PredictVariablesInterest.3PG: Predicte variables of interest

In drGeorgeXenakis/fr3PGD: The Physiological Principles for Predicting Growth Model (3PG)

Usage

PredictVariablesInterest.3PG(state, parms, cod.pred)

Arguments

state
parms
cod.pred

Examples

##---- Should be DIRECTLY executable !! ----
##-- ==>  Define data, use random,
##--	or do  help(data=index)  for the standard data sets.

## The function is currently defined as
function (state, parms, cod.pred) 
{
    SLA0 <- parms[["SLA0"]]
    SLA1 <- parms[["SLA1"]]
    tSLA <- parms[["tSLA"]]
    fracBB0 <- parms[["fracBB0"]]
    fracBB1 <- parms[["fracBB1"]]
    tBB <- parms[["tBB"]]
    t <- state[["t"]]
    N <- state[["N"]]
    Wl <- state[["Wl"]]
    Wr <- state[["Wr"]]
    Wsbr <- state[["Wsbr"]]
    SLA <- SLA1 + (SLA0 - SLA1) * exp(-log(2) * (t/tSLA)^2)
    LAI <- Wl * SLA * 0.1
    fracBB <- fracBB1 + (fracBB0 - fracBB1) * exp(-log(2) * (t/tBB))
    Ww <- Wsbr * (1 - fracBB)
    Wb <- (Wsbr - Ww) * 0.5675
    Wbr <- (Wsbr - Ww) - Wb
    Wa <- Wl + Wsbr
    W <- Wl + Wr + Wsbr
    hdom <- EstimateH.3PG(N = N, Wa = Wa)
    wsbrg <- Wsbr * 1000/N
    if (cod.pred == "3PG") {
        rhoMax <- parms[["rhoMax"]]
        rhoMin <- parms[["rhoMin"]]
        tRho <- parms[["tRho"]]
        WoodDensity <- rhoMax + (rhoMin - rhoMax) * exp(-log(2) * 
            t/tRho)
        Vu <- Wsbr * (1 - fracBB)/WoodDensity
        dg <- (wsbrg/parms[["aS"]])^(1/parms[["nS"]])
        G <- (dg/200)^2 * pi * N
    }
    else if (cod.pred == "Allometric") {
        G <- EstimateG.3PG(N = N, Wa = Wa)
        Vub <- EstimateV.3PG(N = N, Ww = Ww)
        dg <- ObtainDg(N = N, G = G)
    }
    state[c("LAI", "Ww", "Wb", "Wbr", "Wa", "W", "hdom", "wsbrg", 
        "G", "Vu", "dg")] <- c(LAI, Ww, Wb, Wbr, Wa, W, hdom, 
        wsbrg, G, Vu, dg)
    return(state)
  }

drGeorgeXenakis/fr3PGD documentation built on June 3, 2020, 6:10 a.m.