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Missing model-fit parameters

Ask questions about the results reported after estimation. If the output includes errors, please include your model code if possible.
Post Reply
jayb
Posts: 4
Joined: 31 Jan 2023, 12:24

Missing model-fit parameters

Post by jayb »

Hi,

I've specified a Latent Class model for BWS Case 1 (no covariates). The model converges but is missing the Rho-sq parameters and the BIC. I'm using Apollo 0.2.9

See below for data structure, code and output:

Best wishes,

Jay


Data structure:

Code: Select all

glimpse(database)
Rows: 7,408
Columns: 77
$ uuid         <chr> "01nemk…
$ panel        <dbl> 13, 13,…
$ setno        <dbl> 1, 2, 3…
$ choice_best  <dbl> 4, 2, 3…
$ choice_worst <dbl> 3, 1, 1…
$ T1_1         <dbl> 0, 0, 0…
$ T1_2         <dbl> 0, 0, 0…
$ T1_3         <dbl> 1, 0, 0…
$ T1_4         <dbl> 0, 0, 0…
$ T2_1         <dbl> 0, 0, 0…
$ T2_2         <dbl> 0, 0, 0…
$ T2_3         <dbl> 0, 0, 0…
$ T2_4         <dbl> 0, 1, 0…
$ T3_1         <dbl> 0, 0, 0…
$ T3_2         <dbl> 0, 0, 0…
$ T3_3         <dbl> 0, 1, 0…
$ T3_4         <dbl> 0, 0, 0…
$ T4_1         <dbl> 1, 0, 0…
$ T4_2         <dbl> 0, 0, 0…
$ T4_3         <dbl> 0, 0, 0…
$ T4_4         <dbl> 0, 0, 0…
$ T5_1         <dbl> 0, 0, 0…
$ T5_2         <dbl> 0, 0, 0…
$ T5_3         <dbl> 0, 0, 0…
$ T5_4         <dbl> 0, 0, 0…
$ T6_1         <dbl> 0, 0, 0…
$ T6_2         <dbl> 0, 0, 0…
$ T6_3         <dbl> 0, 0, 0…
$ T6_4         <dbl> 0, 0, 1…
$ T7_1         <dbl> 0, 0, 0…
$ T7_2         <dbl> 0, 0, 1…
$ T7_3         <dbl> 0, 0, 0…
$ T7_4         <dbl> 0, 0, 0…
$ T8_1         <dbl> 0, 0, 0…
$ T8_2         <dbl> 0, 0, 0…
$ T8_3         <dbl> 0, 0, 0…
$ T8_4         <dbl> 0, 0, 0…
$ T9_1         <dbl> 0, 0, 0…
$ T9_2         <dbl> 0, 0, 0…
$ T9_3         <dbl> 0, 0, 1…
$ T9_4         <dbl> 0, 0, 0…
$ T10_1        <dbl> 0, 0, 0…
$ T10_2        <dbl> 0, 0, 0…
$ T10_3        <dbl> 0, 0, 0…
$ T10_4        <dbl> 0, 0, 0…
$ T11_1        <dbl> 0, 0, 0…
$ T11_2        <dbl> 0, 0, 0…
$ T11_3        <dbl> 0, 0, 0…
$ T11_4        <dbl> 0, 0, 0…
$ T12_1        <dbl> 0, 0, 1…
$ T12_2        <dbl> 0, 0, 0…
$ T12_3        <dbl> 0, 0, 0…
$ T12_4        <dbl> 0, 0, 0…
$ T13_1        <dbl> 0, 0, 0…
$ T13_2        <dbl> 0, 1, 0…
$ T13_3        <dbl> 0, 0, 0…
$ T13_4        <dbl> 0, 0, 0…
$ T14_1        <dbl> 0, 0, 0…
$ T14_2        <dbl> 0, 0, 0…
$ T14_3        <dbl> 0, 0, 0…
$ T14_4        <dbl> 1, 0, 0…
$ T15_1        <dbl> 0, 0, 0…
$ T15_2        <dbl> 1, 0, 0…
$ T15_3        <dbl> 0, 0, 0…
$ T15_4        <dbl> 0, 0, 0…
$ T16_1        <dbl> 0, 1, 0…
$ T16_2        <dbl> 0, 0, 0…
$ T16_3        <dbl> 0, 0, 0…
$ T16_4        <dbl> 0, 0, 0…
$ avail1B      <dbl> 1, 1, 1…
$ avail1W      <dbl> 1, 1, 1…
$ avail2B      <dbl> 1, 1, 1…
$ avail2W      <dbl> 1, 0, 1…
$ avail3B      <dbl> 0, 1, 1…
$ avail3W      <dbl> 1, 1, 0…
$ avail4B      <dbl> 1, 1, 1…
$ avail4W      <dbl> 1, 1, 1…
Code:

Code: Select all

# ################################################################# #
#### LOAD LIBRARY AND DEFINE CORE SETTINGS                       ####
# ################################################################# #

### Clear memory
rm(list = ls())

### Load Apollo and tidyverse library
library(apollo)
library(tidyverse)
library(readxl)
select <- dplyr::select

### Initialise code
apollo_initialise()

### Set core controls
apollo_control = list(
  modelName       = "TEST_NEW_BW_2LCMNL_no_covariates",
  modelDescr      = "LCMNL model on TEST BW choice data, no covariates in class allocation model",
  indivID         = "uuid",
  nCores          = 3,
  outputDirectory = "output",
  seed = 1234
)

# ################################################################# #
#### LOAD DATA AND APPLY ANY TRANSFORMATIONS                     ####
# ################################################################# #
database

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

### Vector of parameters, including any that are kept fixed in estimation
apollo_beta = c(set_names(rep(0, 42),
                          c(paste0("asc_alt", 1:4, "_",  "B"),
                            paste0("asc_alt", 1:4, "_",  "W"),
                            paste0("beta_T", rep(1:16, each = 2), "_", letters[1:2]),
                            paste0("delta_", letters[1:2]))),
                mu_worst = 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("asc_alt1_B", "asc_alt1_W", "beta_T2_a", "beta_T2_b", "delta_a")


# ################################################################# #
#### DEFINE LATENT CLASS COMPONENTS                              ####
# ################################################################# #

apollo_lcPars=function(apollo_beta, apollo_inputs){
  lcpars = list()

  lcpars[["beta_T1"]]  = list(beta_T1_a, beta_T1_b)
  lcpars[["beta_T2"]]  = list(beta_T2_a, beta_T2_b)
  lcpars[["beta_T3"]]  = list(beta_T3_a, beta_T3_b)
  lcpars[["beta_T4"]]  = list(beta_T4_a, beta_T4_b)
  lcpars[["beta_T5"]]  = list(beta_T5_a, beta_T5_b)
  lcpars[["beta_T6"]]  = list(beta_T6_a, beta_T6_b)
  lcpars[["beta_T7"]]  = list(beta_T7_a, beta_T7_b)
  lcpars[["beta_T8"]]  = list(beta_T8_a, beta_T8_b)
  lcpars[["beta_T9"]]  = list(beta_T9_a, beta_T9_b)
  lcpars[["beta_T10"]] = list(beta_T10_a, beta_T10_b)
  lcpars[["beta_T11"]] = list(beta_T11_a, beta_T11_b)
  lcpars[["beta_T12"]] = list(beta_T12_a, beta_T12_b)
  lcpars[["beta_T13"]] = list(beta_T13_a, beta_T13_b)
  lcpars[["beta_T14"]] = list(beta_T14_a, beta_T14_b)
  lcpars[["beta_T15"]] = list(beta_T15_a, beta_T15_b)
  lcpars[["beta_T16"]] = list(beta_T16_a, beta_T16_b)

  V=list()
  V[["class_a"]] = delta_a
  V[["class_b"]] = delta_b

  mnl_settings = list(
    alternatives = c(class_a = 1, class_b = 2),
    avail = 1,
    choiceVar = NA,
    V = V
  )

  lcpars[["pi_values"]] = apollo_mnl(mnl_settings, functionality = "raw")

  lcpars[["pi_values"]] = apollo_firstRow(lcpars[["pi_values"]], apollo_inputs)

  return(lcpars)
}

# ################################################################# #
#### 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()

  ### define class
  for(s in 1:2){

    ### Create tmp list of probabilities P_bw
    P_bw = list()

    ### List of utilities for the "best" choice
    V_best=list()
    V_best[["alt1B"]] = asc_alt1_B + beta_T1[[s]]*T1_1 + beta_T2[[s]]*T2_1 + beta_T3[[s]]*T3_1 + beta_T4[[s]]*T4_1 +
      beta_T5[[s]]*T5_1 + beta_T6[[s]]*T6_1 + beta_T7[[s]]*T7_1 +
      beta_T8[[s]]*T8_1 + beta_T9[[s]]*T9_1 + beta_T10[[s]]*T10_1 +
      beta_T11[[s]]*T11_1 + beta_T12[[s]]*T12_1 + beta_T13[[s]]*T13_1 +
      beta_T14[[s]]*T14_1 + beta_T15[[s]]*T15_1 + beta_T16[[s]]*T16_1
    V_best[["alt2B"]] = asc_alt2_B + beta_T1[[s]]*T1_2 + beta_T2[[s]]*T2_2 + beta_T3[[s]]*T3_2 + beta_T4[[s]]*T4_2 +
      beta_T5[[s]]*T5_2 + beta_T6[[s]]*T6_2 + beta_T7[[s]]*T7_2 +
      beta_T8[[s]]*T8_2 + beta_T9[[s]]*T9_2 + beta_T10[[s]]*T10_2 +
      beta_T11[[s]]*T11_2 + beta_T12[[s]]*T12_2 + beta_T13[[s]]*T13_2 +
      beta_T14[[s]]*T14_2 + beta_T15[[s]]*T15_2 + beta_T16[[s]]*T16_2
    V_best[["alt3B"]] = asc_alt3_B + beta_T1[[s]]*T1_3 + beta_T2[[s]]*T2_3 + beta_T3[[s]]*T3_3 + beta_T4[[s]]*T4_3 +
      beta_T5[[s]]*T5_3 + beta_T6[[s]]*T6_3 + beta_T7[[s]]*T7_3 +
      beta_T8[[s]]*T8_3 + beta_T9[[s]]*T9_3 + beta_T10[[s]]*T10_3 +
      beta_T11[[s]]*T11_3 + beta_T12[[s]]*T12_3 + beta_T13[[s]]*T13_3 +
      beta_T14[[s]]*T14_3 + beta_T15[[s]]*T15_3 + beta_T16[[s]]*T16_3
    V_best[["alt4B"]] = asc_alt4_B + beta_T1[[s]]*T1_4 + beta_T2[[s]]*T2_4 + beta_T3[[s]]*T3_4 + beta_T4[[s]]*T4_4 +
      beta_T5[[s]]*T5_4 + beta_T6[[s]]*T6_4 + beta_T7[[s]]*T7_4 +
      beta_T8[[s]]*T8_4 + beta_T9[[s]]*T9_4 + beta_T10[[s]]*T10_4 +
      beta_T11[[s]]*T11_4 + beta_T12[[s]]*T12_4 + beta_T13[[s]]*T13_4 +
      beta_T14[[s]]*T14_4 + beta_T15[[s]]*T15_4 + beta_T16[[s]]*T16_4

    ### Compute probabilities for "best" choice using MNL model
    mnl_settings_best = list(
      alternatives  = c(alt1B=1, alt2B=2, alt3B=3, alt4B=4),
      avail         = list(alt1B=avail1B, alt2B=avail2B, alt3B=avail3B, alt4B=avail4B),
      choiceVar     = choice_best,
      utilities     = V_best,
      componentName = paste0("Best_Class_", s)
    )
    P_bw[["choice_best"]] = apollo_mnl(mnl_settings_best, functionality)


    ### List of utilities for the "worse" choice
    V_worst = list()
    V_worst[["alt1W"]] = -mu_worst * (asc_alt1_W + beta_T1[[s]]*T1_1 + beta_T2[[s]]*T2_1 + beta_T3[[s]]*T3_1 + beta_T4[[s]]*T4_1 +
                                        beta_T5[[s]]*T5_1 + beta_T6[[s]]*T6_1 + beta_T7[[s]]*T7_1 +
                                        beta_T8[[s]]*T8_1 + beta_T9[[s]]*T9_1 + beta_T10[[s]]*T10_1 +
                                        beta_T11[[s]]*T11_1 + beta_T12[[s]]*T12_1 + beta_T13[[s]]*T13_1 )+
      beta_T14[[s]]*T14_1 + beta_T15[[s]]*T15_1 + beta_T16[[s]]*T16_1
    V_worst[["alt2W"]] = -mu_worst * (asc_alt2_W + beta_T1[[s]]*T1_2 + beta_T2[[s]]*T2_2 + beta_T3[[s]]*T3_2 + beta_T4[[s]]*T4_2 +
                                        beta_T5[[s]]*T5_2 + beta_T6[[s]]*T6_2 + beta_T7[[s]]*T7_2 +
                                        beta_T8[[s]]*T8_2 + beta_T9[[s]]*T9_2 + beta_T10[[s]]*T10_2 +
                                        beta_T11[[s]]*T11_2 + beta_T12[[s]]*T12_2 + beta_T13[[s]]*T13_2 +
                                        beta_T14[[s]]*T14_2 + beta_T15[[s]]*T15_2 + beta_T16[[s]]*T16_2)
    V_worst[["alt3W"]] = -mu_worst * (asc_alt3_W + beta_T1[[s]]*T1_3 + beta_T2[[s]]*T2_3 + beta_T3[[s]]*T3_3 + beta_T4[[s]]*T4_3 +
                                        beta_T5[[s]]*T5_3 + beta_T6[[s]]*T6_3 + beta_T7[[s]]*T7_3 +
                                        beta_T8[[s]]*T8_3 + beta_T9[[s]]*T9_3 + beta_T10[[s]]*T10_3 +
                                        beta_T11[[s]]*T11_3 + beta_T12[[s]]*T12_3 + beta_T13[[s]]*T13_3 +
                                        beta_T14[[s]]*T14_3 + beta_T15[[s]]*T15_3 + beta_T16[[s]]*T16_3)
    V_worst[["alt4W"]] = -mu_worst * (asc_alt4_W + beta_T1[[s]]*T1_4 + beta_T2[[s]]*T2_4 + beta_T3[[s]]*T3_4 + beta_T4[[s]]*T4_4 +
                                        beta_T5[[s]]*T5_4 + beta_T6[[s]]*T6_4 + beta_T7[[s]]*T7_4 +
                                        beta_T8[[s]]*T8_4 + beta_T9[[s]]*T9_4 + beta_T10[[s]]*T10_4 +
                                        beta_T11[[s]]*T11_4 + beta_T12[[s]]*T12_4 + beta_T13[[s]]*T13_4 +
                                        beta_T14[[s]]*T14_4 + beta_T15[[s]]*T15_4 + beta_T16[[s]]*T16_4)

    ### Compute probabilities for "worst" choice using MNL model
    mnl_settings_worst = list(
      alternatives  = c(alt1W=1, alt2W=2, alt3W=3, alt4W=4),
      avail         = list(alt1W=avail1W, alt2W=avail2W, alt3W=avail3W, alt4W=avail4W),
      choiceVar     = choice_worst,
      utilities     = V_worst,
      componentName = paste0("Worst_Class_", s)
    )
    P_bw[["choice_worst"]] = apollo_mnl(mnl_settings_worst, functionality)

    ### Combined model
    P[[paste0("Class_", s)]] = apollo_combineModels(P_bw, apollo_inputs, functionality)$model

    ### Take product across observation for same individual
    P[[paste0("Class_", s)]] = apollo_panelProd(P[[paste0("Class_", s)]], apollo_inputs, functionality)

  }

  ### Compute latent class model probabilities
  lc_settings   = list(inClassProb = P, classProb = pi_values)
  P[["model"]] = apollo_lc(lc_settings, apollo_inputs, functionality)

  ### Prepare and return outputs of function
  P = apollo_prepareProb(P, apollo_inputs, functionality)
  return(P)
}

# ################################################################# #
#### MODEL ESTIMATION                                            ####
# ################################################################# #

### Optional starting values search
# apollo_beta=apollo_searchStart(apollo_beta, apollo_fixed,apollo_probabilities, apollo_inputs)

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

# ################################################################# #
#### MODEL OUTPUTS                                               ####
# ################################################################# #

# ----------------------------------------------------------------- #
#---- FORMATTED OUTPUT (TO SCREEN)                               ----
# ----------------------------------------------------------------- #

apollo_modelOutput(model, modelOutput_settings = list(printPVal=1))

# ----------------------------------------------------------------- #
#---- FORMATTED OUTPUT (TO FILE, using model name)               ----
# ----------------------------------------------------------------- #

apollo_saveOutput(model, saveOutput_settings = list(printPVal=1))
Output:

Code: Select all

Preparing user-defined functions.

Testing likelihood function...

Overview of choices for MNL model component Best_Class_1:
                                   alt1B   alt2B   alt3B   alt4B
Times available                  6762.00 6842.00 6875.00 6891.00
Times chosen                     2099.00 1870.00 1712.00 1727.00
Percentage chosen overall          28.33   25.24   23.11   23.31
Percentage chosen when available   31.04   27.33   24.90   25.06


Overview of choices for MNL model component Worst_Class_1:
                                   alt1W   alt2W   alt3W   alt4W
Times available                  5859.00 6053.00 6213.00 6293.00
Times chosen                     1868.00 1715.00 1881.00 1944.00
Percentage chosen overall          25.22   23.15   25.39   26.24
Percentage chosen when available   31.88   28.33   30.28   30.89


Overview of choices for MNL model component Best_Class_2:
                                   alt1B   alt2B   alt3B   alt4B
Times available                  6762.00 6842.00 6875.00 6891.00
Times chosen                     2099.00 1870.00 1712.00 1727.00
Percentage chosen overall          28.33   25.24   23.11   23.31
Percentage chosen when available   31.04   27.33   24.90   25.06


Overview of choices for MNL model component Worst_Class_2:
                                   alt1W   alt2W   alt3W   alt4W
Times available                  5859.00 6053.00 6213.00 6293.00
Times chosen                     1868.00 1715.00 1881.00 1944.00
Percentage chosen overall          25.22   23.15   25.39   26.24
Percentage chosen when available   31.88   28.33   30.28   30.89


Summary of class allocation for model component :
         Mean prob.
Class_1      0.5000
Class_2      0.5000

Pre-processing likelihood function...
Creating cluster...
Preparing workers for multithreading...
INFORMATION: Apollo was not able to compute analytical gradients for your model.
  This could be because you are using model components for which
  analytical gradients are not yet implemented, or because you coded
  your own model functions. If however you only used apollo_mnl,
  apollo_fmnl, apollo_normalDensity, apollo_ol or apollo_op then there
  could be another issue. You might want to ask for help in the Apollo
  forum (http://www.apollochoicemodelling.com/forum) on how to solve
  this issue. If you do, please post your code and data (if not
  confidential). 

  Current process will resume in 5 seconds unless interrupted by the
  user.....

Analytical gradients could not be calculated for all components, numerical gradients will be used.

Testing influence of parameters......................................
Starting main estimation
Initial function value: -18388.63 
Initial gradient value:
 asc_alt2_B  asc_alt3_B  asc_alt4_B  asc_alt2_W  asc_alt3_W  asc_alt4_W   beta_T1_a   beta_T1_b   beta_T3_a   beta_T3_b   beta_T4_a   beta_T4_b 
  18.166666 -150.833333 -141.166667  119.833334    7.166665  -29.166666  -64.916665  -64.916665   86.666667   86.666667   43.249998   43.249998 
  beta_T5_a   beta_T5_b   beta_T6_a   beta_T6_b   beta_T7_a   beta_T7_b   beta_T8_a   beta_T8_b   beta_T9_a   beta_T9_b  beta_T10_a  beta_T10_b 
  20.541669   20.541669  -43.916669  -43.916669    3.583333    3.583333    1.083332    1.083332  -53.666667  -53.666667 -249.875000 -249.875000 
 beta_T11_a  beta_T11_b  beta_T12_a  beta_T12_b  beta_T13_a  beta_T13_b  beta_T14_a  beta_T14_b  beta_T15_a  beta_T15_b  beta_T16_a  beta_T16_b 
 165.833335  165.833335  -60.666665  -60.666665   75.833334   75.833334  252.541668  252.541668 -116.541665 -116.541665  163.541667  163.541667 
    delta_b    mu_worst 
   0.000000    0.000000 
initial  value 18388.626081 
iter   2 value 17859.727383
iter   3 value 17769.948065
iter   4 value 17703.175543
iter   5 value 17687.815877
iter   6 value 17669.104311
iter   7 value 17659.153141
iter   8 value 17643.408518
iter   9 value 17635.183456
iter  10 value 17633.485223
iter  11 value 17632.984056
iter  12 value 17632.875485
iter  13 value 17630.526578
iter  14 value 17622.949525
iter  15 value 17620.320690
iter  16 value 17616.513862
iter  17 value 17615.470293
iter  18 value 17614.170020
iter  19 value 17414.614631
iter  20 value 17311.599819
iter  21 value 17286.429247
iter  22 value 17273.890577
iter  23 value 17262.846313
iter  24 value 17246.459498
iter  25 value 17241.861015
iter  26 value 17241.201433
iter  27 value 17240.293042
iter  28 value 17239.090533
iter  29 value 17238.796939
iter  30 value 17238.426550
iter  31 value 17237.286312
iter  32 value 17229.261875
iter  33 value 17220.134966
iter  34 value 17218.459536
iter  35 value 17217.036898
iter  36 value 17214.968085
iter  37 value 17214.739770
iter  38 value 17214.637630
iter  39 value 17214.468842
iter  40 value 17213.959256
iter  41 value 17213.745646
iter  42 value 17213.431499
iter  43 value 17212.475123
iter  44 value 17210.658732
iter  45 value 17210.616148
iter  46 value 17210.597836
iter  47 value 17210.576266
iter  48 value 17210.255316
iter  49 value 17209.697710
iter  50 value 17209.685611
iter  51 value 17209.684428
iter  52 value 17209.670543
iter  53 value 17209.656133
iter  54 value 17209.648083
iter  55 value 17209.646848
iter  56 value 17209.636539
iter  57 value 17209.601246
iter  58 value 17209.587566
iter  58 value 17209.587349
iter  59 value 17209.573010
iter  60 value 17209.569862
iter  61 value 17209.568405
iter  62 value 17209.567206
iter  63 value 17209.566941
iter  64 value 17209.566321
iter  64 value 17209.566223
iter  65 value 17209.565063
iter  66 value 17209.563995
iter  66 value 17209.563924
iter  66 value 17209.563732
final  value 17209.563732 
converged
Additional convergence test using scaled estimation. Parameters will be scaled by their current estimates and additional iterations will
  be performed.
initial  value 17209.563732 
iter   2 value 17209.563378
iter   2 value 17209.563134
iter   2 value 17209.563134
final  value 17209.563134 
converged

Estimated parameters with approximate standard errors from BHHH matrix:
              Estimate     BHHH se BHH t-ratio
asc_alt1_B     0.00000          NA          NA
asc_alt2_B    -0.14673     0.03335     -4.4000
asc_alt3_B    -0.23823     0.03336     -7.1418
asc_alt4_B    -0.23358     0.03268     -7.1477
asc_alt1_W     0.00000          NA          NA
asc_alt2_W    -0.03823     0.03114     -1.2277
asc_alt3_W    -0.09717     0.03022     -3.2154
asc_alt4_W    -0.12210     0.03050     -4.0029
beta_T1_a      0.67694     0.06535     10.3585
beta_T1_b     -0.48531     0.06396     -7.5872
beta_T2_a      0.00000          NA          NA
beta_T2_b      0.00000          NA          NA
beta_T3_a      0.14142     0.06132      2.3065
beta_T3_b      1.03926     0.07392     14.0589
beta_T4_a      0.91417     0.07102     12.8723
beta_T4_b     -0.10537     0.06679     -1.5776
beta_T5_a      0.14113     0.06685      2.1112
beta_T5_b      0.58500     0.07335      7.9750
beta_T6_a      0.01218     0.05670      0.2147
beta_T6_b      0.33160     0.06575      5.0432
beta_T7_a      0.78727     0.07129     11.0435
beta_T7_b     -0.19973     0.06471     -3.0866
beta_T8_a      0.21817     0.06478      3.3676
beta_T8_b      0.39217     0.07250      5.4090
beta_T9_a     -0.40291     0.06496     -6.2020
beta_T9_b      0.82334     0.07387     11.1458
beta_T10_a    -0.54886     0.06919     -7.9329
beta_T10_b    -0.23299     0.06404     -3.6381
beta_T11_a     1.56578     0.07989     19.5988
beta_T11_b     0.06541     0.06715      0.9741
beta_T12_a    -0.13454     0.05856     -2.2976
beta_T12_b     0.38004     0.06179      6.1508
beta_T13_a     0.73050     0.06132     11.9122
beta_T13_b     0.27721     0.06861      4.0406
beta_T14_a     1.27846     0.06241     20.4861
beta_T14_b     0.84128     0.05984     14.0594
beta_T15_a     0.23505     0.06473      3.6314
beta_T15_b    -0.42243     0.06551     -6.4485
beta_T16_a     0.88968     0.06103     14.5772
beta_T16_b     0.65635     0.06676      9.8318
delta_a        0.00000          NA          NA
delta_b       -0.14146     0.09678     -1.4617
mu_worst       1.18995     0.05529     21.5233

Final LL: -17209.5631

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...
Computing covariance matrix using numerical methods (numDeriv).
 0%....25%....50%....75%....100%
Negative definite Hessian with maximum eigenvalue: -17.427067
> apollo_modelOutput(model, modelOutput_settings = list(printPVal=1))
Model run by jburns10 using Apollo 0.2.9 on R 4.2.2 for Windows.
www.ApolloChoiceModelling.com

Model name                                  : TEST_NEW_BW_2LCMNL_no_covariates
Model description                           : LCMNL model on TEST BW choice data, no covariates in class allocation model
Model run at                                : 2024-03-26 10:19:42
Estimation method                           : bfgs
Model diagnosis                             : successful convergence
Optimisation diagnosis                      : Maximum found
     hessian properties                     : Negative definitive
     maximum eigenvalue                     : -17.427067
Number of individuals                       : 926
Number of rows in database                  : 7408
Number of modelled outcomes                 : 0

Number of cores used                        :  3 
Model without mixing

LL(start)                                   : -18388.63
LL (whole model) at equal shares, LL(0)     : -18388.63
LL (whole model) at observed shares, LL(C)  : -18348.42
LL(final, whole model)                      : -17209.56
Rho-squared vs equal shares                  :  Not applicable 
Adj.Rho-squared vs equal shares              :  Not applicable 
Rho-squared vs observed shares               :  Not applicable 
Adj.Rho-squared vs observed shares           :  Not applicable 
AIC                                         :  34495.13 
BIC                                         :  NA 

LL(0,Class_1)                    : -18388.63
LL(final,Class_1)                : -18450.09
LL(0,Class_2)                    : -18388.63
LL(final,Class_2)                : -18566.13

Estimated parameters                        : 38
Time taken (hh:mm:ss)                       :  00:05:20.69 
     pre-estimation                         :  00:00:32.37 
     estimation                             :  00:02:30.76 
          initial estimation                :  00:02:22.59 
          estimation after rescaling        :  00:00:8.17 
     post-estimation                        :  00:02:17.56 
Iterations                                  :  73  
     initial estimation                     :  69 
     estimation after rescaling             :  4 

Unconstrained optimisation.

Estimates:
              Estimate        s.e.   t.rat.(0)  p(1-sided)    Rob.s.e. Rob.t.rat.(0)  p(1-sided)
asc_alt1_B     0.00000          NA          NA          NA          NA            NA          NA
asc_alt2_B    -0.14673     0.03409     -4.3036   8.402e-06     0.03600       -4.0760   2.291e-05
asc_alt3_B    -0.23823     0.03479     -6.8479   3.748e-12     0.03719       -6.4053   7.504e-11
asc_alt4_B    -0.23358     0.03475     -6.7218   8.975e-12     0.03752       -6.2258   2.395e-10
asc_alt1_W     0.00000          NA          NA          NA          NA            NA          NA
asc_alt2_W    -0.03823     0.03305     -1.1567    0.123698     0.03659       -1.0448    0.148067
asc_alt3_W    -0.09717     0.03250     -2.9904    0.001393     0.03587       -2.7087    0.003377
asc_alt4_W    -0.12210     0.03242     -3.7662   8.288e-05     0.03576       -3.4147  3.1922e-04
beta_T1_a      0.67694     0.09395      7.2055   2.891e-13     0.17629        3.8400   6.151e-05
beta_T1_b     -0.48531     0.08402     -5.7758   3.829e-09     0.12692       -3.8236   6.576e-05
beta_T2_a      0.00000          NA          NA          NA          NA            NA          NA
beta_T2_b      0.00000          NA          NA          NA          NA            NA          NA
beta_T3_a      0.14142     0.06832      2.0700    0.019228     0.08216        1.7213    0.042599
beta_T3_b      1.03926     0.10422      9.9722    0.000000     0.19870        5.2303   8.461e-08
beta_T4_a      0.91417     0.08991     10.1677    0.000000     0.15460        5.9132   1.677e-09
beta_T4_b     -0.10537     0.07539     -1.3977    0.081107     0.10304       -1.0227    0.153228
beta_T5_a      0.14113     0.06729      2.0973    0.017985     0.07779        1.8143    0.034819
beta_T5_b      0.58500     0.07810      7.4901   3.442e-14     0.09874        5.9247   1.564e-09
beta_T6_a      0.01218     0.06741      0.1806    0.428328     0.08556        0.1423    0.443416
beta_T6_b      0.33160     0.07814      4.2436   1.100e-05     0.10898        3.0427    0.001172
beta_T7_a      0.78727     0.08076      9.7480    0.000000     0.12254        6.4244   6.618e-11
beta_T7_b     -0.19973     0.07650     -2.6108    0.004516     0.11521       -1.7337    0.041489
beta_T8_a      0.21817     0.06739      3.2374  6.0316e-04     0.07570        2.8822    0.001975
beta_T8_b      0.39217     0.07705      5.0900   1.791e-07     0.09487        4.1338   1.784e-05
beta_T9_a     -0.40291     0.07839     -5.1396   1.377e-07     0.11819       -3.4090  3.2598e-04
beta_T9_b      0.82334     0.10013      8.2228   1.110e-16     0.19083        4.3146   7.993e-06
beta_T10_a    -0.54886     0.07514     -7.3042   1.394e-13     0.09265       -5.9241   1.570e-09
beta_T10_b    -0.23299     0.07231     -3.2222  6.3613e-04     0.08504       -2.7398    0.003074
beta_T11_a     1.56578     0.09997     15.6630    0.000000     0.17744        8.8242    0.000000
beta_T11_b     0.06541     0.08347      0.7836    0.216629     0.13783        0.4746    0.317548
beta_T12_a    -0.13454     0.07045     -1.9096    0.028091     0.09360       -1.4373    0.075313
beta_T12_b     0.38004     0.08909      4.2658   9.959e-06     0.16209        2.3447    0.009521
beta_T13_a     0.73050     0.08129      8.9866    0.000000     0.13176        5.5441   1.478e-08
beta_T13_b     0.27721     0.07567      3.6634  1.2446e-04     0.09536        2.9070    0.001825
beta_T14_a     1.27846     0.08385     15.2463    0.000000     0.13992        9.1368    0.000000
beta_T14_b     0.84128     0.08111     10.3722    0.000000     0.12207        6.8917   2.756e-12
beta_T15_a     0.23505     0.06746      3.4841  2.4690e-04     0.08659        2.7147    0.003317
beta_T15_b    -0.42243     0.07759     -5.4445   2.598e-08     0.11209       -3.7687   8.206e-05
beta_T16_a     0.88968     0.07032     12.6520    0.000000     0.09195        9.6760    0.000000
beta_T16_b     0.65635     0.07297      8.9947    0.000000     0.08426        7.7891   3.331e-15
delta_a        0.00000          NA          NA          NA          NA            NA          NA
delta_b       -0.14146     0.15163     -0.9330    0.175421     0.32963       -0.4292    0.333904
mu_worst       1.18995     0.06322     18.8217    0.000000     0.08032       14.8149    0.000000
Top
stephanehess
Site Admin
Posts: 998
Joined: 24 Apr 2020, 16:29

Re: Missing model-fit parameters

Post by stephanehess »

Hi

please update to the newest version of Apollo and see if that fixes it

Thanks
--------------------------------
Stephane Hess
www.stephanehess.me.uk
Top
jayb
Posts: 4
Joined: 31 Jan 2023, 12:24

Re: Missing model-fit parameters

Post by jayb »

Hi Stephane,

Thanks for the reply.

I updated to 0.3.1, the same problem occurs. Here is the formatted output, using same data and code as before:

Code: Select all

Preparing user-defined functions.

Testing likelihood function...

Overview of choices for MNL model component Best_Class_1:
                                   alt1B   alt2B   alt3B   alt4B
Times available                  6762.00 6842.00 6875.00 6891.00
Times chosen                     2099.00 1870.00 1712.00 1727.00
Percentage chosen overall          28.33   25.24   23.11   23.31
Percentage chosen when available   31.04   27.33   24.90   25.06


Overview of choices for MNL model component Worst_Class_1:
                                   alt1W   alt2W   alt3W   alt4W
Times available                  5859.00 6053.00 6213.00 6293.00
Times chosen                     1868.00 1715.00 1881.00 1944.00
Percentage chosen overall          25.22   23.15   25.39   26.24
Percentage chosen when available   31.88   28.33   30.28   30.89


Overview of choices for MNL model component Best_Class_2:
                                   alt1B   alt2B   alt3B   alt4B
Times available                  6762.00 6842.00 6875.00 6891.00
Times chosen                     2099.00 1870.00 1712.00 1727.00
Percentage chosen overall          28.33   25.24   23.11   23.31
Percentage chosen when available   31.04   27.33   24.90   25.06


Overview of choices for MNL model component Worst_Class_2:
                                   alt1W   alt2W   alt3W   alt4W
Times available                  5859.00 6053.00 6213.00 6293.00
Times chosen                     1868.00 1715.00 1881.00 1944.00
Percentage chosen overall          25.22   23.15   25.39   26.24
Percentage chosen when available   31.88   28.33   30.28   30.89


Summary of class allocation for model component :
         Mean prob.
Class_1      0.5000
Class_2      0.5000

Pre-processing likelihood function...
Creating cluster...
Preparing workers for multithreading...
INFORMATION: Apollo was not able to compute analytical gradients for your model. This could be because you are using model
  components for which analytical gradients are not yet implemented, or because you coded your own model
  functions. If however you only used apollo_mnl, apollo_fmnl, apollo_normalDensity, apollo_ol or apollo_op,
  then there could be another issue. You might want to ask for help in the Apollo forum
  (http://www.apollochoicemodelling.com/forum) on how to solve this issue. If you do, please post your code and
  data (if not confidential). 

  Current process will resume in 5 seconds unless interrupted by the user.....

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: 
 output/TEST_NEW_BW_2LCMNL_no_covariates_iterations.csv
    it    nf     F            RELDF    PRELDF    RELDX    MODEL stppar
     0     1 1.838862608e+04
     1     4 1.795709324e+04 2.347e-02 1.449e-02 1.00e+00   G   -3.02e-06
     2     5 1.768922806e+04 1.492e-02 9.298e-03 2.68e-01   G   -3.02e-06
     3     7 1.764658029e+04 2.411e-03 2.237e-03 3.15e-02   S   1.14e+00
     4     8 1.763772514e+04 5.018e-04 1.174e-03 6.05e-02   S   8.40e-02
     5    11 1.762673577e+04 6.231e-04 2.484e-04 7.59e-03   G   9.06e+00
     6    12 1.744605845e+04 1.025e-02 2.424e-03 4.70e-02   G   5.65e-01
     7    13 1.731322895e+04 7.614e-03 5.562e-03 1.81e-01   G   0.00e+00
     8    14 1.724456019e+04 3.966e-03 2.688e-03 1.34e-01   G   0.00e+00
     9    15 1.721839085e+04 1.518e-03 1.274e-03 9.66e-02   S   0.00e+00
    10    16 1.721409176e+04 2.497e-04 1.853e-04 4.47e-02   S   0.00e+00
    11    17 1.721190171e+04 1.272e-04 9.164e-05 2.93e-02   S   0.00e+00
    12    18 1.721068016e+04 7.097e-05 5.190e-05 2.09e-02   S   0.00e+00
    13    19 1.721010059e+04 3.367e-05 2.297e-05 1.45e-02   S   0.00e+00
    14    20 1.720975904e+04 1.985e-05 1.461e-05 1.07e-02   S   0.00e+00
    15    21 1.720962843e+04 7.589e-06 5.559e-06 4.18e-03   S   0.00e+00
    16    22 1.720958040e+04 2.791e-06 2.171e-06 2.00e-03   S   0.00e+00
    17    23 1.720956712e+04 7.717e-07 5.784e-07 1.50e-03   S   0.00e+00
    18    24 1.720956295e+04 2.424e-07 1.937e-07 1.05e-03   S   0.00e+00
    19    25 1.720956211e+04 4.856e-08 3.859e-08 5.45e-04   S   0.00e+00
    20    26 1.720956194e+04 9.849e-09 8.082e-09 1.75e-04   S   0.00e+00
    21    27 1.720956191e+04 2.099e-09 1.771e-09 1.00e-04   S   0.00e+00
    22    28 1.720956190e+04 6.470e-10 5.042e-10 6.26e-05   S   0.00e+00
    23    29 1.720956189e+04 2.055e-10 1.607e-10 2.99e-05   S   0.00e+00
    24    30 1.720956189e+04 4.411e-11 3.590e-11 1.16e-05   S   0.00e+00

***** 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: 
 output/TEST_NEW_BW_2LCMNL_no_covariates_iterations.csv
    it    nf     F            RELDF    PRELDF    RELDX    MODEL stppar
     0     1 1.720956189e+04
     1     2 1.720956189e+04 6.117e-12 4.332e-12 3.38e-05   G   0.00e+00

***** Relative function convergence *****

Estimated parameters with approximate standard errors from BHHH matrix:
              Estimate     BHHH se BHH t-ratio (0)
asc_alt1_B     0.00000          NA              NA
asc_alt2_B    -0.14622     0.03337         -4.3824
asc_alt3_B    -0.23783     0.03338         -7.1258
asc_alt4_B    -0.23348     0.03270         -7.1409
asc_alt1_W     0.00000          NA              NA
asc_alt2_W    -0.03852     0.03118         -1.2353
asc_alt3_W    -0.09705     0.03026         -3.2072
asc_alt4_W    -0.12209     0.03054         -3.9972
beta_T1_a      0.67893     0.06551         10.3639
beta_T1_b     -0.48541     0.06398         -7.5871
beta_T2_a      0.00000          NA              NA
beta_T2_b      0.00000          NA              NA
beta_T3_a      0.14160     0.06143          2.3050
beta_T3_b      1.03743     0.07389         14.0407
beta_T4_a      0.91592     0.07119         12.8650
beta_T4_b     -0.10610     0.06681         -1.5880
beta_T5_a      0.14092     0.06699          2.1036
beta_T5_b      0.58425     0.07335          7.9655
beta_T6_a      0.01198     0.05681          0.2109
beta_T6_b      0.33064     0.06575          5.0289
beta_T7_a      0.78863     0.07144         11.0393
beta_T7_b     -0.19932     0.06472         -3.0796
beta_T8_a      0.21927     0.06492          3.3775
beta_T8_b      0.39195     0.07249          5.4070
beta_T9_a     -0.40424     0.06511         -6.2088
beta_T9_b      0.82166     0.07386         11.1243
beta_T10_a    -0.54927     0.06931         -7.9242
beta_T10_b    -0.23358     0.06405         -3.6466
beta_T11_a     1.56755     0.08007         19.5781
beta_T11_b     0.06597     0.06716          0.9824
beta_T12_a    -0.13424     0.05867         -2.2883
beta_T12_b     0.37898     0.06178          6.1344
beta_T13_a     0.73180     0.06144         11.9108
beta_T13_b     0.27732     0.06860          4.0424
beta_T14_a     1.27958     0.06252         20.4656
beta_T14_b     0.84094     0.05983         14.0544
beta_T15_a     0.23619     0.06485          3.6420
beta_T15_b    -0.42215     0.06551         -6.4437
beta_T16_a     0.89034     0.06113         14.5650
beta_T16_b     0.65591     0.06676          9.8255
delta_a        0.00000          NA              NA
delta_b       -0.13809     0.09684         -1.4259
mu_worst       1.18901     0.05529         21.5050

Final LL: -17209.5619

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...
Computing covariance matrix using numerical methods (numDeriv).
 0%....25%....50%....75%....100%
Negative definite Hessian with maximum eigenvalue: -17.435149
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, modelOutput_settings = list(printPVal=1))
Model run by jburns10 using Apollo 0.3.1 on R 4.2.2 for Windows.
Please acknowledge the use of Apollo by citing Hess & Palma (2019)
  DOI 10.1016/j.jocm.2019.100170
  www.ApolloChoiceModelling.com

Model name                                  : TEST_NEW_BW_2LCMNL_no_covariates
Model description                           : LCMNL model on TEST BW choice data, no covariates in class allocation model
Model run at                                : 2024-04-23 11:51:23
Estimation method                           : bgw
Model diagnosis                             : Relative function convergence
Optimisation diagnosis                      : Maximum found
     hessian properties                     : Negative definite
     maximum eigenvalue                     : -17.435149
     reciprocal of condition number         : 0.00780576
Number of individuals                       : 926
Number of rows in database                  : 7408
Number of modelled outcomes                 : 0

Number of cores used                        :  3 
Model without mixing

LL(start)                                   : -18388.63
LL (whole model) at equal shares, LL(0)     : -18388.63
LL (whole model) at observed shares, LL(C)  : -18348.42
LL(final, whole model)                      : -17209.56
Rho-squared vs equal shares                  :  Not applicable 
Adj.Rho-squared vs equal shares              :  Not applicable 
Rho-squared vs observed shares               :  Not applicable 
Adj.Rho-squared vs observed shares           :  Not applicable 
AIC                                         :  34495.12 
BIC                                         :  NA 

LL(0,Class_1)                    : -18388.63
LL(final,Class_1)                : -18452.88
LL(0,Class_2)                    : -18388.63
LL(final,Class_2)                : -18562.94

Estimated parameters                        : 38
Time taken (hh:mm:ss)                       :  00:03:16.68 
     pre-estimation                         :  00:00:28.98 
     estimation                             :  00:00:29.24 
          initial estimation                :  00:00:26.01 
          estimation after rescaling        :  00:00:3.22 
     post-estimation                        :  00:02:18.46 
Iterations                                  :  25  
     initial estimation                     :  24 
     estimation after rescaling             :  1 

Unconstrained optimisation.

Estimates:
              Estimate        s.e.   t.rat.(0)  p(1-sided)    Rob.s.e. Rob.t.rat.(0)  p(1-sided)
asc_alt1_B     0.00000          NA          NA          NA          NA            NA          NA
asc_alt2_B    -0.14622     0.03409     -4.2888   8.983e-06     0.03600       -4.0619   2.434e-05
asc_alt3_B    -0.23783     0.03479     -6.8361   4.069e-12     0.03720       -6.3940   8.081e-11
asc_alt4_B    -0.23348     0.03475     -6.7184   9.189e-12     0.03752       -6.2221   2.453e-10
asc_alt1_W     0.00000          NA          NA          NA          NA            NA          NA
asc_alt2_W    -0.03852     0.03308     -1.1646    0.122085     0.03662       -1.0520    0.146398
asc_alt3_W    -0.09705     0.03252     -2.9845    0.001420     0.03590       -2.7036    0.003430
asc_alt4_W    -0.12209     0.03244     -3.7631   8.392e-05     0.03578       -3.4120  3.2240e-04
beta_T1_a      0.67893     0.09410      7.2153   2.690e-13     0.17645        3.8477   5.962e-05
beta_T1_b     -0.48541     0.08401     -5.7777   3.786e-09     0.12699       -3.8225   6.605e-05
beta_T2_a      0.00000          NA          NA          NA          NA            NA          NA
beta_T2_b      0.00000          NA          NA          NA          NA            NA          NA
beta_T3_a      0.14160     0.06841      2.0699    0.019232     0.08221        1.7223    0.042506
beta_T3_b      1.03743     0.10405      9.9709    0.000000     0.19819        5.2346   8.268e-08
beta_T4_a      0.91592     0.09006     10.1706    0.000000     0.15476        5.9182   1.627e-09
beta_T4_b     -0.10610     0.07540     -1.4071    0.079694     0.10313       -1.0287    0.151800
beta_T5_a      0.14092     0.06740      2.0909    0.018269     0.07792        1.8084    0.035270
beta_T5_b      0.58425     0.07802      7.4882   3.497e-14     0.09852        5.9305   1.510e-09
beta_T6_a      0.01198     0.06752      0.1775    0.429559     0.08573        0.1398    0.444415
beta_T6_b      0.33064     0.07806      4.2355   1.140e-05     0.10875        3.0403    0.001182
beta_T7_a      0.78863     0.08087      9.7523    0.000000     0.12262        6.4317   6.308e-11
beta_T7_b     -0.19932     0.07647     -2.6065    0.004573     0.11515       -1.7311    0.041720
beta_T8_a      0.21927     0.06748      3.2495  5.7811e-04     0.07576        2.8942    0.001901
beta_T8_b      0.39195     0.07697      5.0922   1.770e-07     0.09470        4.1389   1.745e-05
beta_T9_a     -0.40424     0.07852     -5.1483   1.315e-07     0.11831       -3.4167  3.1698e-04
beta_T9_b      0.82166     0.09998      8.2180   1.110e-16     0.19034        4.3167   7.918e-06
beta_T10_a    -0.54927     0.07522     -7.3022   1.416e-13     0.09265       -5.9282   1.532e-09
beta_T10_b    -0.23358     0.07228     -3.2318  6.1503e-04     0.08498       -2.7486    0.002993
beta_T11_a     1.56755     0.10007     15.6652    0.000000     0.17742        8.8351    0.000000
beta_T11_b     0.06597     0.08345      0.7906    0.214587     0.13780        0.4788    0.316052
beta_T12_a    -0.13424     0.07055     -1.9028    0.028534     0.09374       -1.4320    0.076070
beta_T12_b     0.37898     0.08894      4.2611   1.017e-05     0.16156        2.3458    0.009494
beta_T13_a     0.73180     0.08139      8.9911    0.000000     0.13186        5.5496   1.432e-08
beta_T13_b     0.27732     0.07563      3.6669  1.2278e-04     0.09528        2.9105    0.001804
beta_T14_a     1.27958     0.08392     15.2480    0.000000     0.13986        9.1491    0.000000
beta_T14_b     0.84094     0.08100     10.3814    0.000000     0.12175        6.9070   2.475e-12
beta_T15_a     0.23619     0.06756      3.4962  2.3596e-04     0.08670        2.7241    0.003223
beta_T15_b    -0.42215     0.07754     -5.4442   2.602e-08     0.11199       -3.7696   8.176e-05
beta_T16_a     0.89034     0.07039     12.6479    0.000000     0.09202        9.6757    0.000000
beta_T16_b     0.65591     0.07290      8.9968    0.000000     0.08415        7.7944   3.220e-15
delta_a        0.00000          NA          NA          NA          NA            NA          NA
delta_b       -0.13809     0.15159     -0.9110    0.181151     0.32928       -0.4194    0.337471
mu_worst       1.18901     0.06314     18.8310    0.000000     0.08018       14.8300    0.000000
Last edited by jayb on 23 Apr 2024, 11:59, edited 2 times in total.
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stephanehess
Site Admin
Posts: 998
Joined: 24 Apr 2020, 16:29

Re: Missing model-fit parameters

Post by stephanehess »

Hi

if you share your code and data with me outside the forum, I'll have a look

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