Dense hessian when only NAA-1 (recruitment) are random effects

library(wham)
path_to_examples <- system.file("extdata", package="wham")
asap3 <- read_asap3_dat(file.path(path_to_examples,"ex1_SNEMAYT.dat"))

# only recruitments are random effects
#   = dense Hessian
input1 <- prepare_wham_input(asap3, recruit_model=2, model_name="Ex 1: SNEMA Yellowtail Flounder",
                                selectivity=list(model=rep("age-specific",3), 
                                    re=rep("none",3), 
                                    initial_pars=list(c(0.5,0.5,0.5,0.5,1,0.5),c(0.5,0.5,0.5,1,0.5,0.5),c(0.5,1,0.5,0.5,0.5,0.5)), 
                                    fix_pars=list(5,4,2)),
                                NAA_re = list(sigma="rec", cor="iid"))
m1 <- fit_wham(input1, do.fit=F)
Matrix::image(m1$env$spHess(random=TRUE), border.col=NA)

m1_dense

# Full state-space model (all NAA are random effects)
#   = sparse Hessian
input3 <- prepare_wham_input(asap3, recruit_model=2, model_name="Ex 1: SNEMA Yellowtail Flounder",
                                selectivity=list(model=rep("age-specific",3), 
                                    re=rep("none",3), 
                                    initial_pars=list(c(0.5,0.5,0.5,0.5,1,0.5),c(0.5,0.5,0.5,1,0.5,0.5),c(0.5,1,0.5,0.5,0.5,0.5)), 
                                    fix_pars=list(5,4,2)),
                                NAA_re = list(sigma="rec+1", cor="iid"))
m3 <- fit_wham(input3, do.fit=F)
Matrix::image(m3$env$spHess(random=TRUE), border.col=NA)

m3_sparse

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timjmiller / wham

Dense hessian when only NAA-1 (recruitment) are random effects #21