I need to estimate a MXL/MMNL, which I want to preface by estimating the respective MNL, both to compare likelihood and to solve errors from the bottom up, which worked out up until now. Before going to the MXL, I am now at a stage where I am unsure how to continue, as in practice the MNL model can be estimated, but multiple things are weird:
1) I tried to iteratively construct utility functions, but this leads to a warning:
Code:
Code: Select all
database <- fread("database_sample.csv")
apollo_control = list(
modelName = "MNL_full",
indivID = "case", # I cannot distinguish individuals, i.e. there is only one case per individual
panelData = FALSE,
mixing = FALSE,
nCores = 4
)
apollo_control <- apollo_validateControl(database = database, apollo_control = apollo_control)
database <- apollo_validateData(database, apollo_control, silent = FALSE)
modes = c("car", "bicycle", "ebicycle", "escooter", "emoped")
default = "bicycle"
# mapping of column names and coefficient names
var_map_alternative_specific = c(
"SOC" = "start_SOC",
# "P" = "expected_price_per_min",
# "V" = "is_closeby",
"D" = "length",
"A" = "ascent",
"B" = "mean_bikeability"
)
var_map_generic = c(
"D2C" = "start_d2c",
"O" = "outbound",
"W" = "weekend",
# "H" = "is_holiday", # constant in sample
"T_0" = "timebucket0004",
"T_4" = "timebucket0408",
"T_8" = "timebucket0812",
"T_12" = "timebucket1216",
"T_16" = "timebucket1620",
"T_20" = "timebucket2024",
"C" = "current_temp",
"WS" = "current_wind_speed",
"R" = "current_precipitation_volume",
"POI_start_alpha" = "transport_start",
"POI_start_beta" = "shopping_start",
"POI_start_gamma" = "food_start",
"POI_start_delta" = "leisure_and_entertainment_start",
"POI_start_epsilon" = "education_start",
"POI_start_zeta" = "services_start",
"POI_start_eta" = "outdoor_start",
"POI_end_alpha" = "transport_end",
"POI_end_beta" = "shopping_end",
"POI_end_gamma" = "food_end",
"POI_end_delta" = "leisure_and_entertainment_end",
"POI_end_epsilon" = "education_end",
"POI_end_zeta" = "services_end",
"POI_end_eta" = "outdoor_end"
)
var_map = c(var_map_alternative_specific, var_map_generic)
alternative_specific_coef = c(
c("ASC_car", "ASC_bicycle", "ASC_ebicycle", "ASC_emoped", "ASC_escooter"),
do.call(
paste,
c(
expand.grid(
"b",
c(
"SOC", "D", #"V",
"A", "B", "D2C", "O", "W",
"T_0", "T_4", "T_8",
"T_12", "T_16", "T_20", "C", "WS", "R",
"POI_start_alpha", "POI_start_beta","POI_start_gamma", "POI_start_delta",
"POI_start_epsilon", "POI_start_zeta", "POI_start_eta",
"POI_end_alpha", "POI_end_beta","POI_end_gamma",
"POI_end_delta", "POI_end_epsilon", "POI_end_zeta", "POI_end_eta"
),
modes
),
sep = "_")
)
)
# remove bicycle SOC and generic coefficients for the default alternative (instead of fixing them)
alternative_specific_coef <- alternative_specific_coef[! alternative_specific_coef %in% c(
"b_SOC_bicycle", do.call(paste, c(expand.grid("b", names(var_map_generic), default), sep="_")))]
# generic_coef = c("b_P") # don't use price for now
generic_coef = c()
apollo_beta = c(
setNames(rep(0, length(alternative_specific_coef)),
alternative_specific_coef),
setNames(rep(0, length(generic_coef)), generic_coef)
)
# fix default ASC and one level of categorical variable
apollo_fixed = c(
"ASC_bicycle"
, "b_T_0_car", "b_T_0_ebicycle", "b_T_0_emoped", "b_T_0_escooter"
)
apollo_inputs <- apollo_validateInputs()
# retain various variables for use in apollo function scope
apollo_inputs$modes <- modes
apollo_inputs$modes_encoded <- modes_encoded
apollo_inputs$var_map_alternative_specific <- var_map_alternative_specific
apollo_inputs$var_map_generic <- var_map_generic
apollo_inputs$var_map <- var_map
apollo_inputs$alternative_specific_coef <- alternative_specific_coef
apollo_inputs$generic_coef <- generic_coef
apollo_probabilities = function(apollo_beta, apollo_inputs, functionality="estimate"){
### Function initialisation: do not change the following three commands
### Attach inputs and detach after function exit
apollo_attach(apollo_beta, apollo_inputs)
on.exit(apollo_detach(apollo_beta, apollo_inputs))
### Create list of probabilities P
Prob = list()
### List of utilities: these must use the same names as in mnl_settings, order is irrelevant
# use below for debugging generated utility functions
# V_str = list()
# for (m in apollo_inputs$modes){
# V_str[[m]] = paste("ASC", m, sep="_")
# for (v in apollo_inputs$alternative_specific_coef){
# if (!grepl(paste0("_", m), v, fixed=TRUE)) next # skip other than m coefficients
# if (grepl("ASC", v, fixed=TRUE)) next # skip ASC
# if (v == "SOC" & m == "bicycle") next # don't include SOC in bicycle utility function
# if (v %in% names(apollo_inputs$var_map_generic) & m == "bicycle") next # default
# v_raw = sub(paste0("_",m), "", sub("b_", "", v))
# as_coef = v_raw %in% names(apollo_inputs$var_map_alternative_specific)
# V_str[[m]] = paste(V_str[[m]], "+", v, "*", ifelse(as_coef, paste(apollo_inputs$var_map[v_raw], m, sep="_"), apollo_inputs$var_map[v_raw]))
# }
#
# for (v in apollo_inputs$generic_coef){
# if (v %in% names(apollo_inputs$var_map_generic) & m == "bicycle") next # default
# v_raw = sub("b_", "", v)
# as_coef = v_raw %in% names(apollo_inputs$var_map_alternative_specific)
# V_str[[m]] = paste(V_str[[m]], "+", v, "*", ifelse(as_coef, paste(apollo_inputs$var_map[v_raw], m, sep="_"), apollo_inputs$var_map[v_raw]))
# }
# }
# print(V_str)
V = list()
for (m in apollo_inputs$modes){
V[[m]] = get(paste("ASC", m, sep="_"))
for (v in apollo_inputs$alternative_specific_coef){
if (!grepl(paste0("_", m), v, fixed=TRUE)) next # skip other than mode coefficients
if (grepl("ASC", v, fixed=TRUE)) next # skip ASC, already there
if (v == "SOC" & m == "bicycle") next # don't include SOC in bicycle utility function
if (v %in% names(apollo_inputs$var_map_generic) & m == "bicycle") next # default
v_raw = sub(paste0("_",m), "", sub("b_", "", v))
as_coef = v_raw %in% names(apollo_inputs$var_map_alternative_specific)
V[[m]] = V[[m]] + get(v) * get(ifelse(as_coef, paste(apollo_inputs$var_map[v_raw], m, sep="_"), apollo_inputs$var_map[v_raw]))
}
for (v in apollo_inputs$generic_coef){
if (v %in% apollo_inputs$var_map_generic & m == "bicycle") next # default
v_raw = sub("b_", "", v)
as_coef = v_raw %in% names(apollo_inputs$var_map_alternative_specific)
V[[m]] = V[[m]]+ get(v) * get(ifelse(as_coef, paste(apollo_inputs$var_map[v_raw], m, sep="_"), apollo_inputs$var_map[v_raw]))
}
}
### Define settings for MNL model component
mnl_settings = list(
alternatives = apollo_inputs$modes_encoded,
avail = list(
car = car_available,
bicycle = bicycle_available,
ebicycle = ebicycle_available,
escooter = escooter_available,
emoped = emoped_available
),
choiceVar = vehicle_type,
utilities = V
)
### Compute probabilities using MNL model
Prob[["model"]] = apollo_mnl(mnl_settings, functionality)
### Prepare and return outputs of function
Prob = apollo_prepareProb(Prob, apollo_inputs, functionality)
return(Prob)
}
# ################################################################# #
#### MODEL ESTIMATION ####
# ################################################################# #
model = apollo_estimate(apollo_beta, apollo_fixed, apollo_probabilities, apollo_inputs, list(estimationRoutine="BFGS"))
Code: Select all
Preparing user-defined functions.
$car
[1] "ASC_car + b_SOC_car * start_SOC_car + b_D_car * length_car + b_A_car * ascent_car + b_B_car * mean_bikeability_car + b_D2C_car * start_d2c + b_O_car * outbound + b_W_car * weekend + b_T_0_car * timebucket0004 + b_T_4_car * timebucket0408 + b_T_8_car * timebucket0812 + b_T_12_car * timebucket1216 + b_T_16_car * timebucket1620 + b_T_20_car * timebucket2024 + b_C_car * current_temp + b_WS_car * current_wind_speed + b_R_car * current_precipitation_volume + b_POI_start_alpha_car * transport_start + b_POI_start_beta_car * shopping_start + b_POI_start_gamma_car * food_start + b_POI_start_delta_car * leisure_and_entertainment_start + b_POI_start_epsilon_car * education_start + b_POI_start_zeta_car * services_start + b_POI_start_eta_car * outdoor_start + b_POI_end_alpha_car * transport_end + b_POI_end_beta_car * shopping_end + b_POI_end_gamma_car * food_end + b_POI_end_delta_car * leisure_and_entertainment_end + b_POI_end_epsilon_car * education_end + b_POI_end_zeta_car * services_end + b_POI_end_eta_car * outdoor_end"
$bicycle
[1] "ASC_bicycle + b_D_bicycle * length_bicycle + b_A_bicycle * ascent_bicycle + b_B_bicycle * mean_bikeability_bicycle"
$ebicycle
[1] "ASC_ebicycle + b_SOC_ebicycle * start_SOC_ebicycle + b_D_ebicycle * length_ebicycle + b_A_ebicycle * ascent_ebicycle + b_B_ebicycle * mean_bikeability_ebicycle + b_D2C_ebicycle * start_d2c + b_O_ebicycle * outbound + b_W_ebicycle * weekend + b_T_0_ebicycle * timebucket0004 + b_T_4_ebicycle * timebucket0408 + b_T_8_ebicycle * timebucket0812 + b_T_12_ebicycle * timebucket1216 + b_T_16_ebicycle * timebucket1620 + b_T_20_ebicycle * timebucket2024 + b_C_ebicycle * current_temp + b_WS_ebicycle * current_wind_speed + b_R_ebicycle * current_precipitation_volume + b_POI_start_alpha_ebicycle * transport_start + b_POI_start_beta_ebicycle * shopping_start + b_POI_start_gamma_ebicycle * food_start + b_POI_start_delta_ebicycle * leisure_and_entertainment_start + b_POI_start_epsilon_ebicycle * education_start + b_POI_start_zeta_ebicycle * services_start + b_POI_start_eta_ebicycle * outdoor_start + b_POI_end_alpha_ebicycle * transport_end + b_POI_end_beta_ebicycle * shopping_end + b_POI_end_gamma_ebicycle * food_end + b_POI_end_delta_ebicycle * leisure_and_entertainment_end + b_POI_end_epsilon_ebicycle * education_end + b_POI_end_zeta_ebicycle * services_end + b_POI_end_eta_ebicycle * outdoor_end"
$escooter
[1] "ASC_escooter + b_SOC_escooter * start_SOC_escooter + b_D_escooter * length_escooter + b_A_escooter * ascent_escooter + b_B_escooter * mean_bikeability_escooter + b_D2C_escooter * start_d2c + b_O_escooter * outbound + b_W_escooter * weekend + b_T_0_escooter * timebucket0004 + b_T_4_escooter * timebucket0408 + b_T_8_escooter * timebucket0812 + b_T_12_escooter * timebucket1216 + b_T_16_escooter * timebucket1620 + b_T_20_escooter * timebucket2024 + b_C_escooter * current_temp + b_WS_escooter * current_wind_speed + b_R_escooter * current_precipitation_volume + b_POI_start_alpha_escooter * transport_start + b_POI_start_beta_escooter * shopping_start + b_POI_start_gamma_escooter * food_start + b_POI_start_delta_escooter * leisure_and_entertainment_start + b_POI_start_epsilon_escooter * education_start + b_POI_start_zeta_escooter * services_start + b_POI_start_eta_escooter * outdoor_start + b_POI_end_alpha_escooter * transport_end + b_POI_end_beta_escooter * shopping_end + b_POI_end_gamma_escooter * food_end + b_POI_end_delta_escooter * leisure_and_entertainment_end + b_POI_end_epsilon_escooter * education_end + b_POI_end_zeta_escooter * services_end + b_POI_end_eta_escooter * outdoor_end"
$emoped
[1] "ASC_emoped + b_SOC_emoped * start_SOC_emoped + b_D_emoped * length_emoped + b_A_emoped * ascent_emoped + b_B_emoped * mean_bikeability_emoped + b_D2C_emoped * start_d2c + b_O_emoped * outbound + b_W_emoped * weekend + b_T_0_emoped * timebucket0004 + b_T_4_emoped * timebucket0408 + b_T_8_emoped * timebucket0812 + b_T_12_emoped * timebucket1216 + b_T_16_emoped * timebucket1620 + b_T_20_emoped * timebucket2024 + b_C_emoped * current_temp + b_WS_emoped * current_wind_speed + b_R_emoped * current_precipitation_volume + b_POI_start_alpha_emoped * transport_start + b_POI_start_beta_emoped * shopping_start + b_POI_start_gamma_emoped * food_start + b_POI_start_delta_emoped * leisure_and_entertainment_start + b_POI_start_epsilon_emoped * education_start + b_POI_start_zeta_emoped * services_start + b_POI_start_eta_emoped * outdoor_start + b_POI_end_alpha_emoped * transport_end + b_POI_end_beta_emoped * shopping_end + b_POI_end_gamma_emoped * food_end + b_POI_end_delta_emoped * leisure_and_entertainment_end + b_POI_end_epsilon_emoped * education_end + b_POI_end_zeta_emoped * services_end + b_POI_end_eta_emoped * outdoor_end"
...
$car
[1] "ASC_car + b_POI_end_eta_car * outdoor_end + b_POI_end_eta_car * NA + b_POI_end_eta_car * NA"
$bicycle
[1] "ASC_bicycle + b_B_bicycle * mean_bikeability_bicycle + b_B_bicycle * NA + b_B_bicycle * NA"
$ebicycle
[1] "ASC_ebicycle + b_POI_end_eta_ebicycle * outdoor_end + b_POI_end_eta_ebicycle * NA + b_POI_end_eta_ebicycle * NA"
$escooter
[1] "ASC_escooter + b_POI_end_eta_escooter * outdoor_end + b_POI_end_eta_escooter * NA + b_POI_end_eta_escooter * NA"
$emoped
[1] "ASC_emoped + b_POI_end_eta_emoped * outdoor_end + b_POI_end_eta_emoped * NA + b_POI_end_eta_emoped * NA"
WARNING: The pre-processing of 'apollo_probabilities' failed after
inserting functions. Your model may still run, but this
indicates a potential problem. Please contact the developers
for assistance!
This is where I canceled the process, and went ahead to hardcode utility functions using the outputs generated above:
This replaces the utility function part in apollo_probabilities:
Code: Select all
V = list()
V[["car"]] = ASC_car +
b_SOC_car * start_SOC_car +
b_D_car * length_car +
b_A_car * ascent_car +
b_B_car * mean_bikeability_car +
b_D2C_car * start_d2c +
b_O_car * outbound +
b_W_car * weekend +
b_T_0_car * timebucket0004 +
b_T_4_car * timebucket0408 +
b_T_8_car * timebucket0812 +
b_T_12_car * timebucket1216 +
b_T_16_car * timebucket1620 +
b_T_20_car * timebucket2024 +
b_C_car * current_temp +
b_WS_car * current_wind_speed +
b_R_car * current_precipitation_volume +
b_POI_start_alpha_car * transport_start +
b_POI_start_beta_car * shopping_start +
b_POI_start_gamma_car * food_start +
b_POI_start_delta_car * leisure_and_entertainment_start +
b_POI_start_epsilon_car * education_start +
b_POI_start_zeta_car * services_start +
b_POI_start_eta_car * outdoor_start +
b_POI_end_alpha_car * transport_end +
b_POI_end_beta_car * shopping_end +
b_POI_end_gamma_car * food_end +
b_POI_end_delta_car * leisure_and_entertainment_end +
b_POI_end_epsilon_car * education_end +
b_POI_end_zeta_car * services_end +
b_POI_end_eta_car * outdoor_end
V[["bicycle"]] = ASC_bicycle +
b_D_bicycle * length_bicycle +
b_A_bicycle * ascent_bicycle +
b_B_bicycle * mean_bikeability_bicycle
V[["ebicycle"]] = ASC_ebicycle +
b_SOC_ebicycle * start_SOC_ebicycle +
b_D_ebicycle * length_ebicycle +
b_A_ebicycle * ascent_ebicycle +
b_B_ebicycle * mean_bikeability_ebicycle +
b_D2C_ebicycle * start_d2c +
b_O_ebicycle * outbound +
b_W_ebicycle * weekend +
b_T_0_ebicycle * timebucket0004 +
b_T_4_ebicycle * timebucket0408 +
b_T_8_ebicycle * timebucket0812 +
b_T_12_ebicycle * timebucket1216 +
b_T_16_ebicycle * timebucket1620 +
b_T_20_ebicycle * timebucket2024 +
b_C_ebicycle * current_temp +
b_WS_ebicycle * current_wind_speed +
b_R_ebicycle * current_precipitation_volume +
b_POI_start_alpha_ebicycle * transport_start +
b_POI_start_beta_ebicycle * shopping_start +
b_POI_start_gamma_ebicycle * food_start +
b_POI_start_delta_ebicycle * leisure_and_entertainment_start +
b_POI_start_epsilon_ebicycle * education_start +
b_POI_start_zeta_ebicycle * services_start +
b_POI_start_eta_ebicycle * outdoor_start +
b_POI_end_alpha_ebicycle * transport_end +
b_POI_end_beta_ebicycle * shopping_end +
b_POI_end_gamma_ebicycle * food_end +
b_POI_end_delta_ebicycle * leisure_and_entertainment_end +
b_POI_end_epsilon_ebicycle * education_end +
b_POI_end_zeta_ebicycle * services_end +
b_POI_end_eta_ebicycle * outdoor_end
V[["escooter"]] = ASC_escooter +
b_SOC_escooter * start_SOC_escooter +
b_D_escooter * length_escooter +
b_A_escooter * ascent_escooter +
b_B_escooter * mean_bikeability_escooter +
b_D2C_escooter * start_d2c +
b_O_escooter * outbound +
b_W_escooter * weekend +
b_T_0_escooter * timebucket0004 +
b_T_4_escooter * timebucket0408 +
b_T_8_escooter * timebucket0812 +
b_T_12_escooter * timebucket1216 +
b_T_16_escooter * timebucket1620 +
b_T_20_escooter * timebucket2024 +
b_C_escooter * current_temp +
b_WS_escooter * current_wind_speed +
b_R_escooter * current_precipitation_volume +
b_POI_start_alpha_escooter * transport_start +
b_POI_start_beta_escooter * shopping_start +
b_POI_start_gamma_escooter * food_start +
b_POI_start_delta_escooter * leisure_and_entertainment_start +
b_POI_start_epsilon_escooter * education_start +
b_POI_start_zeta_escooter * services_start +
b_POI_start_eta_escooter * outdoor_start +
b_POI_end_alpha_escooter * transport_end +
b_POI_end_beta_escooter * shopping_end +
b_POI_end_gamma_escooter * food_end +
b_POI_end_delta_escooter * leisure_and_entertainment_end +
b_POI_end_epsilon_escooter * education_end +
b_POI_end_zeta_escooter * services_end +
b_POI_end_eta_escooter * outdoor_end
V[["emoped"]] = ASC_emoped +
b_SOC_emoped * start_SOC_emoped +
b_D_emoped * length_emoped +
b_A_emoped * ascent_emoped +
b_B_emoped * mean_bikeability_emoped +
b_D2C_emoped * start_d2c +
b_O_emoped * outbound +
b_W_emoped * weekend +
b_T_0_emoped * timebucket0004 +
b_T_4_emoped * timebucket0408 +
b_T_8_emoped * timebucket0812 +
b_T_12_emoped * timebucket1216 +
b_T_16_emoped * timebucket1620 +
b_T_20_emoped * timebucket2024 +
b_C_emoped * current_temp +
b_WS_emoped * current_wind_speed +
b_R_emoped * current_precipitation_volume +
b_POI_start_alpha_emoped * transport_start +
b_POI_start_beta_emoped * shopping_start +
b_POI_start_gamma_emoped * food_start +
b_POI_start_delta_emoped * leisure_and_entertainment_start +
b_POI_start_epsilon_emoped * education_start +
b_POI_start_zeta_emoped * services_start +
b_POI_start_eta_emoped * outdoor_start +
b_POI_end_alpha_emoped * transport_end +
b_POI_end_beta_emoped * shopping_end +
b_POI_end_gamma_emoped * food_end +
b_POI_end_delta_emoped * leisure_and_entertainment_end +
b_POI_end_epsilon_emoped * education_end +
b_POI_end_zeta_emoped * services_end +
b_POI_end_eta_emoped * outdoor_end
Code: Select all
Preparing user-defined functions.
Testing likelihood function...
Overview of choices for MNL model component :
bicycle car ebicycle emoped
Times available 10000.0 10000.00 1.0e+04 1e+04
Times chosen 3460.0 6347.00 2.1e+01 5e+00
Percentage chosen overall 34.6 63.47 2.1e-01 5e-02
Percentage chosen when available 34.6 63.47 2.1e-01 5e-02
escooter
Times available 10000.00
Times chosen 167.00
Percentage chosen overall 1.67
Percentage chosen when available 1.67
Pre-processing likelihood function...
Creating cluster...
Preparing workers for multithreading...
Analytical gradient is different to numerical one. Numerical
gradients will be used.
Analytical gradients could not be calculated for all components,
numerical gradients will be used.
Testing influence of parameters...........................................................................................................................
Starting main estimation
BGW is using FD derivatives for model Jacobian. (Caller did not provide derivatives.)
Iterates will be written to:
/home/jmuires/source/modechoice/MNL_full_iterations.csv it nf F RELDF PRELDF RELDX MODEL stppar
0 1 1.609437912e+04
1 4 1.089770578e+04 3.229e-01 3.044e-01 1.00e+00 G 7.78e-01
2 5 7.669113839e+03 2.963e-01 3.799e-01 4.39e-01 G 4.93e+00
3 6 6.786696142e+03 1.151e-01 1.343e-01 5.69e-01 G 2.84e+00
4 7 5.961581698e+03 1.216e-01 1.457e-01 5.42e-01 G 5.83e-01
5 8 5.642541748e+03 5.352e-02 5.860e-02 2.49e-01 G 2.23e-01
6 10 5.400676085e+03 4.286e-02 4.950e-02 4.22e-01 G 2.99e-02
7 12 5.359884706e+03 7.553e-03 6.111e-03 3.14e-02 G 2.51e-01
8 14 5.325231630e+03 6.465e-03 7.497e-03 1.41e-01 G 4.64e-02
9 16 5.318041150e+03 1.350e-03 1.798e-03 8.60e-03 G 1.24e+00
10 18 5.313333668e+03 8.852e-04 8.546e-04 1.37e-02 S 1.26e-01
...
117 146 5.276564207e+03 6.204e-12 6.689e-11 5.12e-06 S 6.49e-02
118 147 5.276564207e+03 3.040e-11 4.788e-11 1.90e-06 S 1.73e-01
119 148 5.276564207e+03 1.430e-11 1.693e-11 4.32e-06 S 7.04e-02
***** Relative function convergence *****
Additional convergence test using scaled estimation. Parameters
will be scaled by their current estimates and additional
iterations will be performed.
BGW is using FD derivatives for model Jacobian. (Caller did not provide derivatives.)
Iterates will be appended to:
/home/jmuires/source/modechoice/MNL_full_iterations.csv it nf F RELDF PRELDF RELDX MODEL stppar
0 1 5.276564207e+03
1 7 5.276564207e+03 9.808e-13 2.158e-12 1.21e-05 G 3.95e+00
2 15 5.276564207e+03 9.790e-14 5.785e-13 3.39e-06 G-S 1.18e+01
3 20 5.276564207e+03 8.101e-15 1.669e-16 1.51e-09 G 1.76e+04
4 26 5.276564207e+03 2.068e-15 3.276e-21 3.00e-14 G 8.89e+08
***** False convergence *****
The estimation of the scaled model failed, and the unscaled
version will be returned instead.
Estimated parameters:
Estimate
ASC_car 2.239579
ASC_bicycle 0.000000
ASC_ebicycle -2.782581
ASC_emoped -2.110107
ASC_escooter 6.039018
b_SOC_car -0.017630
b_D_car -4.384e-05
b_A_car 0.004233
b_B_car -1.206232
b_D2C_car 1.4587e-04
b_O_car 0.147830
b_W_car -0.129533
b_T_0_car 0.000000
b_T_4_car 0.678080
b_T_8_car 0.918854
b_T_12_car 0.824404
b_T_16_car 0.902038
b_T_20_car 0.555138
b_C_car -0.044104
b_WS_car 0.039057
b_R_car 0.080331
b_POI_start_alpha_car -0.543449
b_POI_start_beta_car 1.362755
b_POI_start_gamma_car -1.147940
b_POI_start_delta_car -0.052960
b_POI_start_epsilon_car -1.819648
b_POI_start_zeta_car 0.571766
b_POI_start_eta_car -1.187990
b_POI_end_alpha_car -0.569859
b_POI_end_beta_car 1.066813
b_POI_end_gamma_car -1.262325
b_POI_end_delta_car -0.504386
b_POI_end_epsilon_car -1.617586
b_POI_end_zeta_car 0.980248
b_POI_end_eta_car -0.898540
b_D_bicycle -3.9293e-04
b_A_bicycle 0.004089
b_B_bicycle -0.502259
b_SOC_ebicycle -0.018573
b_D_ebicycle -2.7892e-04
b_A_ebicycle 0.003795
b_B_ebicycle 0.793691
b_D2C_ebicycle 5.2105e-04
b_O_ebicycle 0.511662
b_W_ebicycle -0.905533
b_T_0_ebicycle 0.000000
b_T_4_ebicycle -2.019232
b_T_8_ebicycle -1.654777
b_T_12_ebicycle -1.839972
b_T_16_ebicycle -2.070412
b_T_20_ebicycle -0.845246
b_C_ebicycle -0.028911
b_WS_ebicycle 0.197240
b_R_ebicycle -1.577646
b_POI_start_alpha_ebicycle -1.264917
b_POI_start_beta_ebicycle -3.426741
b_POI_start_gamma_ebicycle -1.354868
b_POI_start_delta_ebicycle 3.031877
b_POI_start_epsilon_ebicycle -3.675478
b_POI_start_zeta_ebicycle -2.896654
b_POI_start_eta_ebicycle 4.386000
b_POI_end_alpha_ebicycle -1.105115
b_POI_end_beta_ebicycle -0.943038
b_POI_end_gamma_ebicycle -0.608778
b_POI_end_delta_ebicycle 0.681714
b_POI_end_epsilon_ebicycle -1.759777
b_POI_end_zeta_ebicycle -1.216601
b_POI_end_eta_ebicycle 2.233369
b_SOC_escooter -0.063476
b_D_escooter -5.3448e-04
b_A_escooter -0.012614
b_B_escooter 0.505693
b_D2C_escooter 1.9038e-04
b_O_escooter -0.034644
b_W_escooter -0.507325
b_T_0_escooter 0.000000
b_T_4_escooter -1.546724
b_T_8_escooter -1.430709
b_T_12_escooter -2.008773
b_T_16_escooter -2.823806
b_T_20_escooter -1.479479
b_C_escooter -8.498e-05
b_WS_escooter -0.018488
b_R_escooter -138.492939
b_POI_start_alpha_escooter -1.146293
b_POI_start_beta_escooter -1.257266
b_POI_start_gamma_escooter -0.142349
b_POI_start_delta_escooter 1.699514
b_POI_start_epsilon_escooter -1.952530
b_POI_start_zeta_escooter -1.570653
b_POI_start_eta_escooter 2.739583
b_POI_end_alpha_escooter 0.499049
b_POI_end_beta_escooter 0.006354
b_POI_end_gamma_escooter -0.982700
b_POI_end_delta_escooter -0.578088
b_POI_end_epsilon_escooter -0.907645
b_POI_end_zeta_escooter 0.007862
b_POI_end_eta_escooter -1.154322
b_SOC_emoped -0.013492
b_D_emoped -8.282e-05
b_A_emoped 0.007010
b_B_emoped -0.063563
b_D2C_emoped -1.5095e-04
b_O_emoped -0.485589
b_W_emoped -15.118535
b_T_0_emoped 0.000000
b_T_4_emoped -16.916359
b_T_8_emoped -2.978931
b_T_12_emoped -2.943153
b_T_16_emoped -3.318909
b_T_20_emoped -17.055849
b_C_emoped 0.109977
b_WS_emoped 0.330498
b_R_emoped 0.009859
b_POI_start_alpha_emoped 3.939417
b_POI_start_beta_emoped -1.097240
b_POI_start_gamma_emoped -3.233876
b_POI_start_delta_emoped -9.084779
b_POI_start_epsilon_emoped -2.095627
b_POI_start_zeta_emoped -1.036817
b_POI_start_eta_emoped 3.084146
b_POI_end_alpha_emoped -5.515382
b_POI_end_beta_emoped 5.072686
b_POI_end_gamma_emoped -8.525597
b_POI_end_delta_emoped 0.754469
b_POI_end_epsilon_emoped 0.404757
b_POI_end_zeta_emoped 0.987306
b_POI_end_eta_emoped -1.418269
Final LL: -5276.5642
Calculating log-likelihood at equal shares (LL(0)) for applicable
models...
Calculating log-likelihood at observed shares from estimation
data (LL(c)) for applicable models...
Calculating LL of each model component...
Calculating other model fit measures
INFORMATION: Your model took more than 10 minutes to estimate, so it was saved
to file /home/jmuires/source/modechoice/MNL_full_model.rds
before calculating its covariance matrix. If calculation of the
covariance matrix fails or is stopped before finishing, you can
load the model up to this point using apollo_loadModel. You may
also want to inspect the approximate BHHH standard errors shown
above to determine whether you wish to continue this process.
Computing covariance matrix using numerical methods (numDeriv).
0%....25%....50%....75%....100%
WARNING: Singular Hessian, cannot calculate s.e.
Could not write hessian to a file.
Negative definite Hessian with maximum eigenvalue: 0
Computing score matrix...
Your model was estimated using the BGW algorithm. Please
acknowledge this by citing Bunch et al. (1993) - DOI
10.1145/151271.151279
> apollo_modelOutput(model)
Model run by jmuires using Apollo 0.3.0 on R 4.3.1 for Linux.
Please acknowledge the use of Apollo by citing Hess & Palma (2019)
DOI 10.1016/j.jocm.2019.100170
www.ApolloChoiceModelling.com
Model name : MNL_full
Model description : No model description provided in apollo_control
Model run at : 2023-08-30 17:41:51.490785
Estimation method : bgw
Model diagnosis : Relative function convergence
Optimisation diagnosis : Maximum found
hessian properties : Negative definite
maximum eigenvalue : 0
reciprocal of condition number : 1.93177e-20
Number of individuals : 10000
Number of rows in database : 10000
Number of modelled outcomes : 10000
Number of cores used : 4
Model without mixing
LL(start) : -16094.38
LL at equal shares, LL(0) : -16094.38
LL at observed shares, LL(C) : -7408.43
LL(final) : -5276.56
Rho-squared vs equal shares : 0.6721
Adj.Rho-squared vs equal shares : 0.6645
Rho-squared vs observed shares : 0.2878
Adj.Rho-squared vs observed shares : 0.2717
AIC : 10799.13
BIC : 11686
Estimated parameters : 123
Time taken (hh:mm:ss) : 02:20:10.36
pre-estimation : 00:02:11.69
estimation : 00:30:38.79
post-estimation : 01:47:19.88
Iterations : 119
Unconstrained optimisation.
Estimates:
Estimate s.e. t.rat.(0)
ASC_car 2.239579 NA NA
ASC_bicycle 0.000000 NA NA
ASC_ebicycle -2.782581 NA NA
ASC_emoped -2.110107 NA NA
ASC_escooter 6.039018 NA NA
b_SOC_car -0.017630 NA NA
b_D_car -4.384e-05 NA NA
b_A_car 0.004233 NA NA
b_B_car -1.206232 NA NA
b_D2C_car 1.4587e-04 NA NA
b_O_car 0.147830 NA NA
b_W_car -0.129533 NA NA
b_T_0_car 0.000000 NA NA
b_T_4_car 0.678080 NA NA
b_T_8_car 0.918854 NA NA
b_T_12_car 0.824404 NA NA
b_T_16_car 0.902038 NA NA
b_T_20_car 0.555138 NA NA
b_C_car -0.044104 NA NA
b_WS_car 0.039057 NA NA
b_R_car 0.080331 NA NA
b_POI_start_alpha_car -0.543449 NA NA
b_POI_start_beta_car 1.362755 NA NA
b_POI_start_gamma_car -1.147940 NA NA
b_POI_start_delta_car -0.052960 NA NA
b_POI_start_epsilon_car -1.819648 NA NA
b_POI_start_zeta_car 0.571766 NA NA
b_POI_start_eta_car -1.187990 NA NA
b_POI_end_alpha_car -0.569859 NA NA
b_POI_end_beta_car 1.066813 NA NA
b_POI_end_gamma_car -1.262325 NA NA
b_POI_end_delta_car -0.504386 NA NA
b_POI_end_epsilon_car -1.617586 NA NA
b_POI_end_zeta_car 0.980248 NA NA
b_POI_end_eta_car -0.898540 NA NA
b_D_bicycle -3.9293e-04 NA NA
b_A_bicycle 0.004089 NA NA
b_B_bicycle -0.502259 NA NA
b_SOC_ebicycle -0.018573 NA NA
b_D_ebicycle -2.7892e-04 NA NA
b_A_ebicycle 0.003795 NA NA
b_B_ebicycle 0.793691 NA NA
b_D2C_ebicycle 5.2105e-04 NA NA
b_O_ebicycle 0.511662 NA NA
b_W_ebicycle -0.905533 NA NA
b_T_0_ebicycle 0.000000 NA NA
b_T_4_ebicycle -2.019232 NA NA
b_T_8_ebicycle -1.654777 NA NA
b_T_12_ebicycle -1.839972 NA NA
b_T_16_ebicycle -2.070412 NA NA
b_T_20_ebicycle -0.845246 NA NA
b_C_ebicycle -0.028911 NA NA
b_WS_ebicycle 0.197240 NA NA
b_R_ebicycle -1.577646 NA NA
b_POI_start_alpha_ebicycle -1.264917 NA NA
b_POI_start_beta_ebicycle -3.426741 NA NA
b_POI_start_gamma_ebicycle -1.354868 NA NA
b_POI_start_delta_ebicycle 3.031877 NA NA
b_POI_start_epsilon_ebicycle -3.675478 NA NA
b_POI_start_zeta_ebicycle -2.896654 NA NA
b_POI_start_eta_ebicycle 4.386000 NA NA
b_POI_end_alpha_ebicycle -1.105115 NA NA
b_POI_end_beta_ebicycle -0.943038 NA NA
b_POI_end_gamma_ebicycle -0.608778 NA NA
b_POI_end_delta_ebicycle 0.681714 NA NA
b_POI_end_epsilon_ebicycle -1.759777 NA NA
b_POI_end_zeta_ebicycle -1.216601 NA NA
b_POI_end_eta_ebicycle 2.233369 NA NA
b_SOC_escooter -0.063476 NA NA
b_D_escooter -5.3448e-04 NA NA
b_A_escooter -0.012614 NA NA
b_B_escooter 0.505693 NA NA
b_D2C_escooter 1.9038e-04 NA NA
b_O_escooter -0.034644 NA NA
b_W_escooter -0.507325 NA NA
b_T_0_escooter 0.000000 NA NA
b_T_4_escooter -1.546724 NA NA
b_T_8_escooter -1.430709 NA NA
b_T_12_escooter -2.008773 NA NA
b_T_16_escooter -2.823806 NA NA
b_T_20_escooter -1.479479 NA NA
b_C_escooter -8.498e-05 NA NA
b_WS_escooter -0.018488 NA NA
b_R_escooter -138.492939 NA NA
b_POI_start_alpha_escooter -1.146293 NA NA
b_POI_start_beta_escooter -1.257266 NA NA
b_POI_start_gamma_escooter -0.142349 NA NA
b_POI_start_delta_escooter 1.699514 NA NA
b_POI_start_epsilon_escooter -1.952530 NA NA
b_POI_start_zeta_escooter -1.570653 NA NA
b_POI_start_eta_escooter 2.739583 NA NA
b_POI_end_alpha_escooter 0.499049 NA NA
b_POI_end_beta_escooter 0.006354 NA NA
b_POI_end_gamma_escooter -0.982700 NA NA
b_POI_end_delta_escooter -0.578088 NA NA
b_POI_end_epsilon_escooter -0.907645 NA NA
b_POI_end_zeta_escooter 0.007862 NA NA
b_POI_end_eta_escooter -1.154322 NA NA
b_SOC_emoped -0.013492 NA NA
b_D_emoped -8.282e-05 NA NA
b_A_emoped 0.007010 NA NA
b_B_emoped -0.063563 NA NA
b_D2C_emoped -1.5095e-04 NA NA
b_O_emoped -0.485589 NA NA
b_W_emoped -15.118535 NA NA
b_T_0_emoped 0.000000 NA NA
b_T_4_emoped -16.916359 NA NA
b_T_8_emoped -2.978931 NA NA
b_T_12_emoped -2.943153 NA NA
b_T_16_emoped -3.318909 NA NA
b_T_20_emoped -17.055849 NA NA
b_C_emoped 0.109977 NA NA
b_WS_emoped 0.330498 NA NA
b_R_emoped 0.009859 NA NA
b_POI_start_alpha_emoped 3.939417 NA NA
b_POI_start_beta_emoped -1.097240 NA NA
b_POI_start_gamma_emoped -3.233876 NA NA
b_POI_start_delta_emoped -9.084779 NA NA
b_POI_start_epsilon_emoped -2.095627 NA NA
b_POI_start_zeta_emoped -1.036817 NA NA
b_POI_start_eta_emoped 3.084146 NA NA
b_POI_end_alpha_emoped -5.515382 NA NA
b_POI_end_beta_emoped 5.072686 NA NA
b_POI_end_gamma_emoped -8.525597 NA NA
b_POI_end_delta_emoped 0.754469 NA NA
b_POI_end_epsilon_emoped 0.404757 NA NA
b_POI_end_zeta_emoped 0.987306 NA NA
b_POI_end_eta_emoped -1.418269 NA NA
Code: Select all
WARNING: Singular Hessian, cannot calculate s.e.
Could not write hessian to a file.
WARNING: Some eigenvalues of the Hessian are positive, indicating
convergence to a saddle point!
Note that choices are heavily unbalanced, but the full dataset has a very large n (revealed preference), so this should be okay in the long run.
Any help is very appreciated!
