phenologyFitter: phenologyFitter

In chillR: Statistical Methods for Phenology Analysis in Temperate Fruit Trees

phenologyFitter

Description

phenologyFitter

Usage

phenologyFitter(
  par.guess = NULL,
  modelfn = PhenoFlex_GDHwrapper,
  bloomJDays,
  SeasonList,
  control = list(smooth = FALSE, verbose = TRUE, maxit = 1000, nb.stop.improvement = 250),
  lower,
  upper,
  seed = 1235433,
  ...
)

Arguments

par.guess	numeric vector. Initial guesses for fit parameters. This can be set to 'NULL', in which case 'GenSA' choses initial parameters.
modelfn	function. Model function which computes the index in 'temperatures' at which blooming occures. It must have as first argument a data frame with at least the two columns 'Temp' and 'JDays' for one season, see 'SeasonList'. It can have further arguments which can be passed via '...'. The 'modelfn' must return a single numeric value for the predicted bloom JDay for that season. 'NA' is an allowed return value if no blooming occures in that season. The default is the PhenoFlex with GDH as heat accumulation. Alternative is PhenoFlex_GAUSSwrapper with GAUSSian heat accumulation. But this function can also be user defined.
bloomJDays	integer vector. vector of observed bloom JDays per year
SeasonList	list. Must be a list of data frames, each data frame for one season. Each data.frame must at least have a column 'Temp' with the temperature vector and 'JDays' with the corresponding JDay vector. Can be generated by e.g. genSeasonList. 'length(SeasonList)' must be equal to 'length(bloomJDays)'.
control	control parameters to 'GenSA', see 'GenSA::GenSA'
lower	Vector with length of ‘par.guess’. Lower bounds for components.
upper	Vector with length of ‘par.guess’. Upper bounds for components.
seed	integer seed for the random number generator used by 'GenSA'.
...	further parameters to be passed on to 'modelfn'.

Value

an object of class 'phenologyFit'. It contains the named elements 'model_fit' with the returned object from GenSA, 'par' the best fit parameters, 'pbloomJDays' the predicted bloom JDays and the inputs 'par.guess', 'modelfn', 'bloomJDays', 'lower', 'upper', 'control', 'SeasonList' and '...'.

Author(s)

Carsten Urbach <[e-mail]>

Examples

## this example does not make sense as a fit, but demonstrates
## how to use `phenologyFitter`
data(KA_weather)
data(KA_bloom)
hourtemps <- stack_hourly_temps(KA_weather, latitude=50.4)
SeasonList <- genSeasonList(hourtemps$hourtemps, years=c(2007,2008))
par <- c(40, 190, 0.5, 25, 3372.8, 9900.3, 6319.5, 5.939917e13, 4, 36, 4, 1.6)
upper <- c(41, 200, 1, 30, 4000, 10000, 7000, 6.e13, 10, 40, 10, 50)
lower <- c(38, 180, 0.1, 0, 3000, 9000, 6000, 5.e13, 0, 0, 0, 0.05)
X <- phenologyFitter(par.guess=par, bloomJDays=KA_bloom$pheno[c(24,25)], 
  SeasonList=SeasonList, lower=lower, upper=upper,
  control=list(smooth=FALSE, verbose=TRUE, maxit=10, nb.stop.improvement=5))
summary(X)
plot(X)

chillR documentation built on Nov. 28, 2023, 1:09 a.m.