GetPoints: Sample points of presence points and background points and...

In MartinezLaura/CanHeMonR.MaxEnt: Application of MaxEnt for canopy health monitoring through airbone image analysis.

Description

Sample points of presence points and background points and save them in a serialized file.

Usage

GetPoints(tile_i, all_tifs, field_name, ninputs_tile, randompt, prob_tifs, Pols,
  vuln_classes, abs_samp, tile_dat)

Arguments

tile_i	Passed by the algorithm, name of the tile to run
all_tifs	Passed by the algorithm, list of all the tiles to run
field_name	Character. The field in AOI.filename that contains the vuln_classes
ninputs_tile	Integer. Number of inputs that we have fore each tile, including the tile, for exemple number of textures
randompt	Boolean. if True random points will be added in the tiles that doesn't have any visual point. Default TRUE
prob_tifs	Boolean (FALSE) if not wanted, Directory if wanted. tifs of each raster to run with 0 vaue for the areas that we don't want to sample and 1 fore the ones that we want to sample. Default FALSE
Pols	Passed by the algorithm, PolygonDataframe Object with all the visual points assesed
vuln_classes	A list of the classes you want to model The list can contain one or more vectors characters. Each element of the 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
abs_samp	Integer. How many 'absence' pixels should be randomly selected from eah tile to train the model. Default is 100.
tile_dat	A dataframe with the points of the prevoius iteration, if the previous iteration is the initial one, an empy dataframe will be passed

Value

A dataframe with the points done for the tile selected

MartinezLaura/CanHeMonR.MaxEnt documentation built on May 17, 2019, 6:21 p.m.