Convert monthly time series vector to a year x month data frame for
several possible subsequent analyses. Leading and trailing empty rows are
removed.
ts2df(x, mon1 = 1, addYr = FALSE, omit = FALSE)
| x | monthly time series vector |
|---|---|
| mon1 | starting month number, i.e., first column of the data frame |
| addYr | rows are normally labelled with the year of the starting month,
but |
| omit | if |
An n x 12 data frame, where n is the number of years.
Our main use of ts2df is to convert a single monthly time series into
a year x month data frame for EOF analysis of interannual
variability.
monthCor finds the month-to-month correlations in a monthly time
series x. It is useful for deciding where to start the 12-month
period for an EOF analysis (mon1 in ts2df), namely, at
a time of low serial correlation in x.
Craddock, J. (1965) A meteorological application of principal component analysis. Statistician 15, 143--156.
Alan Jassby, James Cloern
# San Francisco Bay station 27 chlorophyll has the lowest serial # correlation in Oct-Nov, with Sep-Oct a close second chl27 <- sfbayChla[, 's27'] monthCor(chl27)#> Jan-Feb Feb-Mar Mar-Apr Apr-May May-Jun Jun-Jul Jul-Aug Aug-Sep Sep-Oct Oct-Nov #> 0.621 0.439 0.210 0.553 0.575 0.707 0.898 0.768 0.225 0.019 #> Nov-Dec Dec-Jan #> 0.979 0.352# Convert to a data frame with October, the first month of the # local "water year", in the first column tsp(chl27)#> [1] 1978.000 2009.583 12.000#> Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep #> 1978 NA NA NA 1.1 2.8 5.6 2.7 3.4 1.9 1.6 NA 1.7 #> 1979 2.1 2.2 1.7 1.9 1.8 2.4 3.8 2.3 4.8 1.6 3.9 2.1 #> 1980 1.2 1.1 NA 1.3 1.9 2.1 10.2 3.4 2.1 1.1 1.4 1.6 #> 1981 1.4 1.7 1.3 NA 1.7 2.0 9.1 NA NA NA NA NA #> 1982 NA NA NA 2.8 4.5 6.5 9.3 8.2 3.4 1.4 NA 2.1 #> 1983 1.8 1.7 0.9 NA 1.4 7.0 16.4 16.6 5.4 1.4 1.7 2.0 #> 1984 1.5 1.5 1.4 1.9 2.8 3.0 9.8 3.5 1.2 1.7 2.3 2.9 #> 1985 1.4 NA NA NaN NaN 12.3 8.1 1.6 1.4 0.8 NA 2.0 #> 1986 1.5 1.1 NA 1.2 1.2 4.0 25.5 4.0 1.5 1.5 NA NA #> 1987 1.3 1.2 1.1 1.4 1.4 5.1 5.9 5.1 2.9 1.7 2.0 2.0 #> 1988 2.0 0.7 1.3 NA 4.1 5.2 6.8 2.2 3.0 2.8 2.7 1.7 #> 1989 3.2 1.6 1.6 NA 3.6 10.3 11.0 4.1 2.1 2.2 2.0 2.2 #> 1990 2.8 NA 1.2 2.1 NaN 5.4 15.0 3.1 2.0 NA 2.5 NA #> 1991 NA NA 1.9 1.7 1.9 3.7 8.2 6.3 5.0 NaN 1.9 2.2 #> 1992 NA 0.7 1.2 1.6 2.3 4.8 11.6 3.8 3.0 2.5 2.3 2.1 #> 1993 NA 1.7 1.3 1.3 1.8 22.1 9.8 2.9 1.7 NA NA NaN #> 1994 1.2 1.2 NaN 1.3 2.8 12.6 4.5 4.0 5.7 2.6 1.9 2.0 #> 1995 2.1 1.8 NA 1.5 7.6 22.9 12.9 5.4 1.7 1.2 1.2 1.9 #> 1996 6.4 NA NA 1.0 1.2 6.6 11.5 3.8 0.4 1.1 1.1 1.6 #> 1997 2.2 2.3 1.4 4.0 12.6 6.9 11.4 1.9 1.3 2.7 1.7 2.9 #> 1998 2.6 2.6 NA 2.8 1.7 20.1 18.7 6.3 4.1 2.0 1.8 4.0 #> 1999 NA 1.8 NA 2.4 1.9 13.6 11.5 4.8 3.3 NA 3.4 2.3 #> 2000 16.2 2.4 2.2 3.3 3.2 9.3 13.4 3.3 1.7 1.8 2.6 3.0 #> 2001 2.6 39.4 25.6 NA 2.7 20.3 21.7 4.6 6.4 4.0 NA 2.9 #> 2002 3.3 2.7 3.6 NA NA 15.9 8.3 10.1 NA 2.6 4.2 4.2 #> 2003 4.2 4.0 3.7 4.5 30.3 26.6 9.1 4.6 4.2 4.3 5.2 5.5 #> 2004 3.5 2.6 2.1 2.3 5.3 17.1 6.1 4.9 2.9 3.1 4.7 7.8 #> 2005 5.0 4.0 4.8 2.5 3.9 5.0 8.7 6.8 4.7 NA 5.9 5.2 #> 2006 4.2 6.8 3.7 2.7 16.7 9.9 27.8 17.7 NA 2.6 3.5 4.6 #> 2007 4.8 8.9 3.1 3.9 5.2 8.1 17.1 NA 7.0 8.1 17.0 8.0 #> 2008 5.3 4.6 5.6 3.5 3.2 17.3 27.3 13.8 6.6 5.9 6.6 5.9 #> 2009 5.1 3.0 2.9 2.3 2.6 2.6 8.7 5.2 4.6 4.7 4.6 NAts2df(chl27, mon1 = 10, addYr = TRUE, omit = TRUE)#> Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep #> 1979 2.1 2.2 1.7 1.9 1.8 2.4 3.8 2.3 4.8 1.6 3.9 2.1 #> 1984 1.5 1.5 1.4 1.9 2.8 3.0 9.8 3.5 1.2 1.7 2.3 2.9 #> 1987 1.3 1.2 1.1 1.4 1.4 5.1 5.9 5.1 2.9 1.7 2.0 2.0 #> 1997 2.2 2.3 1.4 4.0 12.6 6.9 11.4 1.9 1.3 2.7 1.7 2.9 #> 2000 16.2 2.4 2.2 3.3 3.2 9.3 13.4 3.3 1.7 1.8 2.6 3.0 #> 2003 4.2 4.0 3.7 4.5 30.3 26.6 9.1 4.6 4.2 4.3 5.2 5.5 #> 2004 3.5 2.6 2.1 2.3 5.3 17.1 6.1 4.9 2.9 3.1 4.7 7.8 #> 2008 5.3 4.6 5.6 3.5 3.2 17.3 27.3 13.8 6.6 5.9 6.6 5.9