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parameter does not influence LL

Ask questions about errors you encouunter. Please make sure to include full details about your model specifications, and ideally your model file.
Post Reply
shengxiao
Posts: 8
Joined: 07 May 2021, 14:23

parameter does not influence LL

Post by shengxiao »

Dear Apollo team,

I am working on a CNL with 12 alternatives. However, I face some challenges in specifying the asc. In your CNL example on the website, you defined acs of different travel modes based on the socioeconomic attributes. In my case, my alternatives are a combination of residential locations (e.g. urban, suburb, rural) and vehicle ownership (no vehicle, less than one vehicle per driver, etc). I do not have specific variables relating to the constants in mind. Therefore, I have fixed one asc to 0, but did not define others. However, the results say: the asc does not influence the likelihood (I saw this in the Q and A on the website). If I understand correctly, as long as I fixed one asc to 0, that is fine. The function will get the other ascs for me.

I look forward to your reply. Thank you!

Best,
Alex
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stephanehess
Site Admin
Posts: 998
Joined: 24 Apr 2020, 16:29

Re: alternative specific constant (asc) in CNL

Post by stephanehess »

Hi

please include your model specification/code so we can help you

Thanks

Stephane
--------------------------------
Stephane Hess
www.stephanehess.me.uk
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shengxiao
Posts: 8
Joined: 07 May 2021, 14:23

Re: alternative specific constant (asc) in CNL

Post by shengxiao »

Thank you, Stephane. Please see my code for the model specification as follows (This is a cross-nested structure with two nests: residential location denoted by Ru (rural), SEC(secondary city), SUB(suburb), and URB (urban); car ownership denoted by D (car deficit), N (no car), S (car sufficient). All specifications for alternatives are the same except for the constants. I have fixed one constant to 0). Thank you for your help!

Specification:

### List of utilities: these must use the same names as in nl_settings, order is irrelevant
V = list()
V[['Ru_D']] = acs_Ru_D + b_age_65 * (age_65_74_1==1) + b_age_75 * (age_75_1==1) + b_AA * (race_AA_1==1) + b_HIS * (race_HIS_1==1) +
b_OTHERS * (race_OTHERS_1==1) + b_inc * (income_1) + b_med*(medc_1==1) + b_retire_o * (retire_one_1==1) +
b_retire_t * (retire_two_1==1) + b_alonem * (alonem_1==1) + b_alonef * (alonef_1==1)+ b_incm * (data$incomem_1==1)
V[['Ru_N']] = acs_Ru_N + b_age_65 * (age_65_74_2==1) + b_age_75 * (age_75_2==1) + b_AA * (race_AA_2==1) + b_HIS * (race_HIS_2==1) +
b_OTHERS * (race_OTHERS_2==1) + b_inc * (income_2) + b_med*(medc_2==1) + b_retire_o * (retire_one_2==1) +
b_retire_t * (retire_two_2==1) + b_alonem * (alonem_2==1) + b_alonef * (alonef_2==1)+ b_incm * (data$incomem_2==1)
V[['Ru_S']] = acs_Ru_S + b_age_65 * (age_65_74_3==1) + b_age_75 * (age_75_3==1) + b_AA * (race_AA_3==1) + b_HIS * (race_HIS_3==1) +
b_OTHERS * (race_OTHERS_3==1) + b_inc * (income_3) + b_med*(medc_3==1) + b_retire_o * (retire_one_3==1) +
b_retire_t * (retire_two_3==1) + b_alonem * (alonem_3==1) + b_alonef * (alonef_3==1)+ b_incm * (data$incomem_3==1)
V[['SEC_D']] = acs_SEC_D + b_age_65 * (age_65_74_4==1) + b_age_75 * (age_75_4==1) + b_AA * (race_AA_4==1) + b_HIS * (race_HIS_4==1) +
b_OTHERS * (race_OTHERS_4==1) + b_inc * (income_4) + b_med*(medc_4==1) + b_retire_o * (retire_one_4==1) +
b_retire_t * (retire_two_4==1) + b_alonem * (alonem_4==1) + b_alonef * (alonef_4==1)+ b_incm * (data$incomem_4==1)
V[['SEC_N']] = acs_SEC_N + b_age_65 * (age_65_74_5==1) + b_age_75 * (age_75_5==1) + b_AA * (race_AA_5==1) + b_HIS * (race_HIS_5==1) +
b_OTHERS * (race_OTHERS_5==1) + b_inc * (income_5) + b_med*(medc_5==1) + b_retire_o * (retire_one_5==1) +
b_retire_t * (retire_two_5==1) + b_alonem * (alonem_5==1) + b_alonef * (alonef_5==1)+ b_incm * (data$incomem_5==1)
V[['SEC_S']] = acs_SEC_S + b_age_65 * (age_65_74_6==1) + b_age_75 * (age_75_6==1) + b_AA * (race_AA_6==1) + b_HIS * (race_HIS_6==1) +
b_OTHERS * (race_OTHERS_6==1) + b_inc * (income_6) + b_med*(medc_6==1) + b_retire_o * (retire_one_6==1) +
b_retire_t * (retire_two_6==1) + b_alonem * (alonem_6==1) + b_alonef * (alonef_6==1)+ b_incm * (data$incomem_6==1)
V[['SUB_D']] = b_age_65 * (age_65_74_7==1) + b_age_75 * (age_75_7==1) + b_AA * (race_AA_7==1) + b_HIS * (race_HIS_7==1) +
b_OTHERS * (race_OTHERS_7==1) + b_inc * (income_7) + b_med*(medc_7==1) + b_retire_o * (retire_one_7==1) +
b_retire_t * (retire_two_7==1) + b_alonem * (alonem_7==1) + b_alonef * (alonef_7==1)+ b_incm * (data$incomem_7==1)
V[['SUB_N']] = acs_SUB_N + b_age_65 * (age_65_74_8==1) + b_age_75 * (age_75_8==1) + b_AA * (race_AA_8==1) + b_HIS * (race_HIS_8==1) +
b_OTHERS * (race_OTHERS_8==1) + b_inc * (income_8) + b_med*(medc_8==1) + b_retire_o * (retire_one_8==1) +
b_retire_t * (retire_two_8==1) + b_alonem * (alonem_8==1) + b_alonef * (alonef_8==1)+ b_incm * (data$incomem_8==1)
V[['SUB_S']] = acs_SUB_S + b_age_65 * (age_65_74_9==1) + b_age_75 * (age_75_9==1) + b_AA * (race_AA_9==1) + b_HIS * (race_HIS_9==1) +
b_OTHERS * (race_OTHERS_9==1) + b_inc * (income_9) + b_med*(medc_9==1) + b_retire_o * (retire_one_9==1) +
b_retire_t * (retire_two_9==1) + b_alonem * (alonem_9==1) + b_alonef * (alonef_9==1)+ b_incm * (data$incomem_9==1)
V[['URB_D']] = acs_URB_D + b_age_65 * (age_65_74_10==1) + b_age_75 * (age_75_10==1) + b_AA * (race_AA_10==1) + b_HIS * (race_HIS_10==1) +
b_OTHERS * (race_OTHERS_10==1) + b_inc * (income_10) + b_med*(medc_10==1) + b_retire_o * (retire_one_10==1) +
b_retire_t * (retire_two_10==1) + b_alonem * (alonem_10==1) + b_alonef * (alonef_10==1)+ b_incm * (data$incomem_10==1)
V[['URB_N']] = acs_URB_N + b_age_65 * (age_65_74_11==1) + b_age_75 * (age_75_11==1) + b_AA * (race_AA_11==1) + b_HIS * (race_HIS_11==1) +
b_OTHERS * (race_OTHERS_11==1) + b_inc * (income_11) + b_med*(medc_11==1) + b_retire_o * (retire_one_11==1) +
b_retire_t * (retire_two_11==1) + b_alonem * (alonem_11==1) + b_alonef * (alonef_11==1)+ b_incm * (data$incomem_11==1)
V[['URB_S']] = acs_URB_S + b_age_65 * (age_65_74_12==1) + b_age_75 * (age_75_12==1) + b_AA * (race_AA_12==1) + b_HIS * (race_HIS_12==1) +
b_OTHERS * (race_OTHERS_12==1) + b_inc * (income_12) + b_med*(medc_12==1) + b_retire_o * (retire_one_12==1) +
b_retire_t * (retire_two_12==1) + b_alonem * (alonem_12==1) + b_alonef * (alonef_12==1)+ b_incm * (data$incomem_12==1)


### Specify nests for CNL model
cnlNests = list(rural=lambda_rural, second=lambda_second,suburb=lambda_suburb,town=lambda_town,urban=lambda_urban,
DEF=lambda_DEF,NO=lambda_NO,SUF=lambda_SUF)


### Specify tree structure, showing membership in nests (one row per nest, one column per alternative)
cnlStructure = matrix(0, nrow=length(cnlNests), ncol=length(V))
cnlStructure[1,] = c(.5,.5,.5,0,0,0,0,0,0,0,0,0)
cnlStructure[2,] = c(0,0,0,.5,.5,.5,0,0,0,0,0,0)
cnlStructure[3,] = c(0,0,0,0,0,0,.5,.5,.5,0,0,0)
cnlStructure[4,] = c(0,0,0,0,0,0,0,0,0,.5,.5,.5)
cnlStructure[5,] = c(.5,0,0,.5,0,0,.5,0,0,.5,0,0)
cnlStructure[6,] = c(0,.5,0,0,.5,0,0,.5,0,0,.5,0)
cnlStructure[7,] = c(0,0,.5,0,0,.5,0,0,.5,0,0,.5)

### Define settings for NL model
cnl_settings <- list(
alternatives = c(Ru_D=1, Ru_N=2, Ru_S=3, SEC_D=4,SEC_N=5,SEC_S=6,
SUB_D=7, SUB_N=8, SUB_S=9, URB_D=10,URB_N=11,URB_S=12),
avail = list(Ru_D=av_Ru_D, Ru_N=av_Ru_N, Ru_S=av_Ru_S, SEC_D=av_SEC_D,SEC_N=av_SEC_N,SEC_S=av_SEC_S,
SUB_D=av_SUB_D, SUB_N=av_SUB_N, SUB_S=av_SUB_S,
URB_D=av_URB_D,URB_N=av_URB_N,URB_S=av_URB_S),
choiceVar = choice,
V = V,
cnlNests = cnlNests,
cnlStructure = cnlStructure
)
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stephanehess
Site Admin
Posts: 998
Joined: 24 Apr 2020, 16:29

Re: alternative specific constant (asc) in CNL

Post by stephanehess »

Thanks, could you please also show the output you're receiving, and confirm which version of Apollo you are using
--------------------------------
Stephane Hess
www.stephanehess.me.uk
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shengxiao
Posts: 8
Joined: 07 May 2021, 14:23

Re: alternative specific constant (asc) in CNL

Post by shengxiao »

Hi Stephan,
Here is my output:
Testing likelihood function...

Overview of choices for CNL model component :
Ru_D Ru_N Ru_S SEC_D SEC_N SEC_S SUB_D SUB_N SUB_S URB_D URB_N URB_S
Times available 2685.00 819 37156.00 1398.0 893.00 13148.00 1284.00 463.00 15871.00 1015.00 818 6562.00
Times chosen 2685.00 819 37156.00 1398.0 893.00 13148.00 1284.00 463.00 15871.00 1015.00 818 6562.00
Percentage chosen overall 3.27 1 45.25 1.7 1.09 16.01 1.56 0.56 19.33 1.24 1 7.99
Percentage chosen when available 100.00 100 100.00 100.0 100.00 100.00 100.00 100.00 100.00 100.00 100 100.00

Structure for CNL model component :
Ru_D (alpha) Ru_N (alpha) Ru_S (alpha) SEC_D (alpha) SEC_N (alpha) SEC_S (alpha) SUB_D (alpha) SUB_N (alpha) SUB_S (alpha)
rural nest 0.5000 0.5000 0.5000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
second nest 0.0000 0.0000 0.0000 0.5000 0.5000 0.5000 0.0000 0.0000 0.0000
suburb nest 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.5000 0.5000 0.5000
town nest 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
urban nest 0.5000 0.0000 0.0000 0.5000 0.0000 0.0000 0.5000 0.0000 0.0000
DEF nest 0.0000 0.5000 0.0000 0.0000 0.5000 0.0000 0.0000 0.5000 0.0000
NO nest 0.0000 0.0000 0.5000 0.0000 0.0000 0.5000 0.0000 0.0000 0.5000
SUF nest 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
URB_D (alpha) URB_N (alpha) URB_S (alpha) lambda
rural nest 0.0000 0.0000 0.0000 0.9500
second nest 0.0000 0.0000 0.0000 0.9500
suburb nest 0.0000 0.0000 0.0000 0.9500
town nest 0.5000 0.5000 0.5000 0.9500
urban nest 0.5000 0.0000 0.0000 0.9500
DEF nest 0.0000 0.5000 0.0000 0.9500
NO nest 0.0000 0.0000 0.5000 0.9500
SUF nest 0.0000 0.0000 0.0000 0.9500

Pre-processing likelihood function...

Testing influence of parametersError in apollo_estimate(apollo_beta, apollo_fixed, apollo_probabilities, :
Parameter acs_Ru_N does not influence the log-likelihood of your model!
>
It works in some steps, but stops due to the constant. I wonder whether there is something wrong with my specification of the constant, but I assume that it is fine to define constants without specifications.

I use the version Apollo 0.2.4 (I guess it is the latest?)
Thank you!

Best,
Alex
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stephanehess
Site Admin
Posts: 998
Joined: 24 Apr 2020, 16:29

Re: alternative specific constant (asc) in CNL

Post by stephanehess »

Hi Alex

the problem is not your ASCs, actually. The problem is clearly visible from this part of the output

Code: Select all

Times available 2685.00 819 37156.00 1398.0 893.00 13148.00 1284.00 463.00 15871.00 1015.00 818 6562.00
Times chosen 2685.00 819 37156.00 1398.0 893.00 13148.00 1284.00 463.00 15871.00 1015.00 818 6562.00
Percentage chosen overall 3.27 1 45.25 1.7 1.09 16.01 1.56 0.56 19.33 1.24 1 7.99
Percentage chosen when available 100.00 100 100.00 100.0 100.00 100.00 100.00 100.00 100.00 100.00 100 100.00
You seem to have set up the model in such a way that in each observation, only a single alternative is available, as reflected in the final line of this output. There is no model to be estimated in that case

Stephane
--------------------------------
Stephane Hess
www.stephanehess.me.uk
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shengxiao
Posts: 8
Joined: 07 May 2021, 14:23

Re: parameter does not influence LL

Post by shengxiao »

Hi Stephane,
You are correct. I just debugged, and found out that I falsely set my availabilities. I should set full availability. Now, every observation has full flexibility of choices, however, the same error message still shows up. I just paste the result here:

Code: Select all

Testing likelihood function...
Setting "avail" is missing, so full availability is assumed.
WARNING: Availability not provided (or some elements are NA). Full availability assumed.

Overview of choices for CNL model component :
                                     Ru_D  Ru_N     Ru_S   SEC_D    SEC_N    SEC_S    SUB_D    SUB_N    SUB_S    URB_D URB_N    URB_S
Times available                  82112.00 82112 82112.00 82112.0 82112.00 82112.00 82112.00 82112.00 82112.00 82112.00 82112 82112.00
Times chosen                      2685.00   819 37156.00  1398.0   893.00 13148.00  1284.00   463.00 15871.00  1015.00   818  6562.00
Percentage chosen overall            3.27     1    45.25     1.7     1.09    16.01     1.56     0.56    19.33     1.24     1     7.99
Percentage chosen when available     3.27     1    45.25     1.7     1.09    16.01     1.56     0.56    19.33     1.24     1     7.99

Structure for CNL model component :
            Ru_D (alpha) Ru_N (alpha) Ru_S (alpha) SEC_D (alpha) SEC_N (alpha) SEC_S (alpha) SUB_D (alpha) SUB_N (alpha) SUB_S (alpha)
rural nest        0.5000       0.5000       0.5000        0.0000        0.0000        0.0000        0.0000        0.0000        0.0000
second nest       0.0000       0.0000       0.0000        0.5000        0.5000        0.5000        0.0000        0.0000        0.0000
suburb nest       0.0000       0.0000       0.0000        0.0000        0.0000        0.0000        0.5000        0.5000        0.5000
town nest         0.0000       0.0000       0.0000        0.0000        0.0000        0.0000        0.0000        0.0000        0.0000
urban nest        0.5000       0.0000       0.0000        0.5000        0.0000        0.0000        0.5000        0.0000        0.0000
DEF nest          0.0000       0.5000       0.0000        0.0000        0.5000        0.0000        0.0000        0.5000        0.0000
NO nest           0.0000       0.0000       0.5000        0.0000        0.0000        0.5000        0.0000        0.0000        0.5000
SUF nest          0.0000       0.0000       0.0000        0.0000        0.0000        0.0000        0.0000        0.0000        0.0000
            URB_D (alpha) URB_N (alpha) URB_S (alpha)      lambda
rural nest         0.0000        0.0000        0.0000      0.9500
second nest        0.0000        0.0000        0.0000      0.9500
suburb nest        0.0000        0.0000        0.0000      0.9500
town nest          0.5000        0.5000        0.5000      0.9500
urban nest         0.5000        0.0000        0.0000      0.9500
DEF nest           0.0000        0.5000        0.0000      0.9500
NO nest            0.0000        0.0000        0.5000      0.9500
SUF nest           0.0000        0.0000        0.0000      0.9500

Pre-processing likelihood function...

Testing influence of parameters..........Error in apollo_estimate(apollo_beta, apollo_fixed, apollo_probabilities,  : 
  Parameter b_age_65 does not influence the log-likelihood of your model!
I wonder what I did wrongly. So let us assume that it is not the problem of the asc. I compared my codes with yours very carefully, and found that what I did differently is that I did not set the alpha function. I simply let the allocation parameters to be 0 or 0.5, as what you did in your paper published on Transportation, and many other transportation planners. So I guess Apollo will not consider that as a problem. I just copy/paste my code here so that you can see clearly what is evidently wrong:

Code: Select all

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 nl_settings, order is irrelevant
  V = list()
  V[['Ru_D']]  = acs_Ru_D + b_age_65 * (age_65_74==1) + b_age_75 * (age_75==1) + b_AA * (race_AA==1) + b_HIS * (race_HIS==1) + 
    b_OTHERS * (race_OTHERS==1) + b_inc * (income) + b_med*(medc==1) + b_retire_o * (retire_one==1) + 
    b_retire_t * (retire_two==1) + b_alonem * (alonem==1) + b_alonef * (alonef==1)+ b_incm * (data$incomem==1)
  V[['Ru_N']]  = acs_Ru_N + b_age_65 * (age_65_74==1) + b_age_75 * (age_75==1) + b_AA * (race_AA==1) + b_HIS * (race_HIS==1) + 
    b_OTHERS * (race_OTHERS==1) + b_inc * (income) + b_med*(medc==1) + b_retire_o * (retire_one==1) + 
    b_retire_t * (retire_two==1) + b_alonem * (alonem==1) + b_alonef * (alonef==1)+ b_incm * (data$incomem==1)
  V[['Ru_S']]  = acs_Ru_S + b_age_65 * (age_65_74==1) + b_age_75 * (age_75==1) + b_AA * (race_AA==1) + b_HIS * (race_HIS==1) + 
    b_OTHERS * (race_OTHERS==1) + b_inc * (income) + b_med*(medc==1) + b_retire_o * (retire_one==1) + 
    b_retire_t * (retire_two==1) + b_alonem * (alonem==1) + b_alonef * (alonef==1)+ b_incm * (data$incomem==1)
  V[['SEC_D']]  = acs_SEC_D + b_age_65 * (age_65_74==1) + b_age_75 * (age_75==1) + b_AA * (race_AA==1) + b_HIS * (race_HIS==1) + 
    b_OTHERS * (race_OTHERS==1) + b_inc * (income) + b_med*(medc==1) + b_retire_o * (retire_one==1) + 
    b_retire_t * (retire_two==1) + b_alonem * (alonem==1) + b_alonef * (alonef==1)+ b_incm * (data$incomem==1)
  V[['SEC_N']]  = acs_SEC_N + b_age_65 * (age_65_74==1) + b_age_75 * (age_75==1) + b_AA * (race_AA==1) + b_HIS * (race_HIS==1) + 
    b_OTHERS * (race_OTHERS==1) + b_inc * (income) + b_med*(medc==1) + b_retire_o * (retire_one==1) + 
    b_retire_t * (retire_two==1) + b_alonem * (alonem==1) + b_alonef * (alonef==1)+ b_incm * (data$incomem==1)
  V[['SEC_S']]  = acs_SEC_S + b_age_65 * (age_65_74==1) + b_age_75 * (age_75==1) + b_AA * (race_AA==1) + b_HIS * (race_HIS==1) + 
    b_OTHERS * (race_OTHERS==1) + b_inc * (income) + b_med*(medc==1) + b_retire_o * (retire_one==1) + 
    b_retire_t * (retire_two==1) + b_alonem * (alonem==1) + b_alonef * (alonef==1)+ b_incm * (data$incomem==1)
  V[['SUB_D']]  = b_age_65 * (age_65_74==1) + b_age_75 * (age_75==1) + b_AA * (race_AA==1) + b_HIS * (race_HIS==1) + 
    b_OTHERS * (race_OTHERS==1) + b_inc * (income) + b_med*(medc==1) + b_retire_o * (retire_one==1) + 
    b_retire_t * (retire_two==1) + b_alonem * (alonem==1) + b_alonef * (alonef==1)+ b_incm * (data$incomem==1)
  V[['SUB_N']]  = acs_SUB_N + b_age_65 * (age_65_74==1) + b_age_75 * (age_75==1) + b_AA * (race_AA==1) + b_HIS * (race_HIS==1) + 
    b_OTHERS * (race_OTHERS==1) + b_inc * (income) + b_med*(medc==1) + b_retire_o * (retire_one==1) + 
    b_retire_t * (retire_two==1) + b_alonem * (alonem==1) + b_alonef * (alonef==1)+ b_incm * (data$incomem==1)
  V[['SUB_S']]  = acs_SUB_S + b_age_65 * (age_65_74==1) + b_age_75 * (age_75==1) + b_AA * (race_AA==1) + b_HIS * (race_HIS==1) + 
    b_OTHERS * (race_OTHERS==1) + b_inc * (income) + b_med*(medc==1) + b_retire_o * (retire_one==1) + 
    b_retire_t * (retire_two==1) + b_alonem * (alonem==1) + b_alonef * (alonef==1)+ b_incm * (data$incomem==1)
  V[['URB_D']]  = acs_URB_D + b_age_65 * (age_65_74==1) + b_age_75 * (age_75==1) + b_AA * (race_AA==1) + b_HIS * (race_HIS==1) + 
    b_OTHERS * (race_OTHERS==1) + b_inc * (income) + b_med*(medc==1) + b_retire_o * (retire_one==1) + 
    b_retire_t * (retire_two==1) + b_alonem * (alonem==1) + b_alonef * (alonef==1)+ b_incm * (data$incomem==1)
  V[['URB_N']]  = acs_URB_N + b_age_65 * (age_65_74==1) + b_age_75 * (age_75==1) + b_AA * (race_AA==1) + b_HIS * (race_HIS==1) + 
    b_OTHERS * (race_OTHERS==1) + b_inc * (income) + b_med*(medc==1) + b_retire_o * (retire_one==1) + 
    b_retire_t * (retire_two==1) + b_alonem * (alonem==1) + b_alonef * (alonef==1)+ b_incm * (data$incomem==1)
  V[['URB_S']]  = acs_URB_S + b_age_65 * (age_65_74==1) + b_age_75 * (age_75==1) + b_AA * (race_AA==1) + b_HIS * (race_HIS==1) + 
    b_OTHERS * (race_OTHERS==1) + b_inc * (income) + b_med*(medc==1) + b_retire_o * (retire_one==1) + 
    b_retire_t * (retire_two==1) + b_alonem * (alonem==1) + b_alonef * (alonef==1)+ b_incm * (data$incomem==1)
  
  
  ### Specify nests for CNL model
  cnlNests = list(rural=lambda_rural, second=lambda_second,suburb=lambda_suburb,town=lambda_town,urban=lambda_urban,
                  DEF=lambda_DEF,NO=lambda_NO,SUF=lambda_SUF)
  
  
  ### Specify tree structure, showing membership in nests (one row per nest, one column per alternative)
  cnlStructure      = matrix(0, nrow=length(cnlNests), ncol=length(V))
  cnlStructure[1,] = c(.5,.5,.5,0,0,0,0,0,0,0,0,0)
  cnlStructure[2,] = c(0,0,0,.5,.5,.5,0,0,0,0,0,0)
  cnlStructure[3,] = c(0,0,0,0,0,0,.5,.5,.5,0,0,0)
  cnlStructure[4,] = c(0,0,0,0,0,0,0,0,0,.5,.5,.5) 
  cnlStructure[5,] = c(.5,0,0,.5,0,0,.5,0,0,.5,0,0)
  cnlStructure[6,] = c(0,.5,0,0,.5,0,0,.5,0,0,.5,0)
  cnlStructure[7,] = c(0,0,.5,0,0,.5,0,0,.5,0,0,.5)
  
  ### Define settings for NL model
  cnl_settings <- list(
    alternatives = c(Ru_D=1, Ru_N=2, Ru_S=3, SEC_D=4,SEC_N=5,SEC_S=6,
                     SUB_D=7, SUB_N=8, SUB_S=9, URB_D=10,URB_N=11,URB_S=12),
    choiceVar    = choice,
    V            = V,
    cnlNests      = cnlNests,
    cnlStructure  = cnlStructure
  )
  ### Compute probabilities using CNL model
  P[["model"]] = apollo_cnl(cnl_settings, functionality)
  
  ### Prepare and return outputs of function
  P = apollo_prepareProb(P, apollo_inputs, functionality)
  return(P)
}
Thank you,
Alex
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stephanehess
Site Admin
Posts: 998
Joined: 24 Apr 2020, 16:29

Re: parameter does not influence LL

Post by stephanehess »

Alex

from what I can see, you have included b_age_65 * (age_65_74==1) in every utility function. Then it will just drop out of the model, that's why you're getting that message. You can't estimate a value for a parameter that multiplies the exact same value in each utility function

Stephane
--------------------------------
Stephane Hess
www.stephanehess.me.uk
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shengxiao
Posts: 8
Joined: 07 May 2021, 14:23

Re: parameter does not influence LL

Post by shengxiao »

Hi Stephane,

That is interesting. I would appreciate it if you can further elaborate. Let me explain further what I am trying to do here. As I can see from the manual, I should change all categorical variables to dummy variables. As you can see in my specifications, this is what I did, and I just dropped of one category to avoid multilinearity. For example, whether a person's age is 55-64, 65-74, or 75 and over affect the utility of all alternatives. Therefore, I just include the dummy of being the age group of 65-74 and 75 and over in my specification. This is what I want to do. And the error message comes. In this case, did I do anything wrong? How should I include the categorical variables in the specifications?

Thank you,
Alex
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stephanehess
Site Admin
Posts: 998
Joined: 24 Apr 2020, 16:29

Re: parameter does not influence LL

Post by stephanehess »

Alex

it is of course a requirement to apply a normalisation to a categorical variable, which is what you do here by dummy coding it, as only differences across levels matter. However, an essential characteristics of random utility models is that only differences in utility matter. If you include the term b_age_65 * (age_65_74==1) in the utility for each alternative, with the same parameter, then it cannot uncover any differences across the alternatives. Is your aim to test whether the mode preferences depend on age? If so, you will need to make these age parameters mode specific too, just like your alternative specific constants, and set them to zero for one mode

Stephane
--------------------------------
Stephane Hess
www.stephanehess.me.uk
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