Getting error: Error in maxOptim(fn=fn, grad=grad, hess=hess, start=start, method="BFGS",:NA in the initial gradient
Posted: 16 Jan 2023, 16:39
I get the error:
When running my model. I could not find any google answers to solve it. Any help would be greatly appreciated.
Code: Select all
Error in maxOptim(fn = fn, grad = grad, hess = hess, start = start, method = "BFGS", :
NA in the initial gradient
In addition: There were 26 warnings (use warnings() to see them)
Code: Select all
>
> #### nautos ####
> # ################################################################# #
> #### LOAD LIBRARY AND DEFINE CORE SETTINGS ####
> # ################################################################# #
>
> ### Clear memory
> rm(list = ls())
> s12 <- read_csv("discretechoice.csv")
Rows: 1259 Columns: 442
── Column specification ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
Delimiter: ","
chr (55): respid, distchannel, language, opleiding, huishoudensinkomen, auto1_eigendom_anderstext, parkering_anders, fiets_eigendom_anderstext, fiets_type_anderstext, scooter_brandstof_a...
dbl (346): pc4_werk_voor_text, pc4_werk_nu, pc4_woon_voor, pc4_woon_nu, status, progress, duration, finished, toestemming_1, toestemming_2, geslacht, geboortejaar, bezigheid_nu, thuiswerk...
lgl (35): extref, auto2_eigendom_anderstext, auto3_eigendom_anderstext, auto4_eigendom_anderstext, auto5_eigendom, auto5_eigendom_anderstext, auto5_brandstof, auto5_bouwjaar, auto5_bouwj...
dttm (6): startdate, enddate, date, startdate.1, enddate.1, date.1
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
>
>
> ### Load Apollo library
> library(apollo)
>
> ### Initialise code
> apollo_initialise()
Apollo ignition sequence completed
>
> ### Set core controls
> apollo_control = list(
+ modelName = "combinedMOL",
+ modelDescr = "Mixed ordered logit model fitted to MOCOLODO questionnaire",
+ indivID = "uuid2",
+ mixing = TRUE,
+ nCores = 4,
+ outputDirectory = "output"
+ )
>
> # ################################################################# #
> #### LOAD DATA AND APPLY ANY TRANSFORMATIONS ####
> # ################################################################# #
>
> ### Loading data from package
> ### if data is to be loaded from a file (e.g. called data.csv),
> ### the code would be: database = read.csv("data.csv",header=TRUE)
> database = s12
> ### for data dictionary, use ?apollo_drugChoiceData
>
> # ################################################################# #
> #### DEFINE MODEL PARAMETERS ####
> # ################################################################# #
>
> #ref cat strategy: don't use one that is too small and think of what you want to explore. Otherwise choose the middle or extremes.
>
> ### Vector of parameters, including any that are kept fixed in estimation
> apollo_beta = c(
+ sigma_1_error12 = 0,
+ sigma_1_error13 = 0,
+ sigma_1_error14 = 0,
+ sigma_2_error12 = 0,
+ sigma_2_error23 = 0,
+ sigma_2_error24 = 0,
+ sigma_3_error13 = 0,
+ sigma_3_error23 = 0,
+ sigma_3_error34 = 0,
+ sigma_4_error14 = 0,
+ sigma_4_error24 = 0,
+ sigma_4_error34 = 0,
+ b_1_weekdist_dec = 0,
+ b_1_weekdist_inc = 0,
+ b_1_weekdist_same = 0,
+ #b_1_weekdist_nojob = 0,
+ b_1_weekdist_lostjob = 0,
+ b_1_weekdist_gotjob = 0,
+ b_1_weekdist_alwayswfh = 0,
+ b_1_weekdist_wfhtocommuting = 0,
+ b_1_weekdist_commutingtowfh = 0,
+ b_1_weekdist_differingaddresses = 0,
+ b_2_weekdist_dec = 0,
+ b_2_weekdist_inc = 0,
+ b_2_weekdist_same = 0,
+ #b_2_weekdist_nojob = 0,
+ b_2_weekdist_lostjob = 0,
+ b_2_weekdist_gotjob = 0,
+ b_2_weekdist_alwayswfh = 0,
+ b_2_weekdist_wfhtocommuting = 0,
+ b_2_weekdist_commutingtowfh = 0,
+ b_2_weekdist_differingaddresses = 0,
+ b_3_weekdist_dec = 0,
+ b_3_weekdist_inc = 0,
+ b_3_weekdist_same = 0,
+ #b_3_weekdist_nojob = 0,
+ b_3_weekdist_lostjob = 0,
+ b_3_weekdist_gotjob = 0,
+ b_3_weekdist_alwayswfh = 0,
+ b_3_weekdist_wfhtocommuting = 0,
+ b_3_weekdist_commutingtowfh = 0,
+ b_3_weekdist_differingaddresses = 0,
+ b_4_weekdist_dec = 0,
+ b_4_weekdist_inc = 0,
+ b_4_weekdist_same = 0,
+ #b_4_weekdist_nojob = 0,
+ b_4_weekdist_lostjob = 0,
+ b_4_weekdist_gotjob = 0,
+ b_4_weekdist_alwayswfh = 0,
+ b_4_weekdist_wfhtocommuting = 0,
+ b_4_weekdist_commutingtowfh = 0,
+ b_4_weekdist_differingaddresses = 0,
+ b_1_inkomen__1870 = 0,
+ #b_1_inkomen_1870_3800 = 0,
+ b_1_inkomen_3800_ = 0,
+ b_2_inkomen__1870 = 0,
+ #b_2_inkomen_1870_3800 = 0,
+ b_2_inkomen_3800_ = 0,
+ b_3_inkomen__1870 = 0,
+ #b_3_inkomen_1870_3800 = 0,
+ b_3_inkomen_3800_ = 0,
+ b_4_inkomen__1870 = 0,
+ #b_4_inkomen_1870_3800 = 0,
+ b_4_inkomen_3800_ = 0,
+ #b_2_nautos_same = 0,
+ b_2_nautos_inc = 0,
+ b_2_nautos_dec = 0,
+ #b_3_nautos_same = 0,
+ b_3_nautos_inc = 0,
+ b_3_nautos_dec = 0,
+ #b_4_nautos_same = 0,
+ b_4_nautos_inc = 0,
+ b_4_nautos_dec = 0,
+ #b_1_nfiets_same = 0,
+ b_1_nfiets_inc = 0,
+ b_1_nfiets_dec = 0,
+ #b_4_nfiets_same = 0,
+ b_4_nfiets_inc = 0,
+ b_4_nfiets_dec = 0,
+ b_1_ovabonnement_dec = 0,
+ b_1_ovabonnement_inc = 0,
+ #b_1_ovabonnement_same = 0,
+ b_2_ovabonnement_dec = 0,
+ b_2_ovabonnement_inc = 0,
+ #b_2_ovabonnement_same = 0,
+ b_3_ovabonnement_dec = 0,
+ b_3_ovabonnement_inc = 0,
+ #b_3_ovabonnement_same = 0,
+ b_1_type_anders = 0,
+ b_1_type_eengezin = 0,
+ b_1_type_eengezinkinderen = 0,
+ b_1_type_meergezinkinderen = 0,
+ #b_1_type_meergezin = 0
+ b_2_type_anders = 0,
+ b_2_type_eengezin = 0,
+ b_2_type_eengezinkinderen = 0,
+ b_2_type_meergezinkinderen = 0,
+ #b_2_type_meergezin = 0
+ b_3_type_anders = 0,
+ b_3_type_eengezin = 0,
+ b_3_type_eengezinkinderen = 0,
+ b_3_type_meergezinkinderen = 0,
+ #b_3_type_meergezin = 0
+ b_4_type_anders = 0,
+ b_4_type_eengezin = 0,
+ b_4_type_eengezinkinderen = 0,
+ b_4_type_meergezinkinderen = 0,
+ #b_4_type_meergezin = 0
+ tau_nelectrischefietsen_change_1 = 0,
+ tau_nelectrischefietsen_change_2 = 1,
+ tau_nnormalefietsen_change_1 = 0,
+ tau_nnormalefietsen_change_2 = 1,
+ tau_ovabonnement_change_1 = 0,
+ tau_ovabonnement_change_2 = 1,
+ tau_nautos_change_1 = 0,
+ tau_nautos_change_2 = 1)
>
> ### Vector with names (in quotes) of parameters to be kept fixed at their starting value in apollo_beta, use apollo_beta_fixed = c() if none
> apollo_fixed = c()
>
> # ################################################################# #
> #### DEFINE RANDOM COMPONENTS ####
> # ################################################################# #
>
> ### Set parameters for generating draws
> apollo_draws = list(
+ interDrawsType = "sobol",
+ interNDraws = 100,
+ interUnifDraws = c(),
+ interNormDraws = paste0("draws_", c("error12",
+ "error13",
+ "error14",
+ "error23",
+ "error24",
+ "error34"
+ )),
+ intraDrawsType = "",
+ intraNDraws = 0,
+ intraUnifDraws = c(),
+ intraNormDraws = c()
+ )
>
> ### Create random parameters
> apollo_randCoeff = function(apollo_beta, apollo_inputs){
+ randcoeff = list()
+
+ randcoeff[["error12"]] = draws_error12
+ randcoeff[["error13"]] = draws_error13
+ randcoeff[["error14"]] = draws_error14
+ randcoeff[["error23"]] = draws_error23
+ randcoeff[["error24"]] = draws_error24
+ randcoeff[["error34"]] = draws_error34
+
+ return(randcoeff)
+ }
>
> # ################################################################# #
> #### GROUP AND VALIDATE INPUTS ####
> # ################################################################# #
>
> apollo_inputs = apollo_validateInputs()
apollo_draws and apollo_randCoeff were found, so apollo_control$mixing was set to TRUE
All checks on apollo_control completed.
All checks on database completed.
Generating inter-individual draws ...... Done
Inter-person draws are being used without a panel structure.
>
> # ################################################################# #
> #### DEFINE MODEL AND LIKELIHOOD FUNCTION ####
> # ################################################################# #
>
> apollo_probabilities=function(apollo_beta, apollo_inputs, functionality="estimate"){
+
+ ### Attach inputs and detach after function exit
+ apollo_attach(apollo_beta, apollo_inputs)
+ on.exit(apollo_detach(apollo_beta, apollo_inputs))
+
+ ### Create list of probabilities P
+ P = list()
+
+ ### Calculate probabilities using Ordered Logit model and likelihood of indicators
+ ol_settings1 = list(outcomeOrdered = nautos_change,
+ utility =
+ sigma_1_error12*error12 +
+ sigma_1_error13*error13 +
+ sigma_1_error14*error14 +
+ b_1_weekdist_dec*weekdist_woonwerk_changecat_decreasedcommute +
+ b_1_weekdist_inc*weekdist_woonwerk_changecat_increasedcommute +
+ b_1_weekdist_same*weekdist_woonwerk_changecat_samecommute +
+ #b_1_weekdist_nojob*weekdist_woonwerk_changecat_nojob +
+ b_1_weekdist_lostjob*weekdist_woonwerk_changecat_lostjob +
+ b_1_weekdist_gotjob*weekdist_woonwerk_changecat_gotjob +
+ b_1_weekdist_alwayswfh*weekdist_woonwerk_changecat_alwaysworkfromhome +
+ b_1_weekdist_wfhtocommuting*weekdist_woonwerk_changecat_workfromhometocommuting +
+ b_1_weekdist_commutingtowfh*weekdist_woonwerk_changecat_commutingtoworkfromhome +
+ b_1_weekdist_differingaddresses*weekdist_woonwerk_changecat_differingaddresses +
+ b_1_inkomen__1870*huishoudensinkomen__1870 +
+ #b_1_inkomen_1870_3800*huishoudensinkomen_1870_3800 +
+ b_1_inkomen_3800_*huishoudensinkomen_3800_- +
+ #b_1_nfiets_same*nnormalefietsen_change_2 +
+ b_1_nfiets_inc*nnormalefietsen_change_3 +
+ b_1_nfiets_dec*nnormalefietsen_change_1 +
+ b_1_ovabonnement_dec*ovabonnement_type_con_change_1 +
+ b_1_ovabonnement_inc*ovabonnement_type_con_change_3 +
+ #b_1_ovabonnement_same*ovabonnement_type_con_change_2
+ b_1_type_anders*huishoudenstype_anders +
+ b_1_type_eengezin*huishoudenstype_eengezinshuishouden +
+ b_1_type_eengezinkinderen*huishoudenstype_eengezinshuishoudenmetkinderen +
+ b_1_type_meergezinkinderen*huishoudenstype_meergezinshuishoudenmetkinderen,
+ #b_1_type_meergezin*huishoudenstype_meergezinshuishoudenzonderkinderen,
+ tau = list(tau_nautos_change_1, tau_nautos_change_2))
+ #rows = (task==1))
+ ol_settings2 = list(outcomeOrdered = nelectrischefietsen_change,
+ utility =
+ sigma_2_error12*error12 +
+ sigma_2_error23*error23 +
+ sigma_2_error24*error24 +
+ b_2_weekdist_dec*weekdist_woonwerk_changecat_decreasedcommute +
+ b_2_weekdist_inc*weekdist_woonwerk_changecat_increasedcommute +
+ b_2_weekdist_same*weekdist_woonwerk_changecat_samecommute +
+ #b_2_weekdist_nojob*weekdist_woonwerk_changecat_nojob +
+ b_2_weekdist_lostjob*weekdist_woonwerk_changecat_lostjob +
+ b_2_weekdist_gotjob*weekdist_woonwerk_changecat_gotjob +
+ b_2_weekdist_alwayswfh*weekdist_woonwerk_changecat_alwaysworkfromhome +
+ b_2_weekdist_wfhtocommuting*weekdist_woonwerk_changecat_workfromhometocommuting +
+ b_2_weekdist_commutingtowfh*weekdist_woonwerk_changecat_commutingtoworkfromhome +
+ b_2_weekdist_differingaddresses*weekdist_woonwerk_changecat_differingaddresses +
+ b_2_inkomen__1870*huishoudensinkomen__1870 +
+ #b_2_inkomen_1870_3800*huishoudensinkomen_1870_3800 +
+ b_2_inkomen_3800_*huishoudensinkomen_3800_- +
+ #b_2_nautos_same*nautos_change_2 +
+ b_2_nautos_inc*nautos_change_3 +
+ b_2_nautos_dec*nautos_change_1 +
+ b_2_ovabonnement_dec*ovabonnement_type_con_change_1 +
+ b_2_ovabonnement_inc*ovabonnement_type_con_change_3 +
+ #b_2_ovabonnement_same*ovabonnement_type_con_change_2
+ b_2_type_anders*huishoudenstype_anders +
+ b_2_type_eengezin*huishoudenstype_eengezinshuishouden +
+ b_2_type_eengezinkinderen*huishoudenstype_eengezinshuishoudenmetkinderen +
+ b_2_type_meergezinkinderen*huishoudenstype_meergezinshuishoudenmetkinderen,
+ #b_2;_type_meergezin*huishoudenstype_meergezinshuishoudenzonderkinderen,
+ tau = list(tau_nelectrischefietsen_change_1, tau_nelectrischefietsen_change_2))
+ #rows = (task==1))
+ ol_settings3 = list(outcomeOrdered = nnormalefietsen_change,
+ utility =
+ sigma_3_error13*error13 +
+ sigma_3_error23*error23 +
+ sigma_3_error34*error34 +
+ b_3_weekdist_dec*weekdist_woonwerk_changecat_decreasedcommute +
+ b_3_weekdist_inc*weekdist_woonwerk_changecat_increasedcommute +
+ b_3_weekdist_same*weekdist_woonwerk_changecat_samecommute +
+ #b_3_weekdist_nojob*weekdist_woonwerk_changecat_nojob +
+ b_3_weekdist_lostjob*weekdist_woonwerk_changecat_lostjob +
+ b_3_weekdist_gotjob*weekdist_woonwerk_changecat_gotjob +
+ b_3_weekdist_alwayswfh*weekdist_woonwerk_changecat_alwaysworkfromhome +
+ b_3_weekdist_wfhtocommuting*weekdist_woonwerk_changecat_workfromhometocommuting +
+ b_3_weekdist_commutingtowfh*weekdist_woonwerk_changecat_commutingtoworkfromhome +
+ b_3_weekdist_differingaddresses*weekdist_woonwerk_changecat_differingaddresses +
+ b_3_inkomen__1870*huishoudensinkomen__1870 +
+ #b_3_inkomen_1870_3800*huishoudensinkomen_1870_3800 +
+ b_3_inkomen_3800_*huishoudensinkomen_3800_- +
+ #b_3_nautos_same*nautos_change_2 +
+ b_3_nautos_inc*nautos_change_3 +
+ b_3_nautos_dec*nautos_change_1 +
+ b_3_ovabonnement_dec*ovabonnement_type_con_change_1 +
+ b_3_ovabonnement_inc*ovabonnement_type_con_change_3 +
+ #b_3_ovabonnement_same*ovabonnement_type_con_change_2
+ b_3_type_anders*huishoudenstype_anders +
+ b_3_type_eengezin*huishoudenstype_eengezinshuishouden +
+ b_3_type_eengezinkinderen*huishoudenstype_eengezinshuishoudenmetkinderen +
+ b_3_type_meergezinkinderen*huishoudenstype_meergezinshuishoudenmetkinderen,
+ #b_3_type_meergezin*huishoudenstype_meergezinshuishoudenzonderkinderen,
+ tau = list(tau_nnormalefietsen_change_1, tau_nnormalefietsen_change_2))
+ #rows = (task==1))
+ ol_settings4 = list(outcomeOrdered = ovabonnement_type_con_change,
+ utility =
+ sigma_4_error14*error14 +
+ sigma_4_error24*error24 +
+ sigma_4_error34*error34 +
+ b_4_weekdist_dec*weekdist_woonwerk_changecat_decreasedcommute +
+ b_4_weekdist_inc*weekdist_woonwerk_changecat_increasedcommute +
+ b_4_weekdist_same*weekdist_woonwerk_changecat_samecommute +
+ #b_4_weekdist_nojob*weekdist_woonwerk_changecat_nojob +
+ b_4_weekdist_lostjob*weekdist_woonwerk_changecat_lostjob +
+ b_4_weekdist_gotjob*weekdist_woonwerk_changecat_gotjob +
+ b_4_weekdist_alwayswfh*weekdist_woonwerk_changecat_alwaysworkfromhome +
+ b_4_weekdist_wfhtocommuting*weekdist_woonwerk_changecat_workfromhometocommuting +
+ b_4_weekdist_commutingtowfh*weekdist_woonwerk_changecat_commutingtoworkfromhome +
+ b_4_weekdist_differingaddresses*weekdist_woonwerk_changecat_differingaddresses +
+ b_4_inkomen__1870*huishoudensinkomen__1870 +
+ #b_4_inkomen_1870_3800*huishoudensinkomen_1870_3800 +
+ b_4_inkomen_3800_*huishoudensinkomen_3800_- +
+ #b_nautos_same*nautos_change_2 +
+ b_4_nautos_inc*nautos_change_3 +
+ b_4_nautos_dec*nautos_change_1 +
+ #b_4_nfiets_same*nnormalefietsen_change_2 +
+ b_4_nfiets_inc*nnormalefietsen_change_3 +
+ b_4_nfiets_dec*nnormalefietsen_change_1 +
+ b_4_type_anders*huishoudenstype_anders +
+ b_4_type_eengezin*huishoudenstype_eengezinshuishouden +
+ b_4_type_eengezinkinderen*huishoudenstype_eengezinshuishoudenmetkinderen +
+ b_4_type_meergezinkinderen*huishoudenstype_meergezinshuishoudenmetkinderen,
+ #b_4_type_meergezin*huishoudenstype_meergezinshuishoudenzonderkinderen,
+ tau = list(tau_ovabonnement_change_1, tau_ovabonnement_change_2))
+ #rows = (task==1))
+
+ P[["nautos"]] = apollo_ol(ol_settings1, functionality)
+ P[["nelektrischefietsen"]] = apollo_ol(ol_settings2, functionality)
+ P[["nnormalefietsen"]] = apollo_ol(ol_settings3, functionality)
+ P[["ov"]] = apollo_ol(ol_settings4, functionality)
+
+ ### Likelihood of the whole model
+ P = apollo_combineModels(P, apollo_inputs, functionality)
+
+ #P[["model"]] = apollo_ol(ol_settings, functionality)
+
+ ### Take product across observation for same individual
+ #P = apollo_panelProd(P, apollo_inputs, functionality)
+
+ ### Average across inter-individual draws
+ P = apollo_avgInterDraws(P, apollo_inputs, functionality)
+
+ ### Prepare and return outputs of function
+ P = apollo_prepareProb(P, apollo_inputs, functionality)
+ return(P)
+ }
>
> # ################################################################# #
> #### MODEL ESTIMATION ####
> # ################################################################# #
>
> model = apollo_estimate(apollo_beta, apollo_fixed, apollo_probabilities, apollo_inputs, estimate_settings=list(maxIterations=1000))
Preparing user-defined functions.
Testing likelihood function...
Apollo found a model component of type OL without a componentName. The name was set to "nautos" by default.
No coding provided for Ordered model component "nautos", so assuming outcomeOrdered goes from 1 to 3
Overview of choices for OL model component nautos:
1 2 3
Times chosen 44.00 1152.0 63
Percentage chosen overall 3.49 91.5 5
Apollo found a model component of type OL without a componentName. The name was set to "nelektrischefietsen" by default.
No coding provided for Ordered model component "nelektrischefietsen", so assuming outcomeOrdered goes from 1 to 3
Overview of choices for OL model component nelektrischefietsen:
1 2 3
Times chosen 18.00 1128.00 113.00
Percentage chosen overall 1.43 89.59 8.98
Apollo found a model component of type OL without a componentName. The name was set to "nnormalefietsen" by default.
No coding provided for Ordered model component "nnormalefietsen", so assuming outcomeOrdered goes from 1 to 3
Overview of choices for OL model component nnormalefietsen:
1 2 3
Times chosen 101.00 1069.00 89.00
Percentage chosen overall 8.02 84.91 7.07
Apollo found a model component of type OL without a componentName. The name was set to "ov" by default.
No coding provided for Ordered model component "ov", so assuming outcomeOrdered goes from 1 to 3
Overview of choices for OL model component ov:
1 2 3
Times chosen 118.00 990.00 151.00
Percentage chosen overall 9.37 78.63 11.99
Pre-processing likelihood function...
Preparing workers for multithreading...
Testing influence of parameters
Starting main estimation
Initial function value: -7098.091
Initial gradient value:
sigma_1_error12 sigma_1_error13 sigma_1_error14 sigma_2_error12 sigma_2_error23
-1.811416e-01 1.685392e-01 -6.127976e-02 1.643254e-01 1.789297e-01
sigma_2_error24 sigma_3_error13 sigma_3_error23 sigma_3_error34 sigma_4_error14
-4.923709e-02 4.240758e-01 -1.133286e-01 -2.044590e-01 2.568534e-01
sigma_4_error24 sigma_4_error34 b_1_weekdist_dec b_1_weekdist_inc b_1_weekdist_same
4.258143e-02 -2.441465e-01 7.867357e+01 2.633315e+01 4.378372e+01
b_1_weekdist_lostjob b_1_weekdist_gotjob b_1_weekdist_alwayswfh b_1_weekdist_wfhtocommuting b_1_weekdist_commutingtowfh
1.490516e+01 2.772703e+00 1.074234e+01 7.776464e+00 3.503762e+00
b_1_weekdist_differingaddresses b_2_weekdist_dec b_2_weekdist_inc b_2_weekdist_same b_2_weekdist_lostjob
2.668539e+01 9.471521e+01 2.579527e+01 4.951478e+01 2.332939e+01
b_2_weekdist_gotjob b_2_weekdist_alwayswfh b_2_weekdist_wfhtocommuting b_2_weekdist_commutingtowfh b_2_weekdist_differingaddresses
4.003762e+00 1.074234e+01 7.776464e+00 4.734820e+00 2.687856e+01
b_3_weekdist_dec b_3_weekdist_inc b_3_weekdist_same b_3_weekdist_lostjob b_3_weekdist_gotjob
7.940086e+01 2.287104e+01 3.670419e+01 1.444304e+01 2.772703e+00
b_3_weekdist_alwayswfh b_3_weekdist_wfhtocommuting b_3_weekdist_commutingtowfh b_3_weekdist_differingaddresses b_4_weekdist_dec
7.818109e+00 5.352230e+00 3.272703e+00 2.133692e+01 6.431757e+01
b_4_weekdist_inc b_4_weekdist_same b_4_weekdist_lostjob b_4_weekdist_gotjob b_4_weekdist_alwayswfh
1.825363e+01 4.020419e+01 1.963622e+01 4.503762e+00 1.051128e+01
b_4_weekdist_wfhtocommuting b_4_weekdist_commutingtowfh b_4_weekdist_differingaddresses b_1_inkomen__1870 b_1_inkomen_3800_
4.121172e+00 1.041644e+00 1.941268e+01 5.687453e+01 1.004272e+02
b_2_inkomen__1870 b_2_inkomen_3800_ b_3_inkomen__1870 b_3_inkomen_3800_ b_4_inkomen__1870
5.560559e+01 1.212378e+02 4.837077e+01 8.803708e+01 5.032913e+01
b_4_inkomen_3800_ b_2_nautos_inc b_2_nautos_dec b_3_nautos_inc b_3_nautos_dec
9.195755e+01 -1.682563e+01 1.143552e+01 -1.717034e+01 6.087050e+00
b_4_nautos_inc b_4_nautos_dec b_1_nfiets_inc b_1_nfiets_dec b_4_nfiets_inc
-1.201505e+01 1.201128e+01 -2.390892e+01 1.975739e+01 1.686728e+01
b_4_nfiets_dec b_1_ovabonnement_dec b_1_ovabonnement_inc b_2_ovabonnement_dec b_2_ovabonnement_inc
1.856421e+01 2.910962e+01 3.234820e+01 3.207174e+01 3.869667e+01
b_3_ovabonnement_dec b_3_ovabonnement_inc b_1_type_anders b_1_type_eengezin b_1_type_eengezinkinderen
2.649221e+01 3.704138e+01 1.290140e+01 7.901827e+01 1.193928e+01
b_1_type_meergezinkinderen b_2_type_anders b_2_type_eengezin b_2_type_eengezinkinderen b_2_type_meergezinkinderen
6.561687e+01 1.586351e+01 7.924933e+01 1.413246e+01 8.219640e+01
b_3_type_anders b_3_type_eengezin b_3_type_eengezinkinderen b_3_type_meergezinkinderen b_4_type_anders
1.324610e+01 7.082134e+01 1.367034e+01 5.272676e+01 8.321870e+00
b_4_type_eengezin b_4_type_eengezinkinderen b_4_type_meergezinkinderen tau_nelectrischefietsen_change_1 tau_nelectrischefietsen_change_2
7.358652e+01 1.567034e+01 5.838205e+01 -1.211470e+03 8.772260e+02
tau_nnormalefietsen_change_1 tau_nnormalefietsen_change_2 tau_ovabonnement_change_1 tau_ovabonnement_change_2 tau_nautos_change_1
-1.106133e+03 8.445673e+02 -1.012157e+03 7.320191e+02 -1.224437e+03
tau_nautos_change_2
9.342010e+02
initial value 7098.090754
iter 2 value 5482.600283
iter 3 value 4709.835218
iter 4 value 4255.318949
iter 5 value 4205.446902
iter 6 value 4028.873862
iter 7 value 3917.354289
iter 8 value 3634.684082
iter 9 value 3482.859801
iter 10 value 3377.289244
iter 11 value 3258.214747
iter 12 value 3164.962265
iter 13 value 3140.143637
iter 14 value 3109.842944
iter 15 value 3004.101564
iter 16 value 2875.696205
iter 17 value 2825.566318
iter 18 value 2781.285210
iter 19 value 2671.956271
iter 20 value 2608.578898
iter 21 value 2548.203437
iter 22 value 2499.550913
iter 23 value 2457.423312
iter 24 value 2433.604797
iter 25 value 2411.648998
iter 26 value 2376.363035
iter 27 value 2319.630401
iter 28 value 2299.223889
iter 29 value 2266.252087
iter 30 value 2251.850161
iter 31 value 2241.852619
iter 32 value 2230.702844
iter 33 value 2215.966727
iter 34 value 2206.596788
iter 35 value 2194.269519
iter 36 value 2182.511303
iter 37 value 2172.384063
iter 38 value 2163.334154
iter 39 value 2154.240076
iter 40 value 2147.000348
iter 41 value 2140.435103
iter 42 value 2132.441024
iter 43 value 2126.108017
iter 44 value 2123.847859
iter 45 value 2123.228885
iter 46 value 2112.187952
iter 47 value 2103.825164
iter 48 value 2098.068199
iter 49 value 2093.395695
iter 50 value 2089.315906
iter 51 value 2085.044149
iter 52 value 2080.547895
iter 53 value 2075.699059
iter 54 value 2069.396423
iter 55 value 2066.338836
iter 56 value 2063.944941
iter 57 value 2061.977642
iter 58 value 2060.091409
iter 59 value 2058.472868
iter 60 value 2056.891665
iter 61 value 2055.603541
iter 62 value 2054.077911
iter 63 value 2052.559137
iter 64 value 2051.466557
iter 65 value 2049.843638
iter 66 value 2048.653142
iter 67 value 2047.325576
iter 68 value 2046.312828
iter 69 value 2045.605711
iter 70 value 2045.213467
iter 71 value 2044.900161
iter 72 value 2044.600305
iter 73 value 2044.193750
iter 74 value 2043.834388
iter 75 value 2043.726602
iter 76 value 2043.009974
iter 77 value 2042.254759
iter 78 value 2041.605572
iter 79 value 2041.054661
iter 80 value 2040.374461
iter 81 value 2040.226951
iter 82 value 2040.165808
iter 83 value 2039.140513
iter 84 value 2038.102507
iter 85 value 2037.536422
iter 86 value 2037.126506
iter 87 value 2036.785083
iter 88 value 2036.349183
iter 89 value 2035.905107
iter 90 value 2035.158762
iter 91 value 2034.447405
iter 92 value 2033.303835
iter 93 value 2032.516644
iter 94 value 2031.296716
iter 95 value 2030.260719
iter 96 value 2029.150835
iter 97 value 2029.107917
iter 98 value 2027.321550
iter 99 value 2026.543799
iter 100 value 2026.002795
iter 101 value 2024.805453
iter 102 value 2023.607466
iter 103 value 2022.711279
iter 104 value 2021.540411
iter 105 value 2020.072245
iter 106 value 2018.750769
iter 107 value 2017.660054
iter 108 value 2016.504241
iter 109 value 2015.079784
iter 110 value 2013.888760
iter 111 value 2012.716823
iter 112 value 2011.331809
iter 113 value 2010.230974
iter 114 value 2009.119294
iter 115 value 2008.025525
iter 116 value 2007.022186
iter 117 value 2005.995059
iter 118 value 2004.848408
iter 119 value 2003.978374
iter 120 value 2003.157147
iter 121 value 2002.398138
iter 122 value 2001.680081
iter 123 value 2000.992112
iter 124 value 2000.506894
iter 125 value 1999.570769
iter 126 value 1998.794613
iter 127 value 1998.144404
iter 128 value 1997.347325
iter 129 value 1996.531416
iter 130 value 1995.438776
iter 131 value 1994.405394
iter 132 value 1993.259297
iter 133 value 1992.371408
iter 134 value 1990.531462
iter 135 value 1989.327549
iter 136 value 1988.117589
iter 137 value 1987.203577
iter 138 value 1986.150348
iter 139 value 1985.243646
iter 140 value 1984.479799
iter 140 value 1984.479799
final value 1984.479799
converged
Additional convergence test using scaled estimation. Parameters will be scaled by their current estimates and additional iterations will be performed.
Error in maxOptim(fn = fn, grad = grad, hess = hess, start = start, method = "BFGS", :
NA in the initial gradient
In addition: There were 26 warnings (use warnings() to see them)