bfast (version 1.6.1)

bfastts: Create a regular time series object by combining data and date information

Description

Create a regular time series object by combining measurements (data) and time (dates) information.

Usage

bfastts(data, dates, type = c("irregular", "16-day", "10-day"))

Value

bfastts returns an object of class "ts", i.e., a list with components as follows.

zz

a regular "ts" time series with a frequency equal to 365 or 23 i.e. 16-day time series.

Arguments

data

A data vector or matrix where columns represent variables

dates

Optional input of dates for each measurement in the 'data' variable. In case the data is a irregular time series, a vector with 'dates' for each measurement can be supplied using this 'dates' variable. The irregular data will be linked with the dates vector to create daily regular time series with a frequency = 365. Extra days in leap years might cause problems. Please be careful using this option as it is experimental. Feedback is welcome.

type

("irregular") indicates that the data is collected at irregular dates and as such will be converted to a daily time series. ("16-day") indicates that data is collected at a regular time interval (every 16-days e.g. like the MODIS 16-day data products). ("10-day") indicates that data is collected at a 10-day time interval of the SPOT VEGETATION (S10) product. Warning: Only use this function for the SPOT VEGETATION S10 time series, as for other 10-day time series a different approach might be required.

Author

Achim Zeileis, Jan Verbesselt

Details

bfastts create a regular time series

See Also

monitor, mefp, breakpoints

Examples

# 16-day time series (i.e. MODIS)
timedf <- data.frame(y = som$NDVI.b, dates = dates[1:nrow(som)])
bfastts(timedf$y, timedf$dates, type = "16-day")

# Irregular
head(bfastts(timedf$y, timedf$dates, type = "irregular"), 50)


if (FALSE) {
# Example of use with a raster

library("raster")
f <- system.file("extdata/modisraster.grd", package="bfast")
modisbrick <- brick(f)
ndvi <- bfastts(as.vector(modisbrick[1]), dates, type = c("16-day")) ## data of pixel 1
plot(ndvi/10000) 

# Time series of 4 pixels
modis_ts = t(as.data.frame(modisbrick))[,1:4]
# Data with multiple columns, 2-4 are external regressors
ndvi <- bfastts(modis_ts, dates, type = c("16-day"))
plot(ndvi/10000)
}