
seaKen(x, plot = FALSE, type = c("slope", "relative"), order = FALSE, pval = .05, mval = .5, pchs = c(19, 21), ...)
x
is a vector:
seaKen
returns a list with the following members:
x
is a matrix or data frame.
mannKen
for additional details). p-values provided here are not corrected for serial correlation among seasons.If plot = TRUE
, then either the Sen slope in units per year (type = "slope"
) or the relative slope in fraction per year (type = "relative"
) is plotted. The relative slope is defined identically to the Sen slope except that each slope is divided by the first of the two values that describe the slope. Plotting the relative slope is useful when the variables in x
are always positive and have different units.
The plot symbols indicate, respectively, that the trend is statistically significant or not. The plot can be customized by passing any arguments used by dotchart
such as xlab
, as well as graphical parameters described in par
.
If mval
or more of the seasonal slope estimates are missing, then that trend is considered to be missing. The seasonal slope estimate (mannKen
), in turn, is missing if half or more of the possible comparisons between the first and last 20% of the years are missing.
The function can be used in conjunction with mts2ts
to calculate a Regional Kendall test of significance for annualized data, along with a regional estimate of trend (Helsel and Frans 2006). See the examples below.
Hirsch, R.M., Slack, J.R., and Smith, R.A. (1982) Techniques of trend analysis for monthly water quality data. Water Resources Research 18, 107-121.
mannKen
, mts2ts
, trendHomog
# Seasonal Kendall test:
chl <- sfbayChla # monthly chlorophyll at 16 stations in San Francisco Bay
seaKen(sfbayChla[, 's27']) # results for a single series at station 27
seaKen(sfbayChla) # results for all stations
seaKen(sfbayChla, plot=TRUE, type="relative", order=TRUE)
# Regional Kendall test:
# Use mts2ts to change 16 series into a single series with 16 "seasons"
seaKen(mts2ts(chl)) # too many missing data
seaKen(mts2ts(chl, seas = 2:4)) # better when just Feb-Apr, spring bloom period,
# but last 4 stations still missing too much.
seaKen(mts2ts(chl[, 1:12], 2:4)) # more reliable result
Run the code above in your browser using DataLab