Unlabelled BNL can not estimate the s.e or t-ratio
Posted: 19 Nov 2025, 23:29
Hi Sir,
Thank you for this platform.
I am estimating an unlabelled BNL with 4 generic attributes. Since Its unlabelled, i did not specify ASC parameters.
On estimation, i get a warning that, "... the BHHH matrix is singular. Apollo will not attempt to compute the
covariance matrix." and the standard errors are not calculated,
When I add the ASCs and fix for 1 attribute, i still get the same issue.
May you kindly point in the right direction.
Here is the result:
Thank you for this platform.
I am estimating an unlabelled BNL with 4 generic attributes. Since Its unlabelled, i did not specify ASC parameters.
On estimation, i get a warning that, "... the BHHH matrix is singular. Apollo will not attempt to compute the
covariance matrix." and the standard errors are not calculated,
When I add the ASCs and fix for 1 attribute, i still get the same issue.
May you kindly point in the right direction.
Code: Select all
# ################################################################# #
#### LOAD LIBRARY AND DEFINE CORE SETTINGS ####
# ################################################################# #
### Clear memory
rm(list = ls())
### Load libraries
library(apollo)
### Initialise code
apollo_initialise()
### Set core controls
apollo_control = list(
modelName ="bnl_base",
modelDescr ="bnl base model on pedestrian choices",
indivID ="ID",
calculateLLC = TRUE,
outputDirectory = 'george_output'
)
# ################################################################# #
#### LOAD DATA AND APPLY ANY TRANSFORMATIONS ####
# ################################################################# #
database = read.csv("db.csv",header=TRUE)
### Use only SP data
#database = subset(database,database$SP==1)
### Create new variable with average income
#database$mean_monthly_income = mean(database$income_month)
database$cost_a = 0.001*database$cost_a
database$cost_b = 0.001*database$cost_b
# ################################################################# #
#### DEFINE MODEL PARAMETERS ####
# ################################################################# #
### Vector of parameters, including any that are kept fixed in estimation
apollo_beta=c(
b_walkingD = 0,
b_waitingT = 0,
b_travelT = 0,
b_cost = 0)
### 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()
# ################################################################# #
#### GROUP AND VALIDATE INPUTS ####
# ################################################################# #
apollo_inputs = apollo_validateInputs()
# ################################################################# #
#### 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()
### List of utilities: these must use the same names as in mnl_settings, order is irrelevant
V = list()
V[['a']] = b_walkingD * walkingD_a + b_waitingT * waitingT_a + b_travelT * travelT_a + b_cost * cost_a
V[['b']] = b_walkingD * walkingD_b + b_waitingT * waitingT_b + b_travelT * travelT_b + b_cost * cost_b
### Define settings for MNL model component
mnl_settings = list(
alternatives = c(a=0, b=1),
avail = list(a=1, b=1),
choiceVar = choice.Code,
V = V
)
### Compute probabilities using MNL model
P[['model']] = apollo_mnl(mnl_settings, functionality)
### Take product across observation for same individual
P = apollo_panelProd(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)
# ################################################################# #
#### MODEL OUTPUTS ####
# ################################################################# #
# ----------------------------------------------------------------- #
#---- FORMATTED OUTPUT (TO SCREEN) ----
# ----------------------------------------------------------------- #
apollo_modelOutput(model)
# ----------------------------------------------------------------- #
#---- FORMATTED OUTPUT (TO FILE, using model name) ----
# ----------------------------------------------------------------- #
#apollo_saveOutput(model)Code: Select all
> model = apollo_estimate(apollo_beta, apollo_fixed, apollo_probabilities, apollo_inputs)
Preparing user-defined functions.
Testing likelihood function...
Overview of choices for MNL model component :
a b
Times available 1784.00 1784.00
Times chosen 907.00 877.00
Percentage chosen overall 50.84 49.16
Percentage chosen when available 50.84 49.16
Pre-processing likelihood function...
Testing influence of parameters
Starting main estimation
BGW using analytic model derivatives supplied by caller...
Iterates will be written to:
george_output/bnl_base_iterations.csv
it nf F RELDF PRELDF RELDX MODEL stppar
0 1 1.236574570e+03
1 2 1.235724293e+03 6.876e-04 6.875e-04 1.00e+00 G 3.32e-01
2 3 1.235646362e+03 6.307e-05 6.307e-05 4.70e-01 G 0.00e+00
3 4 1.235646362e+03 1.296e-11 1.296e-11 1.37e-04 G 0.00e+00
***** Relative function convergence *****
G2;H2;WARNINGh: Estimation succeeded but the BHHH matrix is singular. Apollo will not attempt to compute the
covariance matrix.
Estimated parameters:
gG2; Estimate
b_walkingD -2.2440e-04
b_waitingT 0.004742
b_travelT -0.003591
b_cost -0.425999
Final LL: -1235.6464
gG2;Calculating log-likelihood at equal shares (LL(0)) for applicable models...
gG2;Calculating log-likelihood at observed shares from estimation data (LL(c)) for applicable models...
gG2;Calculating LL of each model component...
gG2;Calculating other model fit measures
gG2;
Your model was estimated using the BGW algorithm. Please acknowledge this by citing Bunch et al. (1993)
- doi.org/10.1145/151271.151279
Please acknowledge the use of Apollo by citing Hess & Palma (2019) - doi.org/10.1016/j.jocm.2019.100170
g
> apollo_modelOutput(model)
Model run by mapfuriramasimbatutsirai using Apollo 0.3.6 on R 4.5.1 for Darwin.
Please acknowledge the use of Apollo by citing Hess & Palma (2019)
DOI 10.1016/j.jocm.2019.100170
www.ApolloChoiceModelling.com
Model name : bnl_base
Model description : bnl base model on pedestrian choices
Model run at : 2025-11-20 00:57:54.461338
Estimation method : bgw
Estimation diagnosis : Relative function convergence
Optimisation diagnosis : Unknown (no Hessian calculated)
Number of individuals : 198
Number of rows in database : 1784
Number of modelled outcomes : 1784
Number of cores used : 1
Model without mixing
LL(start) : -1236.57
LL at equal shares, LL(0) : -1236.57
LL at observed shares, LL(C) : -1236.32
LL(final) : -1235.65
Rho-squared vs equal shares : 8e-04
Adj.Rho-squared vs equal shares : -0.0025
Rho-squared vs observed shares : 5e-04
Adj.Rho-squared vs observed shares : -0.0019
AIC : 2479.29
BIC : 2501.24
Estimated parameters : 4
Time taken (hh:mm:ss) : 00:00:3.98
pre-estimation : 00:00:2.61
estimation : 00:00:0.94
post-estimation : 00:00:0.43
Iterations : 3
Unconstrained optimisation.
Estimates:
Estimate s.e. t.rat.(0) Rob.s.e. Rob.t.rat.(0)
b_walkingD -2.2440e-04 NA NA NA NA
b_waitingT 0.004742 NA NA NA NA
b_travelT -0.003591 NA NA NA NA
b_cost -0.425999 NA NA NA NA