This function creates a list of parameters, sets up TMB object and attempts to do fitting / estimation
fit(
data_list,
silent = FALSE,
inits = NULL,
control = list(eval.max = 2000, iter.max = 1000, rel.tol = 1e-10),
limits = NULL,
fit_model = TRUE
)A list of data, as output from create_data
Boolean passed to TMB::MakeADFun, whether to be verbose or not (defaults to FALSE)
Optional named list of parameters for starting values, defaults to NULL
Optional control list for stats::nlminb. For arguments see ?nlminb. Defaults to eval.max=2000, iter.max=1000, rel.tol=1e-10. For final model runs, the rel.tol should be even smaller
Whether to include limits for stats::nlminb. Can be a list of (lower, upper), or TRUE to use suggested hardcoded limits. Defaults to NULL, where no limits used.
Whether to fit the model. If not, returns a list including the data, parameters, and initial values. Defaults to TRUE
data(fishdist)
# example of fitting fixed effects, no trends, no random effects
set.seed(1)
datalist <- create_data(fishdist[which(fishdist$year > 1970), ],
asymmetric_model = FALSE,
est_mu_re = FALSE, est_sigma_re = FALSE
)
fit <- fit(datalist)
#> Order of parameters:
#> [1] "theta" "b_mu" "log_sigma_mu_devs"
#> [4] "mu_devs" "b_sig1" "b_sig2"
#> [7] "log_sigma1_sd" "sigma1_devs" "log_sigma2_sd"
#> [10] "sigma2_devs" "log_obs_sigma" "log_tdf_1"
#> [13] "log_tdf_2" "log_beta_1" "log_beta_2"
#> Not matching template order:
#> [1] "log_sigma1_sd" "sigma1_devs" "log_sigma2_sd"
#> [4] "sigma2_devs" "theta" "mu_devs"
#> [7] "log_sigma_mu_devs" "log_tdf_1" "log_tdf_2"
#> [10] "log_beta_1" "log_beta_2" "log_obs_sigma"
#> [13] "b_mu" "b_sig1" "b_sig2"
#> Your parameter list has been re-ordered.
#> (Disable this warning with checkParameterOrder=FALSE)
#> Constructing atomic D_lgamma
#> Constructing atomic qnorm1
#> outer mgc: 25034.62
#> outer mgc: 2574.348
#> outer mgc: 1978.553
#> outer mgc: 200.0837
#> outer mgc: 201.8165
#> outer mgc: 165.3092
#> outer mgc: 135.522
#> outer mgc: 356.5337
#> outer mgc: 120.1827
#> outer mgc: 138.867
#> outer mgc: 123.0076
#> outer mgc: 72.46919
#> outer mgc: 34.31654
#> outer mgc: 42.47041
#> outer mgc: 77.59186
#> outer mgc: 15.58226
#> outer mgc: 55.94099
#> outer mgc: 18.16199
#> outer mgc: 5.722491
#> outer mgc: 30.59853
#> outer mgc: 12.27546
#> outer mgc: 23.85256
#> outer mgc: 4.909127
#> outer mgc: 16.05365
#> outer mgc: 2.657193
#> outer mgc: 5.044657
#> outer mgc: 1.330298
#> outer mgc: 5.259609
#> outer mgc: 2.40497
#> outer mgc: 1.914672
#> outer mgc: 0.3580338
#> outer mgc: 1.486636
#> outer mgc: 0.4165588
#> outer mgc: 0.243783
#> outer mgc: 0.3935108
#> outer mgc: 0.2062873
#> outer mgc: 0.3560087
#> outer mgc: 0.2509
#> outer mgc: 0.2320703
#> outer mgc: 0.2115966
#> outer mgc: 0.1605053
#> outer mgc: 0.5950958
#> outer mgc: 0.1653233
#> outer mgc: 0.2129569
#> outer mgc: 0.4200267
#> outer mgc: 0.153131
#> outer mgc: 0.4769271
#> outer mgc: 0.4690585
#> outer mgc: 0.2463581
#> outer mgc: 2.354513
#> outer mgc: 0.5144498
#> outer mgc: 0.3407818
#> outer mgc: 0.7156203
#> outer mgc: 0.2290494
#> outer mgc: 0.3342214
#> outer mgc: 0.1680087
#> outer mgc: 0.05198574
#> outer mgc: 0.07477491
#> outer mgc: 0.01717437
#> outer mgc: 0.02687706
#> outer mgc: 0.007900119
#> outer mgc: 0.008503209
#> outer mgc: 0.002959148
#> outer mgc: 0.002543861
#> outer mgc: 0.0009483259
#> outer mgc: 0.0007261544
#> outer mgc: 0.000288091
#> outer mgc: 0.0002083385
#> outer mgc: 8.78474e-05
#> outer mgc: 6.004225e-05
#> outer mgc: 2.612294e-05
#> outer mgc: 1.697649e-05
#> outer mgc: 7.594411e-06
#> outer mgc: 4.810276e-06
#> outer mgc: 4.810276e-06
#> outer mgc: 0.05355298
#> outer mgc: 0.05355582
#> outer mgc: 0.05127458
#> outer mgc: 0.05127639
#> outer mgc: 0.05241413
#> outer mgc: 0.05241576
#> outer mgc: 0.05583219
#> outer mgc: 0.05583443
#> outer mgc: 0.05241437
#> outer mgc: 0.05241552
#> outer mgc: 0.05697153
#> outer mgc: 0.056974
#> outer mgc: 0.05583182
#> outer mgc: 0.0558348
#> outer mgc: 0.06266844
#> outer mgc: 0.06267164
#> outer mgc: 0.05469277
#> outer mgc: 0.05469494
#> outer mgc: 0.05697135
#> outer mgc: 0.05697418
#> outer mgc: 4.814768e-06
#> outer mgc: 4.80579e-06
#> outer mgc: 0.9690304
#> outer mgc: 0.9709709
#> outer mgc: 0.01573754
#> outer mgc: 0.01574038
#> outer mgc: 0.04861318
#> outer mgc: 0.0486681
#> outer mgc: 6321.927
#
# # example of model with random effects in means only, and symmetric distribution
# set.seed(1)
# datalist <- create_data(fishdist[which(fishdist$year > 1970),], asymmetric_model = FALSE,
# est_sigma_re = FALSE)
# fit <- fit(datalist)
# # example of model with random effects in variances
# set.seed(1)
# datalist <- create_data(fishdist[which(fishdist$year > 1970),], asymmetric_model = TRUE,
# est_mu_re = TRUE)
# fit <- fit(datalist)
#
# # example of model with poisson response
# set.seed(1)
# datalist <- create_data(fishdist[which(fishdist$year > 1970),], asymmetric_model = FALSE,
# est_sigma_trend=FALSE, est_mu_trend=FALSE, est_mu_re = TRUE,
# family="poisson")
# fit <- fit(datalist)