detect_outliers: Detect outliers in PhenoCam time series

In phenocamr: Facilitates 'PhenoCam' Data Access and Time Series Post-Processing

Detect outliers in PhenoCam time series

Description

The function fills in the existing column to hold outlier flags, and either overwrites the original file or outputs a data structure.

Usage

detect_outliers(
  data,
  iterations = 20,
  sigma = 2,
  grvi = FALSE,
  snowflag = FALSE,
  plot = FALSE,
  internal = TRUE,
  out_dir = tempdir()
)

Arguments

data	PhenoCam data structure or filename
iterations	number of itterations in order to detect outliers ()
sigma	number of deviations to exclude outliers at
grvi	reverse the direction of the screening intervals to accomodate for GRVI outliers
snowflag	use manual snow flag labels as outliers
plot	visualize the process, mostly for debugging (TRUE / FALSE = default)
internal	return a data structure if given a file on disk (TRUE / FALSE = default) to accomodate for GRVI outliers
out_dir	output directory where to store data

Examples

## Not run: 
# download demo data (do not detect outliers)
download_phenocam(site = "harvard$",
                  veg_type = "DB",
                  roi_id = "1000",
                  frequency = "3",
                  outlier_detection = FALSE)

# detect outliers in the downloaded file
detect_outliers(file.path(tempdir(),"harvard_DB_1000_3day.csv"))


## End(Not run)

phenocamr documentation built on July 21, 2022, 1:06 a.m.