smooth_ts: Smooth a PhenoCam time series

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

Smooth a PhenoCam time series

Description

Smooths time series iteratively using a Akaike information criterion (AIC) to find an optimal smoothing parameter and curve.

Usage

smooth_ts(
  data,
  metrics = c("gcc_mean", "gcc_50", "gcc_75", "gcc_90", "rcc_mean", "rcc_50", "rcc_75",
    "rcc_90"),
  force = TRUE,
  internal = TRUE,
  out_dir = tempdir()
)

Arguments

data	a PhenoCam data file or data structure
metrics	which metrics to process, normally all default ones
force	TRUE / FALSE, force reprocessing?
internal	return a data structure if given a file on disk (TRUE / FALSE = default)
out_dir	output directory where to store data

Value

An PhenoCam data structure or file with optimally smoothed time series objects added to the original file. Smoothing is required for 'phenophase()' and 'transition_dates()' functions.

Examples

## Not run: 
# with defaults, outputting a data frame
# with smoothed values, overwriting the original

# download demo data (do not smooth)
download_phenocam(site = "harvard$",
                  veg_type = "DB",
                  roi_id = "1000",
                  frequency = "3",
                  smooth = FALSE)

# smooth the downloaded file (and overwrite the original)
smooth_ts(file.path(tempdir(),"harvard_DB_1000_3day.csv"))

# the function also works on a PhenoCam data frame
df <- read_phenocam(file.path(tempdir(),"harvard_DB_1000_3day.csv"))
df <- smooth_ts(df)

## End(Not run)

bluegreen-labs/phenocamr documentation built on Feb. 6, 2024, 10:42 p.m.