step_model: Calculation of cumulative temperature metric according to a...

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

Calculation of cumulative temperature metric according to a user-defined stepwise weight function

Description

This function calculates heat for temperate trees according to a stepwise model provided by the user.

Usage

step_model(
  HourTemp,
  df = data.frame(lower = c(-1000, 1.4, 2.4, 9.1, 12.4, 15.9, 18), upper = c(1.4, 2.4,
    9.1, 12.4, 15.9, 18, 1000), weight = c(0, 0.5, 1, 0.5, 0, -0.5, -1)),
  summ = TRUE
)

Arguments

HourTemp	Vector of hourly temperatures.
df	data.frame with three columns: lower, upper and weight. lower should contain the lower boundary of a chilling weight interval and upper should contain the upper boundary. weight indicates the weighting to be applied to the respective temperature interval.
summ	Boolean parameter indicating whether calculated metrics should be provided as cumulative values over the entire record (TRUE) or as the actual accumulation for each hour (FALSE).

Details

Temperature-based metric calculated according to the user-defined model.

Value

Vector of length length(HourTemp) containing the cumulative temperature metric over the entire duration of HourTemp.

Author(s)

Eike Luedeling

Examples

weather<-fix_weather(KA_weather[which(KA_weather$Year>2006),])

stack<-stack_hourly_temps(weather,latitude=50.4)

df=data.frame(
  lower=c(-1000,1,2,3,4,5,6),
  upper=c(1,2,3,4,5,6,1000),
  weight=c(0,1,2,3,2,1,0))

custom<-function(x) step_model(x,df)

custom(stack$Temp)

models<-list(Chilling_Hours=Chilling_Hours,Utah_Chill_Units=Utah_Model,
Chill_Portions=Dynamic_Model,GDH=GDH,custom=custom)

tempResponse(stack,Start_JDay = 305,End_JDay = 60,models)

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