Finds various measures of the amplitude of the annual cycle, or of some specified season range.

Arguments

x

A seasonal time series, or a class zoo object.

season.range

A vector of two numbers specifying the season range to be considered.

Value

A matrix of class ts or zoo with individual series for the range, variance, median absolute deviation, mean, median and -- in the case of zoo objects -- number of samples.

Details

phenoAmp gives three measures of the amplitude of a seasonal cycle: the range, the variance, and the median absolute deviation, along with the mean and median to allow calculation of other statistics as well.

These measures can be restricted to a subset of the year by giving the desired range of season numbers. This can be useful for isolating measures of, say, the spring and autumn phytoplankton blooms in temperate waters. In the case of a monthly time series, for example, a non-missing value is required for every month or the result will be NA, so using a period shorter than one year can also help avoid any months that are typically not covered by the sampling program. Similarly, in the case of dated observations, a shorter period can help avoid times of sparse data. The method for time series allows for other than monthly frequencies, but season.range is always interpreted as months for zoo objects.

Note that the amplitude is sensitive to the number of samples for small numbers. This could be a problem for zoo objects if the sample number is changing greatly from year to year, depending on the amplitude measure and the underlying data distribution. So use ts objects or make sure that the sample number stays more or less the same over time.

tsMake can be used to produce ts and zoo objects suitable as arguments to this function.

References

Cloern, J.E. and Jassby, A.D. (2008) Complex seasonal patterns of primary producers at the land-sea interface. Ecology Letters 11, 1294--1303.

See also

Examples


y <- sfbayChla[, "s27"]
phenoAmp(y) # entire year
#> Time Series:
#> Start = 1978 
#> End = 2009 
#> Frequency = 1 
#>          range        var      mad     mean   median
#> 1978        NA         NA       NA       NA       NA
#> 1979        NA         NA       NA       NA       NA
#> 1980  9.083333   6.334411 0.518910 2.461111 1.658333
#> 1981        NA         NA       NA       NA       NA
#> 1982        NA         NA       NA       NA       NA
#> 1983        NA         NA       NA       NA       NA
#> 1984        NA         NA       NA       NA       NA
#> 1985        NA         NA       NA       NA       NA
#> 1986        NA         NA       NA       NA       NA
#> 1987  5.240000   3.052736 0.889560 2.609167 1.950000
#> 1988        NA         NA       NA       NA       NA
#> 1989        NA         NA       NA       NA       NA
#> 1990        NA         NA       NA       NA       NA
#> 1991        NA         NA       NA       NA       NA
#> 1992        NA         NA       NA       NA       NA
#> 1993        NA         NA       NA       NA       NA
#> 1994        NA         NA       NA       NA       NA
#> 1995        NA         NA       NA       NA       NA
#> 1996 11.033333  10.166372 0.904386 2.850694 1.500000
#> 1997        NA         NA       NA       NA       NA
#> 1998        NA         NA       NA       NA       NA
#> 1999        NA         NA       NA       NA       NA
#> 2000 37.720000 140.330088 1.564143 9.101667 3.245000
#> 2001        NA         NA       NA       NA       NA
#> 2002        NA         NA       NA       NA       NA
#> 2003 28.240000  90.367287 1.497426 8.540625 4.510000
#> 2004 14.786667  15.135511 1.536962 5.670000 4.870000
#> 2005        NA         NA       NA       NA       NA
#> 2006        NA         NA       NA       NA       NA
#> 2007        NA         NA       NA       NA       NA
#> 2008 24.390000  55.099670 3.746036 8.413056 5.870000
#> 2009        NA         NA       NA       NA       NA
# i.e., Jan-Jun only, which yields results for more years
phenoAmp(y, c(1, 6))
#> Time Series:
#> Start = 1978 
#> End = 2009 
#> Frequency = 1 
#>          range        var       mad      mean    median
#> 1978  4.450000   2.312417  1.111950  2.908333  2.750000
#> 1979  3.033333   1.445630  0.766010  2.822222  2.350000
#> 1980  8.900000  11.274519  0.728945  3.505556  2.100000
#> 1981        NA         NA        NA        NA        NA
#> 1982  6.509444   7.006464  3.572860  5.798750  5.515972
#> 1983        NA         NA        NA        NA        NA
#> 1984  8.583333   9.666513  1.188551  3.700556  2.858333
#> 1985       NaN         NA        NA       NaN        NA
#> 1986 24.263333  90.404759  2.057108  6.241574  2.772222
#> 1987  4.500000   3.961750  2.260965  3.625000  3.975000
#> 1988        NA         NA        NA        NA        NA
#> 1989        NA         NA        NA        NA        NA
#> 1990       NaN         NA        NA       NaN        NA
#> 1991  6.462857   6.461304  3.291372  4.453810  4.310000
#> 1992 10.005000  13.333374  1.882902  4.521389  3.385000
#> 1993 20.793333  68.078331  1.193493  6.603889  2.325000
#> 1994 11.260000  15.512343  2.189306  5.137444  4.239000
#> 1995 21.428333  66.623200  7.267211  8.658611  6.491667
#> 1996 11.033333  18.452134  2.668680  4.084722  2.510000
#> 1997 11.260000  22.888277  5.644999  6.336667  5.427500
#> 1998 18.438333  68.247959  4.420619  8.944278  5.205000
#> 1999 11.790000  25.408630  2.861418  6.265000  4.070000
#> 2000 11.650000  21.107987  1.178667  5.693333  3.270000
#> 2001        NA         NA        NA        NA        NA
#> 2002        NA         NA        NA        NA        NA
#> 2003 26.100000 144.565841  3.654609 13.209583  6.805000
#> 2004 14.786667  29.404031  2.367218  6.423333  5.120000
#> 2005  6.190000   4.819497  2.149770  5.278333  4.820000
#> 2006        NA         NA        NA        NA        NA
#> 2007        NA         NA        NA        NA        NA
#> 2008 24.083333  88.733597 10.158281 11.946111 10.195000
#> 2009  6.370000   5.986723  1.742055  4.338889  3.628333