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