projectModel Calculates the probability ratio output (PRO) of a given
model for any points where values of the explanatory variables in the model
are known.The transformations performed on the explanatory variables to build
the model must be specified.
Data frame of all the explanatory variables (EVs) included in the
model, with column names matching EV names. See
Full pathway of the 'transformations.Rdata' file
containing the transformations used to build the model. This file is saved
as a result of the
Full pathway of the '.lambdas' file of the model in question.
This file is saved as a result of
Logical. Do clamping sensu Phillips et al. (2006).
Logical. Linearly rescale model output (PRO or raw) with
respect to the projection
Logical. Return raw Maxent output instead of probability ratio output (PRO)? Default is FALSE.
Missing data (NA) for a continuous variable will result in NA output for that point. Missing data for a categorical variable is treated as belonging to none of the categories.
rescale = FALSE the scale of the model output (PRO or raw)
returned by this function is dependent on the data used to train the model.
For example, a location with PRO = 2 can be interpreted as having a
probability of presence twice as high as an average site in the
training data (Halvorsen, 2013, Halvorsen et al., 2015). When
rescale = TRUE, the output is linearly rescaled with respect to the
data onto which the model is projected. In this case, a location with PRO = 2
can be interpreted as having a probability of presence twice as high as an
average site in the projection data. Similarly, raw values are on a
scale which is dependent on the size of either the training data extent
rescale = FALSE) or projection data extent (
rescale = TRUE).
List of 2:
A data frame with the model output in column 1 and the corresponding explanatory data in subsequent columns.
A data frame showing the range of
data compared to the
training data, on a 0-1 scale.
Halvorsen, R. (2013) A strict maximum likelihood explanation of MaxEnt, and some implications for distribution modelling. Sommerfeltia, 36, 1-132.
Halvorsen, R., Mazzoni, S., Bryn, A. & Bakkestuen, V. (2015) Opportunities for improved distribution modelling practice via a strict maximum likelihood interpretation of MaxEnt. Ecography, 38, 172-183.
Phillips, S.J., Anderson, R.P. & Schapire, R.E. (2006) Maximum entropy modeling of species geographic distributions. Ecological Modelling, 190, 231-259.
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## Not run: modeloutput <- projectModel(newdat, transformation = "D:/path/to/modeling/directory/deriveVars/transformations.Rdata", model = "D:/path/to/modeling/directory/selectEV/round/model/1.lambdas") ## End(Not run) proj <- projectModel(toydata_sp1po, toydata_dvs$transformations, system.file("extdata/sommerfeltia", "1.lambdas", package = "MIAmaxent")) proj ## Not run: # From vignette: grasslandPrediction <- projectModel(grasslandPO, transformation = grasslandDVs[], model = system.file("extdata", "1.lambdas", package = "MIAmaxent")) head(grasslandPrediction$output) grasslandPrediction$ranges # From vignette: library(raster) contfiles <- list.files(system.file("extdata", "EV_continuous", package = "MIAmaxent"), full.names = TRUE) catfiles <- list.files(system.file("extdata", "EV_categorical", package = "MIAmaxent"), full.names = TRUE) stack <- raster::stack(c(contfiles, catfiles)) stackpts <- rasterToPoints(stack) spatialPrediction <- projectModel(stackpts, transformation = grasslandDVs[], model = system.file("extdata", "1.lambdas", package = "MIAmaxent")) Predictionraster <- raster(stack, layer=0) Predictionraster <- rasterize(spatialPrediction$output[, c("x", "y")], Predictionraster, field = spatialPrediction$output$PRO) plot(Predictionraster, colNA="black") ## End(Not run)
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