calibrate_sicktree_model: Calibrate vegetation distribution models

In pieterbeck/CanHeMonR: Tools for canopy health monitoring through image analysis

Description

For each class in .shp polygon file, calibrate a distribution model using a raster brick as predictors

Usage

calibrate_sicktree_model(r_train, vuln_classes, Pols, field_name,
  model_outp_dir, train_samp = 100)

Arguments

r_train	A rasterstack or rasterbrick with layers used as predictor variables in the model.
vuln_classes	A list of the classes you want to model. The list can contain one or more vectors. Each vector represents a seperate vegetation class and response variable for the model and the vector elements are synonyms used to describe that class. The fist place in each vector will be used in the output name used to store the calibrated model, so it should not contain spaces. The other places should appear as attributes in the field 'field_name' of Pols.
Pols	SpatialPolygonsDataFrame of which one field contains the vuln.classe
field_name	The field in AOI.filename that contains the vuln_classes
model_outp_dir	The folder and filename prefix to save the model objects to
train_samp	How many 'presence' pixels should be randomly selected to train the model? Default is 100.

Value

Saves class-specific distribution models, using image layers as inputs

Note

Run in 32-bit R installation. Do you need a 'require(rJava)?'. Implement optional parallel

pieterbeck/CanHeMonR documentation built on May 25, 2019, 7:11 a.m.