a4aGrpredict-a4aGr.RdPredicts ages or lengths using a growth class
# S4 method for a4aGr
predict(object, ...)the a4aGr object
arguments to be passed to the rMvdc and copula methods
a matrix object with lengths or ages
# Set up the a4aGr object and parameters for the marginals
mm <- matrix(NA, ncol=3, nrow=3)
diag(mm) <- c(50, 0.001,0.001)
mm[upper.tri(mm)] <- mm[lower.tri(mm)] <- c(0.1,0.01,0.00004)
md <- ~linf*(1-exp(-k*(t-t0)))
imd <- ~t0-1/k*log(1-len/linf)
prs <- FLPar(linf=58.5, k=0.086, t0=0.001, units=c("cm","yr^-1","yr"))
vbObj <- a4aGr(grMod=md, grInvMod=imd, params=prs, vcov=mm, distr="norm")
predict(vbObj, len=1:50+0.5)
#> iter
#> 1
#> 1 0.3030405
#> 2 0.5088496
#> 3 0.7183671
#> 4 0.9317292
#> 5 1.1490795
#> 6 1.3705702
#> 7 1.5963619
#> 8 1.8266250
#> 9 2.0615402
#> 10 2.3012993
#> 11 2.5461064
#> 12 2.7961786
#> 13 3.0517473
#> 14 3.3130595
#> 15 3.5803795
#> 16 3.8539900
#> 17 4.1341940
#> 18 4.4213174
#> 19 4.7157106
#> 20 5.0177511
#> 21 5.3278470
#> 22 5.6464397
#> 23 5.9740081
#> 24 6.3110724
#> 25 6.6581999
#> 26 7.0160099
#> 27 7.3851808
#> 28 7.7664578
#> 29 8.1606619
#> 30 8.5687005
#> 31 8.9915801
#> 32 9.4304211
#> 33 9.8864759
#> 34 10.3611503
#> 35 10.8560295
#> 36 11.3729105
#> 37 11.9138409
#> 38 12.4811684
#> 39 13.0776020
#> 40 13.7062907
#> 41 14.3709234
#> 42 15.0758608
#> 43 15.8263088
#> 44 16.6285514
#> 45 17.4902721
#> 46 18.4210012
#> 47 19.4327614
#> 48 20.5410193
#> 49 21.7661416
#> 50 23.1357118
predict(vbObj, t=1:20+0.5)
#> iter
#> 1
#> 1 7.075601
#> 2 11.313268
#> 3 15.201727
#> 4 18.769754
#> 5 22.043756
#> 6 25.047961
#> 7 27.804602
#> 8 30.334079
#> 9 32.655114
#> 10 34.784882
#> 11 36.739144
#> 12 38.532364
#> 13 40.177813
#> 14 41.687666
#> 15 43.073100
#> 16 44.344365
#> 17 45.510871
#> 18 46.581250
#> 19 47.563424
#> 20 48.464661