getPreds {fuzzySim}

Get model predictions

Description

This function allows getting the predictions of multiple models when applied to a given dataset. It can be useful if you have a list of model objects (e.g. resulting from multGLM) and want to apply them to a new data set containing the same variables for another region or time period. There are options to include the logit link ('Y') and/or 'Favourability' (see Fav).

Usage

getPreds(data, models, id.col = NULL, Y = FALSE, P = TRUE, 
Favourability = TRUE, incl.input = FALSE, verbosity = 2)

Arguments

data

an object of class either 'data.frame' or 'RasterStack' to which to apply the 'models' (below) to get their predictions; must contain all variables (with the same names, case-sensitive) included in any of the 'models'.

models

an object of class 'list' containing one or more model objects, obtained e.g. with function glm or multGLM.

id.col

optionally, the index number of a column of 'data' containing row identifiers, to be included in the result. Ignored if incl.input = TRUE, or if 'data' is a RasterStack rather than a data frame.

Y

logical, whether to include the logit link (y) value in the predictions.

P

logical, whether to include the probability value in the predictions.

Favourability

logical, whether to include Favourability in the predictions (see Fav).

incl.input

logical, whether to include input columns in the output data frame (if the 'data' input is a data frame too). The default is FALSE.

verbosity

numeric value indicating the amount of messages to display; currently meaningful values are 0, 1, and 2 (the default).

Value

This function returns the model predictions in an object of the same class as the input 'data', i.e. either a data frame or a RasterStack.

Author(s)

A. Marcia Barbosa

See Also

multGLM, predict

Examples

data(rotif.env)

names(rotif.env)


# identify rotifer data in the Eastern and Western hemispheres:

unique(rotif.env$CONTINENT)

rotif.env$HEMISPHERE <- "Eastern"

rotif.env$HEMISPHERE[rotif.env$CONTINENT %in%
c("NORTHERN_AMERICA", "SOUTHERN_AMERICA")] <- "Western"

head(rotif.env)


# separate the rotifer data into hemispheres

east.hem <- rotif.env[rotif.env$HEMISPHERE == "Eastern", ]
west.hem <- rotif.env[rotif.env$HEMISPHERE == "Western", ]


# make models for 3 of the species in rotif.env based on their distribution
# in the Eastern hemisphere:

mods <- multGLM(east.hem, sp.cols = 18:20, var.cols = 5:17, 
id.col = 1, step = FALSE, FDR = FALSE, trim = FALSE)


# get the models' predictions for the Western hemisphere dataset:

preds <- getPreds(west.hem, models = mods$models, P = TRUE, 
Favourability = TRUE)

head(preds)