timeDiff: Subtracts two time series by matching irregular time indexes
In Smisc: Sego Miscellaneous

Description Usage Arguments Details Value Author(s) Examples

Subtracts two time series by matching irregular time indexes. Can also be used to align the indexes of a time series to a set of standard time indexes.

1	timeDiff(v1, v2, n.ind = 1, full = FALSE)

`v1`	A time series vector: vector with dates or datetimes for names which are non repeating and in chronological order
`v2`	Another time series vector
`n.ind`	An integer >= 1 that indicates how many elements of the longer of the two vectors will be averaged and matched to the closest timestamp of the shorter vector. See details below.
`full`	`=TRUE` returns a data frame that shows in detail how the two vectors were matched and the difference calculated

The format for the timestamps (in the vector names) can be virtually any reasonable format, which will be converted to POSIXct by formatDT.

Suppose that v1 is shorter than v2. For each index of the v1, the n.ind indices of v2 that are closest in time to the v1 index are identified and "matched" (by averaging them) to the v1 index. In the case of ties, for example, the nearest v2 indexes are both 5 seconds before and 5 seconds after the v1 index of interest, then the closest v2 index in the past (5 seconds before) is matched to the v1 index.

Hence, the average of the n.ind elements of v2 that best "match" (are closest in time to) the element of v1 are subtracted from v1, which creates a series of differences with the same length (and timestamps) as v1.

If instead, v2 is shorter than v1, then the time stamps of v2 become the 'standard' to which the times of v1 are matched.

If v1 and v2 are the same length, then the timestamps of v2 are matched to v1 and the resulting vector of differences has the same timestamps as v1.

if full = FALSE then the difference (after matching) of (v1 - v2) is returned. Otherwise, a data frame is returned that shows how the vectors were matched and the resulting difference vector.

Landon Sego

data(timeDiff.eg)

# Show the objects
print(timeDiff.eg)

# Extract the objects from the list for easier use in the example
sepList(timeDiff.eg)

# Print warnings as they occur
op <- options(warn = 1)

# Show various differences
timeDiff(x1, x2, full = TRUE)
timeDiff(x2.d, x1.d, full = TRUE)
timeDiff(x1, x1)

options(op)

### If we need to average a time-series at 30 second invervals:

# Create the vector that will be averaged, with time stamps occuring
# about every 10 seconds
v1.names <- seq(formatDT("2009-09-12 3:20:31")$dt.posix,
                formatDT("2009-09-12 3:29:15")$dt.posix, by = 10)

# Now jitter the times a bit and look at the time spacing
v1.names <- v1.names + round(rnorm(length(v1.names), sd = 1.5))
diff(v1.names)

# Create the vector
v1 <- abs(rnorm(length(v1.names), mean = 7, sd = 3))
names(v1) <- v1.names

# Now create a standard vector with values of 0 with time stamps every 30 seconds
standard.names <- seq(formatDT("2009-09-12 3:21:30")$dt.posix,
                      formatDT("2009-09-12 3:28:30")$dt.posix, by = 30)
standard <- double(length(standard.names))
names(standard) <- standard.names

# Now average the v1 values by matching the 3 closest values to each standard time:
timeDiff(v1, standard, n.ind = 3, full = TRUE)
v1.avg <- timeDiff(v1, standard, n.ind = 3)

# Check that every 3 obs were averaged
v1.avg.check <- tapply(v1[6:50], rep(1:15, each = 3), mean)
max(abs(v1.avg.check - v1.avg))