Predict Method for gllvm Fits
Obtains predictions from a fitted generalized linear latent variable model object.
## S3 method for class 'gllvm' predict(object, newX = NULL, newTR = NULL, newLV = NULL, type = "link", ...)
object |
an object of class 'gllvm'. |
newX |
A new data frame of environmental variables. If omitted, the original matrix of environmental variables is used. |
newTR |
A new data frame of traits for each response taxon. If omitted, the original matrix of traits is used. |
newLV |
A new matrix of latent variables. If omitted, the original matrix of latent variables is used. |
type |
the type of prediction required. The default ( |
... |
not used. |
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.
A matrix containing requested predictor types.
Jenni Niku <jenni.m.e.niku@jyu.fi>, David Warton
# 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") 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")
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