block_average: Calculate Yearly Means for Event Metrics.
In ajsmit/RmarineHeatWaves: Detect Marine Heat Waves and Marine Cold Spells

Description Usage Arguments Details Value Author(s) References Examples

Calculate Yearly Means for Event Metrics.

1	block_average(data, x = t, y = temp, report = "full")

`data`	Accepts the data returned by the `detect` function.
`x`	This column is expected to contain a vector of dates as per the specification of `make_whole`. If a column headed `t` is present in the dataframe, this argument may be ommitted; otherwise, specify the name of the column with dates here.
`y`	This is a column containing the measurement variable. If the column name differs from the default (i.e. `temp`), specify the name here.
`report`	Specify either `full` or `partial`. Selecting `full` causes the report to contain NAs for any years in which no events were detected (except for `count`, which will be zero in those years), while `partial` reports only the years wherein events were detected. The default is `full`.

This function needs to be provided with the full output from the detect function. Note that the yearly averages are calculted only for complete years (i.e. years that start/end part-way through the year at the beginning or end of the original time series are removed from the calculations).

This function differs from the python implementation of the function of the same name (i.e., blockAverage, see https://github.com/ecjoliver/marineHeatWaves) in that we only provide the ability to calculate the average (or aggregate) event metrics in 'blocks' of one year, while the python version allows arbitrary (integer) block sizes.

The function will return a data frame of the averaged (or aggregate) metrics. It includes the following:

`year`	The year over which the metrics were averaged.
`temp_mean`	Seawater temperature for the specified year [deg. C].
`temp_min`	The minimum temperature for the specified year [deg. C].
`temp_max`	The maximum temperature for the specified year [deg. C].
`count`	The number of events per year.
`duration`	The average duration of events per year [days].
`int_mean`	The average event "mean intensity" in each year [deg. C].
`int_max`	The average event "maximum (peak) intensity" in each year [deg. C].
`int_var`	The average event "intensity variability" in each year [deg. C].
`int_cum`	The average event "cumulative intensity" in each year [deg. C x days].
`rate_onset`	Average event onset rate in each year [deg. C / days].
`rate_decline`	Average event decline rate in each year [deg. C / days].
`total_days`	Total number of events days in each year [days].
`total_icum`	Total cumulative intensity over all events in each year [deg. C x days].

int_max_rel_thresh, int_mean_rel_thresh, int_var_rel_thresh, and int_cum_rel_thresh are as above except relative to the threshold (e.g., 90th percentile) rather than the seasonal climatology.

int_max_abs, int_mean_abs, int_var_abs, and int_cum_abs are as above except as absolute magnitudes rather than relative to the seasonal climatology or threshold.

int_max_norm and int_mean_norm are as above except units are in multiples of threshold exceedances, i.e., a value of 1.5 indicates the event intensity (relative to the climatology) was 1.5 times the value of the threshold (relative to climatology, i.e., threshold - climatology.)

Albertus J. Smit, Eric C. J. Oliver

Hobday, A.J. et al. (2016), A hierarchical approach to defining marine heatwaves, Progress in Oceanography, 141, pp. 227-238, doi: 10.1016/j.pocean.2015.12.014

# ts_dat <- make_whole(sst_Med)
# res <- detect(ts_dat, climatology_start = "1983-01-01",
#               climatology_end = "2012-12-31")
# out <- block_average(res)
# summary(glm(count ~ year, out, family = "poisson"))

## Not run: 
plot(out$year, out$count, col = "salmon", pch = 16,
     xlab = "Year", ylab = "Number of events")
lines(out$year, out$count)

## End(Not run)