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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)
# 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)
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