| spatialGreen {phenopix} | R Documentation |
Process pixel-based greenness indexes
Description
This function allows to filter, fit a curve and extract thresholds in a
pixel-based analysis exactly as autoFilter and greenProcess
do in a ROI-based analysis, except that uncertainty cannot be estimated
(since it would be too computationally intense)
Usage
spatialGreen(filtered.data, fit, threshold, ncores='all',
log.file=NULL)
Arguments
filtered.data |
A list as in output from |
fit |
A character vector of length 1. Available options are: |
threshold |
A character vector of length 1. Available options are: |
ncores |
Number of processors to be used in parallel computation, defaults to 'all' which will accidentally slow down any other activity on your computer. Otherwise set the number of processors you want to use in parallelization. |
log.file |
It can be NULL or a path where to generate and refresh a txt file which logs the progress of the filtering procedure |
Details
This function allows to fit a curve and extract thresholds in a
pixel-based analysis exactly as greenProcess
does in a ROI-based analysis, except that uncertainty cannot be estimated
(since it would be too computationally intense). This function takes as first argument
a list as in output from spatialFilter. For each pixel
in the ROI the function fits a curve (according to options specified in fit) and
extracts thresholds (as defined in threshold). This function performs the same task
that greenProcess does in a ROI-based analysis, except that uncertainty cannot be estimated
(since it would be too computationally intense). For pixel-based analysis, it is recommended to use
rather low resolution images or split your region of interest into multiple subROIs (function splitROI.
A specific vignette for spatial analysis is stored as pdf in the package folder. The user is adviced to carefully read it before
starting a spatial analysis.
Author(s)
Gianluca Filippa <gian.filippa@gmail.com>