sick_tree_errors: Sick tree error calculator
In pieterbeck/CanHeMonR: Tools for canopy health monitoring through image analysis

Calculate errors in automated detection of declining trees using visual inspection data as reference

1
2
3

sick_tree_errors(r_pred_dir, tile = "ALL", thresh = NA, vuln_classes, pnts,
  radius, field_name, abs_samp = 100, parallel = F, nWorkers = 4,
  data_outp_dir = NULL)

`r_pred_dir`	A directory where binary .tifs predicting presence as 1 and absence as 0 can be found for multiple tiles
`tile`	Character vector. Names of tile(s) to run. 'ALL will run all tiles in r_pred_dir. Default is 'ALL'
`thresh`	If the image data is non-binary, the value threhs can be set to split the image values between presence (> thresh) and absence (<= thresh). Default is NA, in which case the image values are assumed binary.
`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 pnts.
`pnts`	SpatialPointsDataFrame of which one field contains the vuln_classes
`radius`	The radius within which a presence point must be found for it to be considered 'correct'
`field_name`	The field in pnts that contains the vuln_classes
`abs_samp`	How many 'absence' pixels should be randomly selected from each tile to evaluate the absences? Default is 100.
`parallel`	Should the code be run in parallel using the doParallel package? Default is FALSE.
`nWorkers`	If running the ocde in parallel, how many workers should be used? Default is 4.
`data_outp_dir`	The folder and filename prefix to save the sampled data to. No data is saved is data_outp_dir is NULL. Default is NULL.