dtwSat (version 0.2.8)

createPatterns: Create patterns

Description

Create temporal patterns from objects of class twdtwTimeSeries.

Usage

createPatterns(
  x,
  from = NULL,
  to = NULL,
  freq = 1,
  attr = NULL,
  split = TRUE,
  formula,
  ...
)
# S4 method for twdtwTimeSeries
createPatterns(
  x,
  from = NULL,
  to = NULL,
  freq = 1,
  attr = NULL,
  split = TRUE,
  formula,
  ...
)

Value

an object of class twdtwTimeSeries

Arguments

x

an object of class twdtwTimeSeries.

from

A character or Dates object in the format "yyyy-mm-dd". If not provided it is equal to the smallest date of the first element in x. See details.

to

A character or Dates object in the format "yyyy-mm-dd". If not provided it is equal to the greatest date of the first element in x. See details.

freq

An integer. The sampling frequency of the output patterns.

attr

A vector character or numeric. The attributes in x to be used. If not declared the function uses all attributes.

split

A logical. If TRUE the samples are split by label. If FALSE all samples are set to the same label.

formula

A formula. Argument to pass to gam.

...

other arguments to pass to the function gam in the package mgcv.

Author

Victor Maus,

Details

The hidden assumption is that the temporal pattern is a cycle the repeats itself within a given time interval. Therefore, all time series samples in x are aligned with each other, keeping their respective sequence of days of the year. The function fits a Generalized Additive Model (GAM) to the aligned set of samples.

References

Maus:2019dtwSat

Maus:2016dtwSat

See Also

twdtwMatches-class, twdtwTimeSeries-class, getTimeSeries, and twdtwApply

Examples

# Creating patterns from objects of class twdtwTimeSeries 
evi = brick(system.file("lucc_MT/data/evi.tif", package="dtwSat"))
ndvi = brick(system.file("lucc_MT/data/ndvi.tif", package="dtwSat"))
timeline = scan(system.file("lucc_MT/data/timeline", package="dtwSat"), what="date")
rts = twdtwRaster(evi, ndvi, timeline=timeline)

# Read field samples 
if (FALSE) {
field_samples = read.csv(system.file("lucc_MT/data/samples.csv", package="dtwSat"))
prj_string = scan(system.file("lucc_MT/data/samples_projection", package="dtwSat"), 
                  what = "character")

# Extract time series 
ts = getTimeSeries(rts, y = field_samples, proj4string = prj_string)

# Create temporal patterns 
patt = createPatterns(x=ts, from="2005-09-01", to="2006-09-01", freq=8, formula = y~s(x))

# Plot patterns 
autoplot(patt[[1]], facets = NULL) + xlab("Time") + ylab("Value")

}