MMNL on SP/RP data - multiple cores error

[Mat2089119]

Hi,

I would like to estimate a mixed-logit from SP and RP data. The data comes from different RP/SP individuals, the SP data has 4 choice tasks for each individual, the RP data is just a single observation. I do not have the socio-demographics for the RP observations so they should only be used to scale the model.

The model is estimating on a single core, but this takes obviously very long with the complexity. When specifying multiple cores, I am encounting issues. Exactly the same has happened with a standard MNL, but there the computation speed was not such an issue.

First approach, error: "checkForRemoteErrors(lapply(cl, recvResult)) :
10 nodes produced errors; first error: SPECIFICATION ISSUE - You attempted to call the function apollo_panelProd at a stage where the probabilities are already at the individual level!"

Code: Select all

##############################################################
#### 1.  LOAD LIBRARY & CORE SETTINGS                      ####
##############################################################
library(apollo)
apollo_initialise()

apollo_control = list(
  modelName       = "MMNL_RP_SP",
  modelDescr      = "RP–SP mixed logit",
  indivID         = "RID",
  nCores          = 4,            # adjust to your machine
  outputDirectory = "output"
)

##############################################################
#### 2.  LOAD DATA                                         ####
##############################################################
database <- data  

##############################################################
#### 3.  DEFINE FIXED & MEAN/S.D. PARAMETERS               ####
##############################################################
apollo_beta = c(

  ## ─── Random coefficients: means & st. devs ─────────────────
  mu_asc_opt1    =  0,  sigma_asc_opt1    =  1,
  mu_asc_opt2    =  0,  sigma_asc_opt2    =  1,
  mu_asc_opt3    =  0,  sigma_asc_opt3    =  1,
  mu_log_b_price = -3, sigma_log_b_price = .5,  # log-normal, keeps sign < 0

  ## ─── Fixed taste parameters ────────────────────────────────
  b_opt3                 =  0,

  ## SP interactions: gender
  b_gender_opt1          =  0,  b_gender_opt2       = 0,  b_gender_opt3      = 0,
  ## SP interactions: city centre
  b_cityCenter_opt1      =  0,  b_cityCenter_opt2   = 0,  b_cityCenter_opt3  = 0,
  ## SP interactions: age categories
  b_age18_29_opt1        =  0,  b_age18_29_opt2     = 0,  b_age18_29_opt3    = 0,
  b_age30_44_opt1        =  0,  b_age30_44_opt2     = 0,  b_age30_44_opt3    = 0,
  b_age45_64_opt1        =  0,  b_age45_64_opt2     = 0,  b_age45_64_opt3    = 0,
  ## SP interactions: category segments 1–5
  b_cat1_opt1 = 0, b_cat1_opt2 = 0, b_cat1_opt3 = 0,
  b_cat2_opt1 = 0, b_cat2_opt2 = 0, b_cat2_opt3 = 0,
  b_cat3_opt1 = 0, b_cat3_opt2 = 0, b_cat3_opt3 = 0,
  b_cat4_opt1 = 0, b_cat4_opt2 = 0, b_cat4_opt3 = 0,
  b_cat5_opt1 = 0, b_cat5_opt2 = 0, b_cat5_opt3 = 0,
  ## SP interactions: PT subscription dummy
  b_ptsub_opt1 = 0, b_ptsub_opt2 = 0, b_ptsub_opt3 = 0,

  ## Scale parameters
  mu_RP  = 1,   # fixed
  mu_SP  = 1    # to be estimated
)

apollo_fixed = c("mu_RP")         # keep RP scale normalised

##############################################################
#### 4.  RANDOM COEFFICIENTS & DRAWS                        ####
##############################################################
apollo_draws = list(
  interDrawsType = "halton",
  interNDraws    = 500,
  interNormDraws = c("d_asc_opt1","d_asc_opt2","d_asc_opt3","d_b_price"),
  intraDrawsType = "none",  intraNDraws = 0
)

apollo_randCoeff = function(apollo_beta, apollo_inputs){
  rc = list()

  ## Normally-distributed ASCs
  rc[["asc_opt1"]] =  mu_asc_opt1 + sigma_asc_opt1 * d_asc_opt1
  rc[["asc_opt2"]] =  mu_asc_opt2 + sigma_asc_opt2 * d_asc_opt2
  rc[["asc_opt3"]] =  mu_asc_opt3 + sigma_asc_opt3 * d_asc_opt3

  ## Log-normal (negative) price coefficient
  rc[["b_price"]]  = -exp(mu_log_b_price + sigma_log_b_price * d_b_price)

  return(rc)
}

##############################################################
#### 5.  GROUP & VALIDATE INPUTS                            ####
##############################################################
apollo_inputs = apollo_validateInputs()

##############################################################
#### 6.  LIKELIHOOD FUNCTION                                ####
##############################################################
apollo_probabilities = function(apollo_beta, apollo_inputs, functionality="estimate"){  

  apollo_attach(apollo_beta, apollo_inputs)
  on.exit(apollo_detach(apollo_beta, apollo_inputs))

  P = list()                                          # container

  ## ── (1) SP-only ASC terms with interactions ─────────────────
  asc_opt1_val = asc_opt1
  asc_opt2_val = asc_opt2
  asc_opt3_val = asc_opt3

  if(functionality=="estimate"){                      # only needed when estimating
    asc_opt1_val = asc_opt1_val +
      ifelse(rp==0, b_gender_opt1    * gender,        0) +
      ifelse(rp==0, b_cityCenter_opt1* city_center,   0) +
      ifelse(rp==0, b_age18_29_opt1  * age_cat_18_29, 0) +
      ifelse(rp==0, b_age30_44_opt1  * age_cat_30_44, 0) +
      ifelse(rp==0, b_age45_64_opt1  * age_cat_45_64, 0) +
      ifelse(rp==0, b_cat1_opt1      * category_1,    0) +
      ifelse(rp==0, b_cat2_opt1      * category_2,    0) +
      ifelse(rp==0, b_cat3_opt1      * category_3,    0) +
      ifelse(rp==0, b_cat4_opt1      * category_4,    0) +
      ifelse(rp==0, b_cat5_opt1      * category_5,    0) +
      ifelse(rp==0, b_ptsub_opt1     * ptsub,         0)

    asc_opt2_val = asc_opt2_val +
      ifelse(rp==0, b_gender_opt2    * gender,        0) +
      ifelse(rp==0, b_cityCenter_opt2* city_center,   0) +
      ifelse(rp==0, b_age18_29_opt2  * age_cat_18_29, 0) +
      ifelse(rp==0, b_age30_44_opt2  * age_cat_30_44, 0) +
      ifelse(rp==0, b_age45_64_opt2  * age_cat_45_64, 0) +
      ifelse(rp==0, b_cat1_opt2      * category_1,    0) +
      ifelse(rp==0, b_cat2_opt2      * category_2,    0) +
      ifelse(rp==0, b_cat3_opt2      * category_3,    0) +
      ifelse(rp==0, b_cat4_opt2      * category_4,    0) +
      ifelse(rp==0, b_cat5_opt2      * category_5,    0) +
      ifelse(rp==0, b_ptsub_opt2     * ptsub,         0)

    asc_opt3_val = asc_opt3_val +
      ifelse(rp==0, b_gender_opt3    * gender,        0) +
      ifelse(rp==0, b_cityCenter_opt3* city_center,   0) +
      ifelse(rp==0, b_age18_29_opt3  * age_cat_18_29, 0) +
      ifelse(rp==0, b_age30_44_opt3  * age_cat_30_44, 0) +
      ifelse(rp==0, b_age45_64_opt3  * age_cat_45_64, 0) +
      ifelse(rp==0, b_cat1_opt3      * category_1,    0) +
      ifelse(rp==0, b_cat2_opt3      * category_2,    0) +
      ifelse(rp==0, b_cat3_opt3      * category_3,    0) +
      ifelse(rp==0, b_cat4_opt3      * category_4,    0) +
      ifelse(rp==0, b_cat5_opt3      * category_5,    0) +
      ifelse(rp==0, b_ptsub_opt3     * ptsub,         0)
  }

  ## ── (2) Utilities ───────────────────────────────────────────
  V = list(
    opt1   = asc_opt1_val + b_price * price_opt1,
    opt2   = asc_opt2_val + b_price * price_opt2,
    opt3   = asc_opt3_val + b_price * price_opt3 + b_opt3 * opt3,
    opt4   = 0
  )

  ## ── (3) RP model block ──────────────────────────────────────
  mnl_RP = list(
    alternatives = c(opt1=1, opt2=2, opt3=3, opt4=4),
    avail        = list(opt1=avail_opt1, opt2=avail_opt2, opt3=avail_opt3, opt4=1),
    choiceVar    = pref1,
    utilities    = lapply(V, function(x) mu_RP * x),
    rows         = (rp==1)
  )
  P[["RP"]] = apollo_mnl(mnl_RP, functionality)

  ## ── (4) SP model block ──────────────────────────────────────
  mnl_SP = list(
    alternatives = c(opt1=1, opt2=2, opt3=3, opt4=4),
    avail        = list(opt1=avail_opt1, opt2=avail_opt2, opt3=avail_opt3, opt4=1),
    choiceVar    = pref1,
    utilities    = lapply(V, function(x) mu_SP * x),
    rows         = (rp==0)
  )
  P[["SP"]] = apollo_mnl(mnl_SP, functionality)

  ## ── (5) Combine, panel product, average draws, return ───────
  P = apollo_combineModels(P, apollo_inputs, functionality)
  P = apollo_panelProd(       P, apollo_inputs, functionality)
  P = apollo_avgInterDraws(   P, apollo_inputs, functionality)
  P = apollo_prepareProb(     P, apollo_inputs, functionality)
  return(P)
}

##############################################################
#### 7.  ESTIMATE                                           ####
##############################################################
model = apollo_estimate(
  apollo_beta, apollo_fixed,
  apollo_probabilities, apollo_inputs
)

##############################################################
#### 8.  OUTPUT                                             ####
##############################################################
apollo_modelOutput(model)
apollo_saveOutput(model)

With the help of ChatGPT I tried a different solution, but now the error becomes: "apollo_avgInterDraws(P, apollo_inputs, functionality) :
SPECIFICATION ISSUE - Observations from the same individual must be combined (i.e. multiplied) before averaging over inter-individual draws."

Code: Select all

##############################################################
#### 1. LIBRARIES & CORE CONTROLS                          ####
##############################################################
library(apollo)
apollo_initialise()

apollo_control = list(
  modelName       = "RP–SP mixed logit",
  modelDescr      = "RP–SP mixed logit",
  indivID         = "RID",
  nCores          = 4,
  outputDirectory = "output"
)

##############################################################
#### 2. LOAD DATA                                          ####
##############################################################
database <- data

##############################################################
#### 3. STARTING VALUES                                    ####
##############################################################
apollo_beta = c(
  ## random-coeff means & s.d.'s
  mu_asc_opt1    =  0, sigma_asc_opt1    = 1,
  mu_asc_opt2    =  0, sigma_asc_opt2    = 1,
  mu_asc_opt3    =  0, sigma_asc_opt3    = 1,
  mu_log_b_price = -3, sigma_log_b_price = .5,
  ## fixed taste parameters
  b_opt3             = 0,
  b_gender_opt1      = 0, b_gender_opt2     = 0, b_gender_opt3     = 0,
  b_cityCenter_opt1  = 0, b_cityCenter_opt2 = 0, b_cityCenter_opt3 = 0,
  b_age18_29_opt1    = 0, b_age18_29_opt2   = 0, b_age18_29_opt3   = 0,
  b_age30_44_opt1    = 0, b_age30_44_opt2   = 0, b_age30_44_opt3   = 0,
  b_age45_64_opt1    = 0, b_age45_64_opt2   = 0, b_age45_64_opt3   = 0,
  b_cat1_opt1        = 0, b_cat1_opt2       = 0, b_cat1_opt3       = 0,
  b_cat2_opt1        = 0, b_cat2_opt2       = 0, b_cat2_opt3       = 0,
  b_cat3_opt1        = 0, b_cat3_opt2       = 0, b_cat3_opt3       = 0,
  b_cat4_opt1        = 0, b_cat4_opt2       = 0, b_cat4_opt3       = 0,
  b_cat5_opt1        = 0, b_cat5_opt2       = 0, b_cat5_opt3       = 0,
  b_ptsub_opt1       = 0, b_ptsub_opt2      = 0, b_ptsub_opt3      = 0,
  ## scales
  mu_RP             = 1,
  mu_SP             = 1
)
apollo_fixed = c("mu_RP")     # keep RP scale normalised

##############################################################
#### 4. DRAWS & RANDOM COEFFICIENTS                       ####
##############################################################
apollo_draws = list(
  interDrawsType = "halton",
  interNDraws    = 500,
  interNormDraws = c("d_asc_opt1","d_asc_opt2","d_asc_opt3","d_b_price"),
  intraDrawsType = "none",
  intraNDraws    = 0
)

apollo_randCoeff = function(apollo_beta, apollo_inputs){
  rc = list()
  rc[["asc_opt1"]] = mu_asc_opt1 + sigma_asc_opt1 * d_asc_opt1
  rc[["asc_opt2"]] = mu_asc_opt2 + sigma_asc_opt2 * d_asc_opt2
  rc[["asc_opt3"]] = mu_asc_opt3 + sigma_asc_opt3 * d_asc_opt3
  rc[["b_price"]] = -exp(mu_log_b_price + sigma_log_b_price * d_b_price)  # log-normal <0
  return(rc)
}

##############################################################
#### 5. VALIDATE INPUTS                                   ####
##############################################################
apollo_inputs = apollo_validateInputs()

##############################################################
#### 6. LIKELIHOOD                                        ####
##############################################################
apollo_probabilities = function(apollo_beta, apollo_inputs,
                                functionality = "estimate"){  

  apollo_attach(apollo_beta, apollo_inputs)
  on.exit(apollo_detach(apollo_beta, apollo_inputs))

  P = list()

  ## --- 6.1 SP-only ASC adjustments ---------------------------
  asc_opt1_val = asc_opt1
  asc_opt2_val = asc_opt2
  asc_opt3_val = asc_opt3
  if(functionality == "estimate"){  
    asc_opt1_val = asc_opt1_val +
      ifelse(rp==0, b_gender_opt1    * gender,      0) +
      ifelse(rp==0, b_cityCenter_opt1* city_center, 0) +
      ifelse(rp==0, b_age18_29_opt1  * age_cat_18_29,0) +
      ifelse(rp==0, b_age30_44_opt1  * age_cat_30_44,0) +
      ifelse(rp==0, b_age45_64_opt1  * age_cat_45_64,0) +
      ifelse(rp==0, b_cat1_opt1      * category_1,  0) +
      ifelse(rp==0, b_cat2_opt1      * category_2,  0) +
      ifelse(rp==0, b_cat3_opt1      * category_3,  0) +
      ifelse(rp==0, b_cat4_opt1      * category_4,  0) +
      ifelse(rp==0, b_cat5_opt1      * category_5,  0) +
      ifelse(rp==0, b_ptsub_opt1     * ptsub,       0)
    asc_opt2_val = asc_opt2_val +
      ifelse(rp==0, b_gender_opt2    * gender,      0) +
      ifelse(rp==0, b_cityCenter_opt2* city_center, 0) +
      ifelse(rp==0, b_age18_29_opt2  * age_cat_18_29,0) +
      ifelse(rp==0, b_age30_44_opt2  * age_cat_30_44,0) +
      ifelse(rp==0, b_age45_64_opt2  * age_cat_45_64,0) +
      ifelse(rp==0, b_cat1_opt2      * category_1,  0) +
      ifelse(rp==0, b_cat2_opt2      * category_2,  0) +
      ifelse(rp==0, b_cat3_opt2      * category_3,  0) +
      ifelse(rp==0, b_cat4_opt2      * category_4,  0) +
      ifelse(rp==0, b_cat5_opt2      * category_5,  0) +
      ifelse(rp==0, b_ptsub_opt2     * ptsub,       0)
    asc_opt3_val = asc_opt3_val +
      ifelse(rp==0, b_gender_opt3    * gender,      0) +
      ifelse(rp==0, b_cityCenter_opt3* city_center, 0) +
      ifelse(rp==0, b_age18_29_opt3  * age_cat_18_29,0) +
      ifelse(rp==0, b_age30_44_opt3  * age_cat_30_44,0) +
      ifelse(rp==0, b_age45_64_opt3  * age_cat_45_64,0) +
      ifelse(rp==0, b_cat1_opt3      * category_1,  0) +
      ifelse(rp==0, b_cat2_opt3      * category_2,  0) +
      ifelse(rp==0, b_cat3_opt3      * category_3,  0) +
      ifelse(rp==0, b_cat4_opt3      * category_4,  0) +
      ifelse(rp==0, b_cat5_opt3      * category_5,  0) +
      ifelse(rp==0, b_ptsub_opt3     * ptsub,       0)
  }

  ## --- 6.2 Utilities -----------------------------------------
  V = list(
    opt1   = asc_opt1_val + b_price * price_opt1,
    opt2   = asc_opt2_val + b_price * price_opt2,
    opt3   = asc_opt3_val + b_price * price_opt3 + b_opt3 * opt3,
    opt4   = 0
  )

  ## --- 6.3 RP likelihood (one row per person) ----------------
  mnl_RP = list(
    alternatives = c(opt1=1, opt2=2, opt3=3, opt4=4),
    avail        = list(opt1=avail_opt1, opt2=avail_opt2, opt3=avail_opt3, opt4=1),
    choiceVar    = pref1,
    utilities    = lapply(V, function(x) mu_RP * x),
    rows         = (rp == 1)
  )
  P[["RP"]] = apollo_mnl(mnl_RP, functionality)

  ## --- 6.4 SP likelihood (panel) -----------------------------
  mnl_SP = list(
    alternatives = c(opt1=1, opt2=2, opt3=3, opt4=4),
    avail        = list(opt1=avail_opt1, opt2=avail_opt2, opt3=avail_opt3, opt4=1),
    choiceVar    = pref1,
    utilities    = lapply(V, function(x) mu_SP * x),
    rows         = (rp == 0)
  )
  P[["SP"]] = apollo_mnl(mnl_SP, functionality)

  ## --- 6.5 Combine RP & SP (still obs × draw) ----------------
  P = apollo_combineModels(P, apollo_inputs, functionality)

  ## --- 6.6 Collapse across panel observations ----------------
  if(any(apollo_inputs$rowsPerIndiv > 1)){
    P = apollo_panelProd(P, apollo_inputs, functionality)
  }

  ## --- 6.7 Average draws & tidy ------------------------------
  P = apollo_avgInterDraws(P, apollo_inputs, functionality)
  P = apollo_prepareProb(P, apollo_inputs, functionality)
  return(P)
}

##############################################################
#### 7. ESTIMATE                                           ####
##############################################################
model = apollo_estimate(
  apollo_beta, apollo_fixed,
  apollo_probabilities, apollo_inputs
)

##############################################################
#### 8. OUTPUT                                             ####
##############################################################
apollo_modelOutput(model)
apollo_saveOutput(model)

Thank you for looking at this!

[stephanehess]

Hi

I suspect your issue might come from

Code: Select all

if(functionality=="estimate"){                      # only needed when estimating

why are you doing this? It will mean that during model validation, this part of the code is not used

Stephane

[Mat2089119]

Thanks for the quick reply. This seems to have been added when I was trying to debug the error with ChatGPT.

However, exactly the same error has happened before and is still happening when I remove this if statement. It also happens with a simple MNL version: 2 nodes produced errors; first error: SPECIFICATION ISSUE - You attempted to call the function apollo_panelProd at a stage where the probabilities are already at the individual level!

Is is possible that is caused by the different structure of the SP/RP data (SP is panel, RP is not)?

Code: Select all

 ################################################################# #
#### LOAD LIBRARY AND DEFINE CORE SETTINGS                       ####
# ################################################################# #
library(apollo)
apollo_initialise()

apollo_control = list(
  modelName       = "MNL_RP_SP",
  modelDescr      = "RP-SP model for opt1 / opt2 / opt3 / opt4 choice",
  indivID         = "RID",
  outputDirectory = "output",
  nCores          = 4
)

# ################################################################# #
#### LOAD DATA                                                   ####
# ################################################################# #

database <- cov_df

# ################################################################# #
#### DEFINE MODEL PARAMETERS                                     ####
# ################################################################# #

apollo_beta = c(
  ## ASCs
  asc_opt1   = 0,
  asc_opt2   = 0,
  asc_opt3   = 0,
  ## common taste parameters
  b_price    = 0,
  b_opt3     = 0,
  ## SP interactions: gender
  b_gender_opt1   = 0,
  b_gender_opt2   = 0,
  b_gender_opt3   = 0,
  ## SP interactions: city_center
  b_cityCenter_opt1 = 0,
  b_cityCenter_opt2 = 0,
  b_cityCenter_opt3 = 0,
  ## SP interactions: age categories
  b_age18_29_opt1   = 0,
  b_age18_29_opt2   = 0,
  b_age18_29_opt3   = 0,
  b_age30_44_opt1   = 0,
  b_age30_44_opt2   = 0,
  b_age30_44_opt3   = 0,
  b_age45_64_opt1   = 0,
  b_age45_64_opt2   = 0,
  b_age45_64_opt3   = 0,
  ## SP interactions: category segments 1–5
  b_cat1_opt1 = 0,
  b_cat1_opt2 = 0,
  b_cat1_opt3 = 0,
  b_cat2_opt1 = 0,
  b_cat2_opt2 = 0,
  b_cat2_opt3 = 0,
  b_cat3_opt1 = 0,
  b_cat3_opt2 = 0,
  b_cat3_opt3 = 0,
  b_cat4_opt1 = 0,
  b_cat4_opt2 = 0,
  b_cat4_opt3 = 0,
  b_cat5_opt1 = 0,
  b_cat5_opt2 = 0,
  b_cat5_opt3 = 0,
  b_ptsub_opt1 = 0,
  b_ptsub_opt2 = 0,
  b_ptsub_opt3 = 0,
  ## scale parameters
  mu_RP      = 1,
  mu_SP      = 0.65
)

## fix scale of RP
apollo_fixed = c("mu_RP")

# ################################################################# #
#### VALIDATE INPUTS                                             ####
# ################################################################# #

apollo_inputs = apollo_validateInputs()

# ################################################################# #
#### DEFINE LIKELIHOOD                                            ####
# ################################################################# #

apollo_probabilities = function(apollo_beta, apollo_inputs, functionality="estimate") {
  apollo_attach(apollo_beta, apollo_inputs)
  on.exit(apollo_detach(apollo_beta, apollo_inputs))
  
  P = list()
  
  ## 1) Build SP‐only ASCs with every socio demo interaction
  asc_opt1_val = asc_opt1
  asc_opt2_val = asc_opt2
  asc_opt3_val = asc_opt3
  
  ## SP‐only ASC for opt1
  asc_opt1_val = asc_opt1 +
      ifelse(rp==0, b_gender_opt1      * gender,           0) +
      ifelse(rp==0, b_cityCenter_opt1  * city_center,      0) +
      ifelse(rp==0, b_age18_29_opt1    * age_cat_18_29,    0) +
      ifelse(rp==0, b_age30_44_opt1    * age_cat_30_44,    0) +
      ifelse(rp==0, b_age45_64_opt1    * age_cat_45_64,    0) +
      ifelse(rp==0, b_cat1_opt1       * category_1,       0) +
      ifelse(rp==0, b_cat2_opt1       * category_2,       0) +
      ifelse(rp==0, b_cat3_opt1       * category_3,       0) +
      ifelse(rp==0, b_cat4_opt1       * category_4,       0) +
      ifelse(rp==0, b_cat5_opt1       * category_5,       0) +
      ifelse(rp==0, b_ptsub_opt1      * ptsub,            0)
    
  ## SP‐only ASC for opt2
  asc_opt2_val = asc_opt2 +
      ifelse(rp==0, b_gender_opt2      * gender,           0) +
      ifelse(rp==0, b_cityCenter_opt2  * city_center,      0) +
      ifelse(rp==0, b_age18_29_opt2    * age_cat_18_29,    0) +
      ifelse(rp==0, b_age30_44_opt2    * age_cat_30_44,    0) +
      ifelse(rp==0, b_age45_64_opt2    * age_cat_45_64,    0) +
      ifelse(rp==0, b_cat1_opt2       * category_1,       0) +
      ifelse(rp==0, b_cat2_opt2       * category_2,       0) +
      ifelse(rp==0, b_cat3_opt2       * category_3,       0) +
      ifelse(rp==0, b_cat4_opt2       * category_4,       0) +
      ifelse(rp==0, b_cat5_opt2       * category_5,       0) +
      ifelse(rp==0, b_ptsub_opt2      * ptsub,            0)
    
  ## SP‐only ASC for opt3
  asc_opt3_val = asc_opt3 +
      ifelse(rp==0, b_gender_opt3      * gender,           0) +
      ifelse(rp==0, b_cityCenter_opt3  * city_center,      0) +
      ifelse(rp==0, b_age18_29_opt3    * age_cat_18_29,    0) +
      ifelse(rp==0, b_age30_44_opt3    * age_cat_30_44,    0) +
      ifelse(rp==0, b_age45_64_opt3    * age_cat_45_64,    0) +
      ifelse(rp==0, b_cat1_opt3       * category_1,       0) +
      ifelse(rp==0, b_cat2_opt3       * category_2,       0) +
      ifelse(rp==0, b_cat3_opt3       * category_3,       0) +
      ifelse(rp==0, b_cat4_opt3       * category_4,       0) +
      ifelse(rp==0, b_cat5_opt3       * category_5,       0) +
      ifelse(rp==0, b_ptsub_opt3      * ptsub,            0)
  
  ## 2) Define utilities
  V = list(
    opt1   = asc_opt1_val + b_price * price_opt1,
    opt2   = asc_opt2_val + b_price * price_opt2,
    opt3   = asc_opt3_val + b_price * price_opt3 + b_opt3 * opt3,
    opt4   = 0
  )
  
  ## 3) RP model
  mnl_RP = list(
    alternatives = c(opt1=1, opt2=2, opt3=3, opt4=4),
    avail        = list(opt1=avail_opt1, opt2=avail_opt2, opt3=avail_opt3, opt4=1),
    choiceVar    = pref1,
    utilities    = lapply(V, function(x) mu_RP * x),
    rows         = (rp==1)
  )
  P[["RP"]] = apollo_mnl(mnl_RP, functionality)
  
  ## 4) SP model
  mnl_SP = list(
    alternatives = c(opt1=1, opt2=2, opt3=3, opt4=4),
    avail        = list(opt1=avail_opt1, opt2=avail_opt2, opt3=avail_opt3, opt4=1),
    choiceVar    = pref1,
    utilities    = lapply(V, function(x) mu_SP * x),
    rows         = (rp==0)
  )
  P[["SP"]] = apollo_mnl(mnl_SP, functionality)
  
  ## 5) Combine & return
  P = apollo_combineModels(P, apollo_inputs, functionality)
  P = apollo_panelProd(P, apollo_inputs, functionality)
  P = apollo_prepareProb(P, apollo_inputs, functionality)
  return(P)
}

# ################################################################# #
#### ESTIMATE MODEL                                              ####
# ################################################################# #

model = apollo_estimate(
  apollo_beta, 
  apollo_fixed, 
  apollo_probabilities, 
  apollo_inputs
)

# ################################################################# #
#### OUTPUT & POST‐PROCESSING                                   ####
# ################################################################# #

apollo_modelOutput(model)
apollo_saveOutput(model)

[stephanehess]

Hi

doesn't look like it. If you are happy to share the code and data with me, then I can have a look for you

Stephane

[stephanehess]

Matthias

What happened here is that when splitting the data across cores, then in some cores, there is only one row per person. You can avoid the error by replacing

P = apollo_panelProd(P, apollo_inputs, functionality)

with

if(length(unique(apollo_inputs$database[, apollo_inputs$apollo_control$indivID]))<nrow(apollo_inputs$database)) P = apollo_panelProd(P, apollo_inputs, functionality)

Stephane