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mydata.R
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mydata.R
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#
# Translated from matlab code of Kooijman, see http://www.bio.vu.nl/thb/deb/deblab/add_my_pet_old/html/Oncorhynchus_mykiss.html
#
# Not useful when working within a Rproject
# Dir='C:\\Users\\starrlight\\Documents\\GitHub\\Kooijman Rainbow trout'
# setwd(Dir)
## mydata_Oncorhynchus_mykiss: rainbow trout, steelhead
# Bas Kooijman 2014/09/26
## References ----
# * <http://www.bio.vu.nl/thb/deb/deblab/add_my_pet/add_my_pet.pdf *add_my_pet manual*>
# * <http://www.bio.vu.nl/thb/research/bib/LikaKear2011.html *LikaKear2011*>
# Lika, K., Kearney, M. R., Freitas, V., Veer, H. W. van der, Meer, J. van der, Wijsman, J. W. M., Pecquerie, L. and Kooijman, S. A. L. M.
# The `covariation method' for estimating the parameters of the standard Dynamic Energy Budget model {I}: philosophy and approach.
# J. Sea Res. 66: 270--277.
# * <http://www.bio.vu.nl/thb/research/bib/Kooy2010.html *Kooy2010*>
# Kooijman, S.A.L.M. 2010
# Dynamic Energy Budget theory for metabolic organisation. Cambridge Univ. Press
# Table 8.1, page 300
# * *YaniHisa2002*
# T. Yanik, S. A. Hisar and C. Bölükbas (2002)
# EARLY DEVELOPMENT AND GROWTH OF ARCTIC CHARR (SALVELINUS ALPINUS) AND RAINBOW TROUT (ONCORHYNCHUS MYKISS) AT A LOW WATER TEMPERATURE
# The Israeli Journal of Aquaculture - Bamidgeh 54(2), 2002, 73-
# * <http://www.fishbase.org/summary/239 *fishbase*>
# * <en.wikipedia.org/wiki/Oncorhynchus_mykiss *Wiki*>
## Facts
# * wiki: many subspecies, e.g. O. m. irideus (coastal rainbow trout), O. m. gairdneri (Columbia River redband trout)
# * YaniHisa2002: best culturing temp 15-16 C
# * wiki: able to spawn several times, each time separated by months
# Call all the functions ----
names.files=list.files(getwd(), recursive = TRUE, pattern = ".R$")
patterns<-paste("(^|/)", c("mydata", "mer.R",
"mydata 2.R" , "verifs.R", "verifs 2.R", "deSolve example.R"), sep="")
names.source=names.files[-unique(unlist(lapply(patterns, grep, names.files, perl = TRUE)))]
mute <- lapply(names.source, source)
COMPLETE = 2.5 # judge the level using LikaFrei2011 adjust if you have less or more data
FIT = 9.4 # compute after having obtained the estimates
# set data
# zero-variate data ----
# real data
ah = 33 # 1 d, age at hatch at f (YaniHisa2002: 30-36 d)
T_ah = 273 + 8.5 # K, temperature for ab
ab = ah + 20 # 2 d, age at birth at f (YaniHisa2002: ah + 19-21 d)
T_ab = 273 + 8.5 # K, temperature for ab
ap = 2.5*365 # 3 d, age at puberty at f (fishbase)
T_ap = 273 + 5 # K, temperature for ap
am = 11*365 # 4 d, life span at f (fishbase)
T_am = 273 + 5 # K, temperature for am
L0 = 0.45 # cm, diam egg (wiki)
Lb = 2 # 5 cm, physical length at birth at f (from Salmo trutta)
Lp = 15 # 6 cm, physical length at puberty at f (fishbase)
Li = 120 # 7 cm, ultimate total length at f (fishbase)
Wi = 25400 # 8 g, ultimate wet weight at f (fishbase)
Ri = Wi * 2.5/ 365 # 9 #/d, reproduction rate(wiki: 2000 till 3000 eggs per kg
T_Ri = 273 + 5 # K, temperature for Ri
# pseudo-data from pars_my_pet at T_ref don't change these data
v = 0.02 # 10 cm/d, energy conductance
kap = 0.80 # 11 -, allocation fraction to soma = growth + somatic maintenance
kap_R = 0.95 # 12 -, reproduction efficiency
p_M = 18 # 13 J/d.cm^3, [p_M] vol-specific somatic maintenance
p_T = 0 # 14 J/d.cm^2, {p_T} surface-specific som maintenance
k_J = 0.002 # 15 1/d, maturity maintenance rate coefficient
kap_G = .8 # 16 -, growth efficiency
# pack data
data = c(ah, ab, ap, am, Lb, Lp, Li, Wi, Ri, # 01:09 real data
v, kap, kap_R, p_M, p_T, k_J, kap_G) # 10:16 pseudo data
# weight coefficients for WLS and ML criteria differ
Data = data.frame(data, data, pmin(100,1 / pmax(rep(1e-6, length(data)), data) ^ 2)) # nmregr, nrregr (WLS criterion)
Data[1:9,3] = 10 * Data[1:9,3] # Data(1:10,3) = 10 * Data(1:10,3) # give real data more weight
Data[16,3] = 200 * Data[16,3] # more weight to kap_G
# insert temperature data for rates and times in the first column
Data[c(1 ,2 ,3 ,4 ,9), 1] = c(T_ah , T_ab , T_ap , T_am ,T_Ri)
txt_data = c(# for presentation of predictions
'1 ah, d, age at hatch ' ,
'2 ab, d, age at birth ' ,
'3 ap, d, age at puberty ' ,
'4 am, d, life span ' ,
'5 Lb, cm, physical length at birth ' ,
'6 Lp, cm, physical length at puberty ' ,
'7 Li, cm, ultimate physical length ',
'8 Wi, g, ultimate wet weight ' ,
'9 Ri, #/d, maximum reprod rate ' ,
'10 v, cm/d, energy conductance ' ,
'11 kap, -, allocation fraction to soma ' ,
'12 kap_R, -, reproduction efficiency ' ,
'13 [p_M], J/d.cm^3, vol-spec som maint ',
'14 {p_T}, J/d.cm^2, sur-spec som maint ' ,
'15 k_J, 1/d, maturity maint rate coefficient ' ,
'16 kap_G, -, growth efficiency')
# uni-variate data ----
# t-Ww data from YaniHisa2002 at T = 273 + 8.5
# initial weight 1.54 g
tW = matrix(c( # time since birth (d), wet weight (g)
0.202 , 1.471,
15.609, 2.434,
31.397, 3.448,
46.426, 4.716,
62.217, 6.136,
77.253, 8.215,
92.859, 10.294,
108.271, 11.917,
123.688 ,14.148,
139.104, 16.227,
154.725, 20.335),
ncol=2, byrow=T)
tW = cbind(tW, tW[,2])
tW[,3] = 5e-1/mean(tW[,2])^2 # append weight coefficients for WLS criterion
# conversion coefficients ----
# (selected copy-paste from pars_my_pet)
# chemical indices
# X V E P
n_O = matrix(c(1.00, 1.00, 1.00, 1.00, # C/C, equals 1 by definition
1.80, 1.80, 1.80, 1.80, # H/C these values show that we consider dry-mass
0.50, 0.50, 0.50, 0.50, # O/C
0.15, 0.15, 0.15, 0.15), ncol=4, byrow=T) # N/C
# C H O N
n_M = matrix(c( 1 , 0 , 0 , 0 , # C/C, equals 0 or 1
0 , 2 , 0 , 3 , # H/C
2 , 1 , 2 , 0 , # O/C
0 , 0 , 0 , 1), ncol=4, byrow=T) # N/C
# specific densities
# X V E P
d_O = c(0.2, 0.2, 0.2, 0.2) # g/cm^3, specific densities for organics
# chemical potentials
# X V E P
mu_O = c(525, 500 , 550 , 480) * 1000 # J/mol, chemical potentials for organics
# molecular weights
w_O = t(n_O) %*% c(12, 1 , 16 , 14) # g/mol, mol-weights for organics
# pack coefficients
dwm = cbind(d_O, w_O, mu_O) # g/cm^3, g/mol, kJ/mol spec density, mol weight, chem pot
# parameters: initial values at T_ref ----
T_ref = 293 # 1 K, temp for which rate pars are given don't change this value
T_A = 8000 # 2 K, Arrhenius temp
f = 1 # 3 -, scaled functional response
z = 9.882 # 4 -, zoom factor for z = 1: L_m = 1 cm
del_M = 0.1461 # 5 -, shape coefficient
F_m = 6.5 # 6 l/d.cm^2, {F_m} max spec searching rate
kap_X = 0.8 # 7 -, digestion efficiency of food to reserve
v = 0.0486 # 8 cm/d, energy conductance
kap = 0.31 # 9 -, allocation fraction to soma = growth + somatic maintenance
kap_R = 0.95 #10 -, reproduction efficiency
p_M = 18.28 #11 J/d.cm^3, [p_M] vol-specific somatic maintenance
p_T = 0 #12 J/d.cm^2, {p_T} surface-specific som maintenance
k_J = 0.002 #13 1/d, maturity maint rate coefficient
E_G = 5265 #14 J/cm^3, [E_G], spec cost for structure
E_Hh = 3.351e1 #15 J, E_H^h maturity at hatch
E_Hb = 1.510e2 #15 J, E_H^b maturity at birth
E_Hj = 1.107e3 #16 J, E_H^j maturity at metamorphosis
E_Hp = 1.554e5 #17 J, E_H^p maturity at puberty
h_a = 1.115e-6 #18 1/d^2, Weibull aging acceleration
s_G = 1e-4 #19 -, Gompertz stress coefficient
f_tL = 1.97 #20 -, scaled functional response for tL-data
W_0 = 1.5 #21 g, wet weight at zero
# pack parameters and fix T_ref and f and possibly other as well
# in second column: 0 = fix 1 = release
pars = matrix(c(T_ref, 0, T_A, 0 , f, 0, z , 1 , del_M ,1 ,F_m , 0 ,
kap_X ,0 , v , 1 , kap ,1 ,kap_R ,0 , p_M , 1 , p_T, 0 ,
k_J , 0 , E_G ,0 , E_Hh ,1 , E_Hb ,1 , E_Hj, 1, E_Hp, 1 , h_a , 1 ,s_G , 0 , f_tL ,1 ,W_0, 0), ncol=2, byrow=T)
txt_pars = c( # for presentation of parameter estimates
'T_ref, K' , 'T_A, K' , 'f, -' ,
'z, -' , 'del_M, -' , '{F_m}, l/d.cm^2' ,
'kap_X, -' , 'v, cm/d' , 'kap, -' ,
'kap_R, -' ,'[p_M], J/d.cm^3', '{p_T}, J/d.cm^2' ,
'k_J, 1/d' ,'[E_G], J/cm^3' , 'E_Hh, J' , 'E_Hb, J' , 'E_Hj, J' ,
'E_Hp, J' , 'h_a, 1/d^2' , 's_G, -', 'f_tL, -', 'W_0, g')
# estimate parameters ----
#
# nmregr_options('default') # set options for parameter estimation #!!!!!!!!!!!!!!!!!!!!!!!!!!! nmregr_options ???
# nmregr_options('max_step_number',1000) # set options for parameter estimation
# nmregr_options('max_fun_evals',2e4) # set options for parameter estimation
#
pars = nmregr('predict_Oncorhynchus_mykiss', pars, list(Data,tW)) # WLS estimate parameters using overwrite
sd = 0 * pars[,1] # initiate standard deviations
[cov cor sd] = pregr('predict_Oncorhynchus_mykiss', pars, Data, tW) # get standard deviation for WLS
# get FIT ----
# Data[,3] = 0
# Data[1:9,3] = 1 # give unit weight to real data, zero to pseudo-data
# merr = mre('predict_Oncorhynchus_mykiss', pars, Data, tW) # WLS-method
# FIT = 10 * (1 - merr) # get mark for goodness of fit
# get predictions ----
tspan = data.frame(0:380) # times for plotting length data
predict=predict_Oncorhynchus_mykiss(pars[,1], Data, tspan) # notice use of first column of pars only
# PLOT ----
plot(tW[,2]~tW[,1], col="red", xlim=c(0,max(tspan[,1])), ylim=c(0, max(predict[[2]])))
points(predict[[2]]~tspan[,1], type="l", col="green")
## Results ----
#
# fprintf('FIT = #3.1f \n', FIT)
# fprintf('COMPLETE = #3.1f \n\n', COMPLETE)
#
# printpar(txt_data, data, Edata, 'data and expectations') # for zero-variate data
# fprintf('\n') # insert a blank line in screen output
# printpar(txt_pars, pars, sd)
#
# close all # remove existing figures, else you get more and more if you retry
#
# figure # one figure to show results of uni-variate data
# plot(tW(:,1), tW(:,2), '.r', t, EW, 'g', 'linewidth', 2, 'markersize', 20)
# set(gca, 'Fontsize', 15, 'Box', 'on')
# xlabel('time, d')
# ylabel('wet weight, g')
#
# # options.showCode = false publish('mydata_Oncorhynchus_mykiss', options)