buildTsObjective {hydromad}R Documentation

Generate objective functions with aggregation, transformation and a reference model


Generate objective functions with temporal aggregation, data transformation and an optional reference model.


buildTsObjective(Q, groups = NULL, FUN = sum, ...,
                  ref = NULL, boxcox = FALSE, start = NULL)



observed data, typically a zoo object.


an optional grouping variable, of the same length as Q, to aggregate the observed, fitted and reference time series. This can be a factor, or a plain vector. It can also be a zoo object with factor-type coredata, in which case it will be matched with corresponding times in Q, etc. Typically groups would be generated by cut.Date (for regular time periods) or eventseq (for events). See examples.

FUN, ...

the aggregation function (and any extra arguments) to use on each group when groups is specified. The actual aggregation is done by eventapply.


output from a reference model correponding to Q. This is passed, after any aggregation and/or transformation, to nseStat. If left as NULL, the mean (of aggregated/transformed data) is used.

boxcox, start

if boxcox = TRUE, each dataset will be transformed with a Box-Cox transformation (see bcPower). The power is estimated from the observed series Q (after any aggregation) by powerTransform. Alternatively the power can be specified as the value of the boxcox argument. Note that boxcox = 0 is a log transform. The offset is specified as the start argument; if NULL it defaults to the 10 percentile (i.e. lowest decile) of the non-zero values of Q.


buildTsObjective returns a function, which can be passed arguments Q, X, ... (the standard signature for hydromad objective functions). The Q argument is ignored since it was already specified directly to buildTsObjective: i.e. the returned function is only valid on the same dataset Q with corresponding fitted values X. Further arguments to the returned function will be passed on to nseStat (therefore the objective function is to be maximised, not minimised). If boxcox = TRUE was specified, the estimated Box-Cox power can be extracted from the returned function f by environment(f)$lambda and similarly for the offset value start.


Felix Andrews

See Also

nseStat, eventseq, cut.Date, hydromad.stats


dat <- window(Cotter, start = "1990-01-01", end = "1993-01-01")

## use Box-Cox transform with parameters estimated from Q
objfun <- buildTsObjective(dat$Q, boxcox = TRUE)
objfun(X = dat$Q + 10)
## extract the estimated Box-Cox parameters
lambda <- environment(objfun)$lambda
start <- environment(objfun)$start

qqmath(~ bcPower(dat$Q + start, lambda))
## in this case the result is the same as:
nseStat(bcPower(dat$Q + start, lambda),
        bcPower(dat$Q + 10 + start, lambda))

## use monthly aggregation and log transform (Box-Cox lambda = 0)
objfun <- buildTsObjective(dat$Q, groups = cut(time(dat), "months"),
                           FUN = sum, boxcox = 0)
objfun(X = dat$Q + 10)

[Package hydromad version 0.9-18 Index]