paleofire (version 1.2.4)

kdffreq: Fire frequency using kernel density

Description

Computes paleo-fire frequency for a set of fire events (or frequency from other events types, see examples) using a gaussian kernel density estimation procedure based on a defined bandwidth (see Mudelsee 2004 for details). Pseudo-replicated values are used to correct for edge bias, equivalent to "minimum slope" correction in Mann (2004).

Usage

kdffreq(
  fevent,
  up = NULL,
  lo = NULL,
  interval = 10,
  bandwidth = NULL,
  boot = "full",
  bootper = 0.1,
  nbboot = NULL,
  alpha = NULL,
  pseudo = FALSE,
  pseudo_per = NULL
)

Arguments

fevent

Numeric vector, set of dates

up

Numeric, upper age for fire frequency calculus

lo

Numeric, lower age for fire frequency calculus

interval

Numeric, interval between two points for fire frequency calculus (default 10 years)

bandwidth

Numeric, bandwidth in years, or character for automatic bandwidth calculation (e.g. "bw.ucv" for unbiased cross validation) see bandwidth for details

boot

Character, "full" or "partial" see @details

bootper

Numeric, percentage of fire events randomly added or removed in the "partial" replication procedure (default 0.1)

nbboot

Numeric, number of bootstrap replicates

alpha

Numeric, confidence interval (default 0.01)

pseudo

Logical, apply (TRUE) or not (FALSE) Mann (2004) correction (default=FALSE)

pseudo_per

percentage of actual data used in reflection in the Mann (2004) correction

Value

ff data.frame, with fire frequency, bandwidth and CIs

Details

By using boot="partial" option (beta!) fire dates are randomly removed or added within a defined percentage (by default between 1 and 10% of total number of events) in order to make new series that are then used to calculate ensemble members fire frequencies. This procedure differs slightly from the full bootstrapp where fire dates are randomly picked with replacement. Theoretically classic bootstrap could result in a sample where a single fire event date is replicated n times which makes no sense for fires. By randomly removing or adding fire dates the confidence intervals are narrower and likely better reflect the long term fire regime variablility.

References

Mann, M. E. (2004). On smoothing potentially non-stationary climate time series. Geophysical Research Letters, 31(7). Mudelsee, M., B<U+00F6>rngen, M., Tetzlaff, G., & Gr<U+00FC>newald, U. (2004). Extreme floods in central Europe over the past 500 years: Role of cyclone pathway <U+201C>Zugstrasse Vb<U+201D>. Journal of Geophysical Research: Atmospheres (1984<U+2013>2012), 109(D23).

See Also

plot.kdffreq

Examples

# NOT RUN {
 
# }
# NOT RUN {
 set.seed(123)
 fevent=c(round(abs(rnorm(20,mean=7,sd=5))*1000),round(abs(rnorm(10,mean=8,sd=1))*1000))
 ff=kdffreq(fevent,bandwidth = 1000, nbboot=10)

 
 # Estimate the frequency of armed conflicts from 1946 to 2014
 # Data from the The Uppsala Conflict Data Program (UCDP) available at: https://www.prio.org

 dat=read.csv('http://ucdp.uu.se/downloads/ucdpprio/ucdp-prio-acd-4-2016.csv')
 res=kdffreq(dat$Year,bandwidth = "bw.ucv", nbboot=1000, up = 1946, lo = 2014, interval=1, pseudo=T)
 plot(res, ylab="# armed conflict/year")
 
# }