skills(object, target = NULL, model = "ols", calibration = "50%", timespan = NULL)dcc or
seascorr can be used as object for the
function. The required data is then taken from this object and no
further processing of the tree and climate variables has to be
done for by the user. This reflects the flow of analysis, where
first general climate/growth relations are explored, and then the
strongest ones are deployed for reconstruction purposes.target is an aggregation modifier (one of
.mean, .sum, and
.range). The user should be aware of the fact that
in case the aggregation modifier evaluates to more than one
variable (e.g., summer means for both temperature and
precipiation), a warning message is issued, and only the first
variable is taken into consideration for evaluating the
reconstruction skills. If not specified, the selection from the
original call to dcc is used.
The type of regression model (ordinary least squares or errors-in-variables via reduced major axis regression) can be selected.
The part of the data to be used as a calibration subset can be specified in three different ways: 1) as a range of years, these are then taken as calibration period; 2) as a single integer, if positive, this number of observations at the recent end of the data set is taken as calibration set, if negative, this number of oldest observations is taken; and 3) as a character string giving a percentage of values, e.g., "-40%" would select the 40% oldest observations, while "55%" would select the 55% most recent ones.
The relationship between climate and tree-ring data is evaluated for the calibration period and the complete data set. Frequently used verification statistics are computed: reduction of error (RE), coefficient of efficiency (CE), and the Durban-Watson statistic (DW) (Cook et al. 1994, Durbin and Watson, 1951).
Durbin, J, Watson, GS (1951) Testing for serial correlation in least squares regression. Biometrika 38:159-78.
## Not run:
# dc <- dcc(muc_fake, muc_clim, .mean(6:9, "temp") + .sum(6:9,
# "prec"))
# sk <- skills(dc)
# sk
# plot(sk)
# ## End(Not run)
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