted site scores from variational approximations or laplace approximation or given site scores in newLV. Level 0 sets the latent variable to zero. Defaults to 1.
specification whether of not offset values are included to the predictions in case they are in the model, defaults to TRUE when offset values that are used to fit the model are included to the predictions. Alternatives are matrix/vector (number of rows must match with the newX) of new offset values or FALSE, when offsets are ignored.
not used.
A matrix containing requested predictor types.
If newX, newTR and newLV are omitted the predictions are based on the data used for fitting the model. Notice that newTR need to match with the number of species in the original data.
Instead, new sites can be specified in newX. If predictors newX (and newTR) are given, and newLV is not, latent variables are not used in the predictions.
# \donttest{
# Load a dataset from the mvabund package
data(antTraits)
y <- as.matrix(antTraits$abund)
X <- scale(antTraits$env[, 1:3])
# Fit gllvm model
fit <- gllvm(y = y, X, family = poisson())
# fitted values
predfit <- predict(fit, type = "response")
# linear predictors
predlin <- predict(fit)
# Predict new sites:
# Generate matrix of environmental variables for 10 new sites
xnew <- cbind(rnorm(10), rnorm(10), rnorm(10))
colnames(xnew) <- colnames(X)
predfit <- predict(fit, newX = xnew, type = "response", level = 0)
TR <- (antTraits$tr[, 1:3])
fitt <- gllvm(y = y, X, TR, family = poisson())
# linear predictors
predlin <- predict(fitt)
# Predict new sites:
# Generate matrix of environmental variables for 10 new sites
xnew <- cbind(rnorm(10), rnorm(10), rnorm(10))
colnames(xnew) <- colnames(X)
# Generate matrix of traits for species
trnew <- data.frame(Femur.length = rnorm(41), No.spines = rnorm(41),
Pilosity = factor(sample(0:3, 41, replace = TRUE)))
predfit <- predict(fitt, newX = xnew, newTR = trnew, type = "response", level = 0)
# }