I am using the Latent class with continuous random parameters. The model failed saying "Estimation failed. No covariance matrix to compute" and the results come out with NaNs.
Could you please kindly let me know what should I do to solve this problem? Many thanks in advance.
Code for my model:
Code: Select all
apollo_control = list(
modelName = "mangrove restoration CE",
modelDescr = "Latent Class method on mangrove restoration CE data",
indivID = "ID",
mixing = TRUE ,
nCores = 6,
outputDirectory = "/Users/pw123456ifi/Desktop/Apollo"
)
# ################################################################# #
#### LOAD DATA AND APPLY ANY TRANSFORMATIONS ####
# ################################################################# #
### Loading data from package
### if data is to be loaded from a file (e.g. called data.csv),
### the code would be: database = read.csv("data.csv",header=TRUE)
### for data dictionary, use ?apollo_swissRouteChoiceData
# ################################################################# #
#### DEFINE MODEL PARAMETERS ####
# ################################################################# #
### Vector of parameters, including any that are kept fixed in estimation
apollo_beta = c(asc = -10,
sigma_asc = 0,
beta_area_a = 0.02,
sigma_area_a = 0,
beta_area_b = 0.02,
sigma_area_b = 0,
beta_area_c = 0.02,
sigma_area_c = 0,
beta_area_d = 0.02,
sigma_area_d = 0,
#beta_area_e = 0,
beta_fish_a = 0.002,
sigma_fish_a = 0,
beta_fish_b = 0.002,
sigma_fish_b = 0,
beta_fish_c = 0.002,
sigma_fish_c = 0,
beta_fish_d = 0.002,
sigma_fish_d = 0,
#beta_fish_e = 0,
beta_CO2_a = 0.002,
sigma_CO2_a = 0,
beta_CO2_b = 0.002,
sigma_CO2_b = 0,
beta_CO2_c = 0.002,
sigma_CO2_c = 0,
beta_CO2_d = 0.002,
sigma_CO2_d = 0,
#beta_CO2_e = 0,
beta_typhoon_a = 0.15,
sigma_typhoon_a = 0,
beta_typhoon_b = 0.15,
sigma_typhoon_b =0,
beta_typhoon_c = 0.15,
sigma_typhoon_c = 0,
beta_typhoon_d = 0.15,
sigma_typhoon_d = 0,
#beta_typhoon_e = 0,
beta_Aes_a = 0.15,
sigma_Aes_a = 0,
beta_Aes_b = 0.15,
sigma_Aes_b = 0,
beta_Aes_c = 0.15,
sigma_Aes_c = 0,
beta_Aes_d = 0.15,
sigma_Aes_d = 0,
#beta_Aes_e = 0,
beta_mosquito_a = -0.3,
sigma_mosquito_a = 0,
beta_mosquito_b = -0.3,
sigma_mosquito_b = 0,
beta_mosquito_c = -0.3,
sigma_mosquito_c = 0,
beta_mosquito_d = -0.3,
sigma_mosquito_d = 0,
#beta_mosquito_e = 0,
beta_log_Cost_a = -3,
sigma_log_Cost_a = 1,
beta_log_Cost_b = -3,
sigma_log_Cost_b = 1,
beta_log_Cost_c = -3,
sigma_log_Cost_c = 1,
beta_log_Cost_d = -3,
sigma_log_Cost_d = 1,
#beta_Cost_e = 0,
delta_a_mu = 0,
delta_a_sig = 0,
#gamma_age_a = 0,
#gamma_gender_a =0,
gamma_residence_a = 0,
gamma_income_a = 0,
gamma_acknowledgement_a =0,
gamma_frequency_a = 0,
gamma_education_a = 0,
delta_b = 0,
#gamma_age_b = 0,
#gamma_gender_b =0,
gamma_residence_b = 0,
gamma_acknowledgement_b =0,
gamma_income_b = 0,
gamma_frequency_b = 0,
gamma_education_b = 0,
delta_c = 0,
# gamma_age_c = 0,
#gamma_gender_c =0,
gamma_residence_c = 0,
gamma_acknowledgement_c =0,
gamma_income_c = 0,
gamma_education_c = 0,
gamma_frequency_c = 0,
delta_d = 0,
#gamma_age_d = 0,
#gamma_gender_d =0,
gamma_residence_d = 0,
gamma_acknowledgement_d =0,
gamma_income_d = 0,
gamma_education_d = 0,
gamma_frequency_d = 0)
#delta_e = 0,
#gamma_age_e = 0,
#gamma_gender_e =0,
#gamma_residence_e = 0,
#gamma_acknowledgement_e =0,
#gamma_income_e = 0,
#gamma_education_e = 0,
#gamma_frequency_e = 0)
### Vector with names (in quotes) of parameters to be kept fixed at their starting value in apollo_beta, use apollo_beta_fixed = c() if none
apollo_beta_fixed = c()
apollo_fixed = c("delta_d","gamma_acknowledgement_d","gamma_income_d","gamma_education_d","gamma_frequency_d","gamma_residence_d")
### Set parameters for generating draws
apollo_draws = list(
interDrawsType = "halton",
interNDraws = 500,
interUnifDraws = c(),
interNormDraws = c("draws_area","draws_fish","draws_CO2","draws_typhoon","draws_Aes",
"draws_mosquito","draws_Cost","draws_asc","draws_pi"),
intraDrawsType = "halton",
intraNDraws = 0,
intraUnifDraws = c(),
intraNormDraws = c()
)
### Create random parameters
apollo_randCoeff = function(apollo_beta, apollo_inputs){
randcoeff = list()
randcoeff[["b_area_a"]] =beta_area_a + sigma_area_a * draws_area
randcoeff[["b_area_b"]] =beta_area_b + sigma_area_b * draws_area
randcoeff[["b_area_c"]] =beta_area_c + sigma_area_c * draws_area
randcoeff[["b_area_d"]] =beta_area_d + sigma_area_d * draws_area
randcoeff[["b_fish_a"]] = beta_fish_a + sigma_fish_a * draws_fish
randcoeff[["b_fish_b"]] = beta_fish_b + sigma_fish_b * draws_fish
randcoeff[["b_fish_c"]] = beta_fish_c + sigma_fish_c * draws_fish
randcoeff[["b_fish_d"]] = beta_fish_d + sigma_fish_d * draws_fish
randcoeff[["b_CO2_a"]] = beta_CO2_a + sigma_CO2_a * draws_CO2
randcoeff[["b_CO2_b"]] = beta_CO2_b + sigma_CO2_b * draws_CO2
randcoeff[["b_CO2_c"]] = beta_CO2_c + sigma_CO2_c * draws_CO2
randcoeff[["b_CO2_d"]] = beta_CO2_d + sigma_CO2_d * draws_CO2
randcoeff[["b_typhoon_a"]] = beta_typhoon_a + sigma_typhoon_a * draws_typhoon
randcoeff[["b_typhoon_b"]] = beta_typhoon_b + sigma_typhoon_b * draws_typhoon
randcoeff[["b_typhoon_c"]] = beta_typhoon_c + sigma_typhoon_c * draws_typhoon
randcoeff[["b_typhoon_d"]] = beta_typhoon_d + sigma_typhoon_d * draws_typhoon
randcoeff[["b_Aes_a"]] = beta_Aes_a + sigma_Aes_a * draws_Aes
randcoeff[["b_Aes_b"]] = beta_Aes_b + sigma_Aes_b * draws_Aes
randcoeff[["b_Aes_c"]] = beta_Aes_c + sigma_Aes_c * draws_Aes
randcoeff[["b_Aes_d"]] = beta_Aes_d + sigma_Aes_d * draws_Aes
randcoeff[["b_mosquito_a"]] = beta_mosquito_a + sigma_mosquito_a * draws_mosquito
randcoeff[["b_mosquito_b"]] = beta_mosquito_b + sigma_mosquito_b * draws_mosquito
randcoeff[["b_mosquito_c"]] = beta_mosquito_c + sigma_mosquito_c * draws_mosquito
randcoeff[["b_mosquito_d"]] = beta_mosquito_d + sigma_mosquito_d * draws_mosquito
randcoeff[["b_Cost_a"]] = -exp(beta_log_Cost_a + sigma_log_Cost_a * draws_Cost)
randcoeff[["b_Cost_b"]] = -exp(beta_log_Cost_b + sigma_log_Cost_b * draws_Cost)
randcoeff[["b_Cost_c"]] = -exp(beta_log_Cost_c + sigma_log_Cost_c * draws_Cost)
randcoeff[["b_Cost_d"]] = -exp(beta_log_Cost_d + sigma_log_Cost_d * draws_Cost)
randcoeff[["ec"]] = sigma_asc * draws_asc
randcoeff[["delta_a"]] = delta_a_mu + delta_a_sig*draws_pi
return(randcoeff)
}
# ################################################################# #
#### DEFINE LATENT CLASS COMPONENTS ####
# ################################################################# #
apollo_lcPars=function(apollo_beta, apollo_inputs){
lcpars = list()
lcpars[["beta_area"]] = list(b_area_a, b_area_b, b_area_c, b_area_d)
lcpars[["beta_fish"]] = list(b_fish_a, b_fish_b, b_fish_c, b_fish_d)
lcpars[["beta_CO2"]] = list(b_CO2_a, b_CO2_b, b_CO2_c, b_CO2_d)
lcpars[["beta_typhoon"]] = list(b_typhoon_a, b_typhoon_b, b_typhoon_c, b_typhoon_d)
lcpars[["beta_Aes"]] = list(b_Aes_a, b_Aes_b, b_Aes_c, b_Aes_d)
lcpars[["beta_mosquito"]] = list(b_mosquito_a, b_mosquito_b, b_mosquito_c, b_mosquito_d)
lcpars[["beta_Cost"]] = list(b_Cost_a, b_Cost_b, b_Cost_c, b_Cost_d)
V=list()
V[["class_a"]] = delta_a + gamma_acknowledgement_a*acknowledgement2 +gamma_income_a*income_HH3+
gamma_education_a*education3+gamma_frequency_a*frequency3 + gamma_residence_a*residence2
V[["class_b"]] = delta_b + gamma_acknowledgement_b*acknowledgement2+gamma_income_b*income_HH3+
gamma_education_b*education3+gamma_frequency_b*frequency3 +gamma_residence_b*residence2
V[["class_c"]] = delta_c + gamma_acknowledgement_c*acknowledgement2+gamma_income_c*income_HH3+
gamma_education_c*education3+gamma_frequency_c*frequency3+gamma_residence_c*residence2
V[["class_d"]] = delta_d + gamma_acknowledgement_d*acknowledgement2+gamma_income_d*income_HH3+
gamma_education_d*education3+gamma_frequency_d*frequency3+gamma_residence_d*residence2
#V[["class_e"]] = delta_e + gamma_acknowledgement_e*acknowledgement2+gamma_income_e*income_HH3+gamma_education_e*education3+gamma_frequency_e*frequency3
### Settings for class allocation models
classAlloc_settings = list(
classes = c(class_a=1, class_b=2, class_c=3, class_d=4),
utilities = V
)
lcpars[["pi_values"]] = apollo_classAlloc(classAlloc_settings)
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 settings for MNL model component that are generic across classes
mnl_settings = list(
alternatives = c(low="A", high="B", NB="C"),
avail = list(low=1, high=1, NB=1),
choiceVar = choice
)
### Loop over classes
for(s in 1:4){
### Compute class-specific utilities
### Compute class-specific utilities
V=list()
V[['NB']] = asc + ec
V[['low']] = beta_area[[s]]*areaA/100 + beta_fish[[s]]*fishA + beta_CO2[[s]]*CO2A + beta_typhoon[[s]]*typhoonA/1000 +
beta_Aes[[s]]*AesA + beta_mosquito[[s]]*mosquitoA + beta_Cost[[s]]*CostA
V[['high']] = beta_area[[s]]*areaB/100 + beta_fish[[s]]*fishB + beta_CO2[[s]]*CO2B + beta_typhoon[[s]]*typhoonB/1000 +
beta_Aes[[s]]*AesB + beta_mosquito[[s]]*mosquitoB + beta_Cost[[s]]*CostB
mnl_settings$utilities = V
mnl_settings$componentName = paste0("Class_",s)
### Compute within-class choice probabilities using MNL model
P[[paste0("Class_",s)]] = apollo_mnl(mnl_settings, functionality)
### Take product across observation for same individual
P[[paste0("Class_",s)]] = apollo_panelProd(P[[paste0("Class_",s)]], apollo_inputs ,functionality)
### Average across inter-individual draws within classes
P[[paste0("Class_",s)]] = apollo_avgInterDraws(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)
### Average across inter-individual draws in class allocation probabilities
P[["model"]] = apollo_avgInterDraws(P[["model"]], 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)
Estimation process:
Code: Select all
Preparing user-defined functions.
Testing likelihood function...
Apollo found a model component of type classAlloc without a componentName. The name was set to
"classAlloc" by default.
Setting "avail" is missing, so full availability is assumed.
Overview of choices for MNL model component Class_1:
low high NB
Times available 6174.00 6174.00 6174.00
Times chosen 2903.00 2620.00 651.00
Percentage chosen overall 47.02 42.44 10.54
Percentage chosen when available 47.02 42.44 10.54
Overview of choices for MNL model component Class_2:
low high NB
Times available 6174.00 6174.00 6174.00
Times chosen 2903.00 2620.00 651.00
Percentage chosen overall 47.02 42.44 10.54
Percentage chosen when available 47.02 42.44 10.54
Overview of choices for MNL model component Class_3:
low high NB
Times available 6174.00 6174.00 6174.00
Times chosen 2903.00 2620.00 651.00
Percentage chosen overall 47.02 42.44 10.54
Percentage chosen when available 47.02 42.44 10.54
Overview of choices for MNL model component Class_4:
low high NB
Times available 6174.00 6174.00 6174.00
Times chosen 2903.00 2620.00 651.00
Percentage chosen overall 47.02 42.44 10.54
Percentage chosen when available 47.02 42.44 10.54
Summary of class allocation for LC model component :
Mean prob.
Class_1 0.2500
Class_2 0.2500
Class_3 0.2500
Class_4 0.2500
Class probability for model component "model" averaged across observations of each individual.
Pre-processing likelihood function...
Preparing workers for multithreading...
Testing influence of parameters
Starting main estimation
Initial function value: -7122.441
Initial gradient value:
asc sigma_asc beta_area_a sigma_area_a
2.066304e+02 7.710731e+00 8.268517e+02 -1.889842e+00
beta_area_b sigma_area_b beta_area_c sigma_area_c
8.268517e+02 -1.889842e+00 8.268517e+02 -1.889842e+00
beta_area_d sigma_area_d beta_fish_a sigma_fish_a
8.268517e+02 -1.889842e+00 9.082778e+02 1.465505e+02
beta_fish_b sigma_fish_b beta_fish_c sigma_fish_c
9.082778e+02 1.465505e+02 9.082778e+02 1.465505e+02
beta_fish_d sigma_fish_d beta_CO2_a sigma_CO2_a
9.082778e+02 1.465505e+02 -3.759434e+03 1.583446e+02
beta_CO2_b sigma_CO2_b beta_CO2_c sigma_CO2_c
-3.759434e+03 1.583446e+02 -3.759434e+03 1.583446e+02
beta_CO2_d sigma_CO2_d beta_typhoon_a sigma_typhoon_a
-3.759434e+03 1.583446e+02 -4.241461e+01 8.316066e-01
beta_typhoon_b sigma_typhoon_b beta_typhoon_c sigma_typhoon_c
-4.241461e+01 8.316066e-01 -4.241461e+01 8.316066e-01
beta_typhoon_d sigma_typhoon_d beta_Aes_a sigma_Aes_a
-4.241461e+01 8.316066e-01 -4.815464e+01 1.414961e+00
beta_Aes_b sigma_Aes_b beta_Aes_c sigma_Aes_c
-4.815464e+01 1.414961e+00 -4.815464e+01 1.414961e+00
beta_Aes_d sigma_Aes_d beta_mosquito_a sigma_mosquito_a
-4.815464e+01 1.414961e+00 -1.641941e+02 -2.814759e+00
beta_mosquito_b sigma_mosquito_b beta_mosquito_c sigma_mosquito_c
-1.641941e+02 -2.814759e+00 -1.641941e+02 -2.814759e+00
beta_mosquito_d sigma_mosquito_d beta_log_Cost_a sigma_log_Cost_a
-1.641941e+02 -2.814759e+00 -1.776934e+02 6.149588e+02
beta_log_Cost_b sigma_log_Cost_b beta_log_Cost_c sigma_log_Cost_c
-1.776934e+02 6.149588e+02 -1.776934e+02 6.149588e+02
beta_log_Cost_d sigma_log_Cost_d delta_a_mu delta_a_sig
-1.776934e+02 6.149588e+02 9.600407e-16 2.850266e-17
gamma_residence_a gamma_income_a gamma_acknowledgement_a gamma_frequency_a
1.020786e-15 6.569797e-15 8.169337e-16 3.231941e-15
gamma_education_a delta_b gamma_residence_b gamma_acknowledgement_b
-1.122416e-15 9.600407e-16 1.020786e-15 8.169337e-16
gamma_income_b gamma_frequency_b gamma_education_b delta_c
6.569797e-15 3.231941e-15 -1.122416e-15 5.322143e-16
gamma_residence_c gamma_acknowledgement_c gamma_income_c gamma_education_c
5.577681e-16 3.139308e-16 4.416787e-15 -1.261111e-15
gamma_frequency_c
1.323978e-15
initial value 7122.440877
iter 2 value 7106.935795
iter 3 value 7076.713589
iter 4 value 6950.666054
iter 5 value 6680.767105
iter 6 value 6116.311162
iter 7 value 6020.032647
iter 8 value 5983.822518
iter 9 value 5941.313710
iter 10 value 5937.608466
iter 11 value 5739.592568
iter 12 value 5438.059900
iter 13 value 5366.295055
iter 14 value 5328.173248
iter 15 value 5275.438537
iter 16 value 5256.285425
iter 17 value 5248.275035
iter 18 value 5232.980727
iter 19 value 5226.615120
iter 20 value 5224.322760
iter 21 value 5221.086711
iter 22 value 5215.467103
iter 23 value 5209.461951
iter 24 value 5205.891635
iter 25 value 5200.870198
iter 26 value 5154.431023
iter 27 value 5133.956294
iter 28 value 5087.848414
iter 29 value 5078.041745
iter 30 value 5063.787535
iter 31 value 5060.302405
iter 32 value 5044.503168
iter 33 value 5019.341600
iter 34 value 5000.768990
iter 35 value 4982.695607
iter 36 value 4940.215238
iter 37 value 4919.038270
iter 38 value 4886.915374
iter 39 value 4871.654753
iter 40 value 4871.585141
iter 41 value 4867.664140
iter 42 value 4851.295203
iter 43 value 4812.119641
iter 44 value 4734.643192
iter 45 value 4686.065527
iter 46 value 4672.248552
iter 47 value 4650.609257
iter 48 value 4595.096150
iter 49 value 4576.798344
iter 50 value 4547.450106
iter 51 value 4524.365664
iter 52 value 4512.196570
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iter 54 value 4501.654166
iter 55 value 4497.847761
iter 56 value 4489.517255
iter 57 value 4477.795177
iter 58 value 4471.490308
iter 59 value 4467.504148
iter 60 value 4462.607279
iter 61 value 4453.234026
iter 62 value 4449.711402
iter 63 value 4448.566537
iter 64 value 4448.040002
iter 65 value 4445.105278
iter 66 value 4441.207599
iter 67 value 4437.380834
iter 68 value 4431.919690
iter 69 value 4427.854384
iter 70 value 4425.663439
iter 71 value 4422.671344
iter 72 value 4420.502098
iter 73 value 4418.305352
iter 74 value 4414.806071
iter 75 value 4411.523412
iter 76 value 4407.676871
iter 77 value 4406.560814
iter 78 value 4404.102662
iter 79 value 4401.612148
iter 80 value 4399.718171
iter 81 value 4396.623146
iter 82 value 4394.714258
iter 83 value 4391.178742
iter 84 value 4389.038885
iter 85 value 4388.469755
iter 86 value 4385.548038
iter 87 value 4382.065975
iter 88 value 4379.577005
iter 89 value 4377.353393
iter 90 value 4375.549760
iter 91 value 4373.837606
iter 92 value 4373.772929
iter 93 value 4372.177473
iter 94 value 4371.174890
iter 95 value 4366.010903
iter 96 value 4363.670437
iter 97 value 4361.433989
iter 98 value 4360.893068
iter 99 value 4359.580893
iter 100 value 4359.191702
iter 101 value 4358.669140
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iter 105 value 4355.332810
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iter 112 value 4350.129842
iter 113 value 4349.832376
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iter 115 value 4349.339092
iter 116 value 4349.089084
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iter 131 value 4346.901107
iter 132 value 4346.806431
iter 133 value 4346.726085
iter 134 value 4346.614974
iter 135 value 4346.501060
iter 136 value 4346.394930
iter 137 value 4346.314916
iter 138 value 4346.236819
iter 139 value 4346.114147
iter 140 value 4345.975456
iter 141 value 4345.831264
iter 142 value 4345.635596
iter 143 value 4345.268287
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iter 161 value 4343.202672
iter 162 value 4343.202605
iter 163 value 4343.172270
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iter 166 value 4343.096619
iter 167 value 4343.084333
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iter 169 value 4343.060498
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iter 171 value 4343.058885
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iter 175 value 4343.026645
iter 176 value 4343.020579
iter 177 value 4343.012079
iter 178 value 4342.998448
iter 179 value 4342.986554
iter 180 value 4342.977175
iter 181 value 4342.963834
iter 182 value 4342.957631
iter 183 value 4342.956584
iter 184 value 4342.954056
iter 185 value 4342.949269
iter 186 value 4342.934681
iter 187 value 4342.919805
iter 188 value 4342.916052
iter 189 value 4342.905828
iter 190 value 4342.904629
iter 191 value 4342.896891
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iter 194 value 4342.858660
iter 195 value 4342.836226
iter 196 value 4342.835004
iter 197 value 4342.826300
iter 198 value 4342.808204
iter 199 value 4342.794730
iter 200 value 4342.770903
final value 4342.770903
stopped after 200 iterations
Estimated parameters:
Estimate
asc -11.061294
sigma_asc -8.222560
beta_area_a -0.030066
sigma_area_a -0.081775
beta_area_b 11.506343
sigma_area_b 13.804198
beta_area_c 1.662038
sigma_area_c -1.335112
beta_area_d -0.655261
sigma_area_d 13.617864
beta_fish_a 0.003697
sigma_fish_a 0.005112
beta_fish_b 0.315508
sigma_fish_b -0.661881
beta_fish_c -0.015327
sigma_fish_c 0.005024
beta_fish_d -0.525983
sigma_fish_d 0.637828
beta_CO2_a 0.002085
sigma_CO2_a 0.004362
beta_CO2_b -0.062888
sigma_CO2_b 0.238467
beta_CO2_c 0.015945
sigma_CO2_c -0.005619
beta_CO2_d 0.031832
sigma_CO2_d 0.230344
beta_typhoon_a 0.159376
sigma_typhoon_a 0.143595
beta_typhoon_b -3.810390
sigma_typhoon_b 3.416624
beta_typhoon_c 0.603164
sigma_typhoon_c -0.631694
beta_typhoon_d 9.746217
sigma_typhoon_d -7.538491
beta_Aes_a 0.141236
sigma_Aes_a -0.548785
beta_Aes_b 10.184351
sigma_Aes_b -0.721753
beta_Aes_c -0.068672
sigma_Aes_c -0.007413
beta_Aes_d -20.613460
sigma_Aes_d 22.508271
beta_mosquito_a -0.388458
sigma_mosquito_a 0.658468
beta_mosquito_b 4.057403
sigma_mosquito_b 6.253682
beta_mosquito_c -0.001292
sigma_mosquito_c -0.084935
beta_mosquito_d -5.520252
sigma_mosquito_d -6.277541
beta_log_Cost_a -4.691008
sigma_log_Cost_a 1.522479
beta_log_Cost_b -28.236226
sigma_log_Cost_b 0.220997
beta_log_Cost_c -18.844314
sigma_log_Cost_c 1.736280
beta_log_Cost_d -3.842124
sigma_log_Cost_d 3.696279
delta_a_mu 5.927363
delta_a_sig -5.175481
gamma_residence_a -2.443949
gamma_income_a 0.066645
gamma_acknowledgement_a -1.739873
gamma_frequency_a -1.313066
gamma_education_a 2.792816
delta_b -18.569702
gamma_residence_b 0.070208
gamma_acknowledgement_b -0.306767
gamma_income_b 2.205548
gamma_frequency_b 2.657856
gamma_education_b -1.864127
delta_c 3.235958
gamma_residence_c -1.827196
gamma_acknowledgement_c -1.089652
gamma_income_c 0.494773
gamma_education_c 1.236508
gamma_frequency_c -1.017687
delta_d 0.000000
gamma_residence_d 0.000000
gamma_acknowledgement_d 0.000000
gamma_income_d 0.000000
gamma_education_d 0.000000
gamma_frequency_d 0.000000
#####################################################################################################################
Estimation failed. No covariance matrix to compute.
#####################################################################################################################
Summary of class allocation for LC model component :
Mean prob.
Class_1 0.77594
Class_2 0.03955
Class_3 0.16864
Class_4 0.01587
Calculating LL(0) for applicable models...
Calculating LL(c) for applicable models...
Calculating LL of each model component...
Results:
Code: Select all
Model name : mangrove restoration CE
Model description : Latent Class method on mangrove restoration CE data
Model run at : 2022-03-14 09:21:16
Estimation method : bfgs
Model diagnosis : iteration limit exceeded
Number of individuals : 1029
Number of rows in database : 6174
Number of modelled outcomes : 6174
Number of cores used : 6
Number of inter-individual draws : 500 (halton)
LL(start) : -7122.44
LL(0, whole model) : -6782.83
LL(C, whole model) : -5900.89
LL(final, whole model) : -4342.77
Rho-square (0) : 0.3597
Adj.Rho-square (0) : 0.3484
Rho-square (C) : 0.264
Adj.Rho-square (C) : 0.251
AIC : 8839.54
BIC : 9357.61
LL(0,Class_1) : -6782.83
LL(final,Class_1) : -4716.53
LL(0,Class_2) : -6782.83
LL(final,Class_2) : -18529.36
LL(0,Class_3) : -6782.83
LL(final,Class_3) : -6451.04
LL(0,Class_4) : -6782.83
LL(final,Class_4) : -17485.42
Estimated parameters : 77
Time taken (hh:mm:ss) : 01:53:6.08
pre-estimation : 00:13:23.85
estimation : 01:39:31.21
post-estimation : 00:00:11.02
Iterations : 201 (iteration limit exceeded )
Unconstrained optimisation.
Estimates:
Estimate s.e. t.rat.(0) p(1-sided) Rob.s.e. Rob.t.rat.(0) p(1-sided)
asc -11.061294 NA NA NA NA NA NA
sigma_asc -8.222560 NA NA NA NA NA NA
beta_area_a -0.030066 NA NA NA NA NA NA
sigma_area_a -0.081775 NA NA NA NA NA NA
beta_area_b 11.506343 NA NA NA NA NA NA
sigma_area_b 13.804198 NA NA NA NA NA NA
beta_area_c 1.662038 NA NA NA NA NA NA
sigma_area_c -1.335112 NA NA NA NA NA NA
beta_area_d -0.655261 NA NA NA NA NA NA
sigma_area_d 13.617864 NA NA NA NA NA NA
beta_fish_a 0.003697 NA NA NA NA NA NA
sigma_fish_a 0.005112 NA NA NA NA NA NA
beta_fish_b 0.315508 NA NA NA NA NA NA
sigma_fish_b -0.661881 NA NA NA NA NA NA
beta_fish_c -0.015327 NA NA NA NA NA NA
sigma_fish_c 0.005024 NA NA NA NA NA NA
beta_fish_d -0.525983 NA NA NA NA NA NA
sigma_fish_d 0.637828 NA NA NA NA NA NA
beta_CO2_a 0.002085 NA NA NA NA NA NA
sigma_CO2_a 0.004362 NA NA NA NA NA NA
beta_CO2_b -0.062888 NA NA NA NA NA NA
sigma_CO2_b 0.238467 NA NA NA NA NA NA
beta_CO2_c 0.015945 NA NA NA NA NA NA
sigma_CO2_c -0.005619 NA NA NA NA NA NA
beta_CO2_d 0.031832 NA NA NA NA NA NA
sigma_CO2_d 0.230344 NA NA NA NA NA NA
beta_typhoon_a 0.159376 NA NA NA NA NA NA
sigma_typhoon_a 0.143595 NA NA NA NA NA NA
beta_typhoon_b -3.810390 NA NA NA NA NA NA
sigma_typhoon_b 3.416624 NA NA NA NA NA NA
beta_typhoon_c 0.603164 NA NA NA NA NA NA
sigma_typhoon_c -0.631694 NA NA NA NA NA NA
beta_typhoon_d 9.746217 NA NA NA NA NA NA
sigma_typhoon_d -7.538491 NA NA NA NA NA NA
beta_Aes_a 0.141236 NA NA NA NA NA NA
sigma_Aes_a -0.548785 NA NA NA NA NA NA
beta_Aes_b 10.184351 NA NA NA NA NA NA
sigma_Aes_b -0.721753 NA NA NA NA NA NA
beta_Aes_c -0.068672 NA NA NA NA NA NA
sigma_Aes_c -0.007413 NA NA NA NA NA NA
beta_Aes_d -20.613460 NA NA NA NA NA NA
sigma_Aes_d 22.508271 NA NA NA NA NA NA
beta_mosquito_a -0.388458 NA NA NA NA NA NA
sigma_mosquito_a 0.658468 NA NA NA NA NA NA
beta_mosquito_b 4.057403 NA NA NA NA NA NA
sigma_mosquito_b 6.253682 NA NA NA NA NA NA
beta_mosquito_c -0.001292 NA NA NA NA NA NA
sigma_mosquito_c -0.084935 NA NA NA NA NA NA
beta_mosquito_d -5.520252 NA NA NA NA NA NA
sigma_mosquito_d -6.277541 NA NA NA NA NA NA
beta_log_Cost_a -4.691008 NA NA NA NA NA NA
sigma_log_Cost_a 1.522479 NA NA NA NA NA NA
beta_log_Cost_b -28.236226 NA NA NA NA NA NA
sigma_log_Cost_b 0.220997 NA NA NA NA NA NA
beta_log_Cost_c -18.844314 NA NA NA NA NA NA
sigma_log_Cost_c 1.736280 NA NA NA NA NA NA
beta_log_Cost_d -3.842124 NA NA NA NA NA NA
sigma_log_Cost_d 3.696279 NA NA NA NA NA NA
delta_a_mu 5.927363 NA NA NA NA NA NA
delta_a_sig -5.175481 NA NA NA NA NA NA
gamma_residence_a -2.443949 NA NA NA NA NA NA
gamma_income_a 0.066645 NA NA NA NA NA NA
gamma_acknowledgement_a -1.739873 NA NA NA NA NA NA
gamma_frequency_a -1.313066 NA NA NA NA NA NA
gamma_education_a 2.792816 NA NA NA NA NA NA
delta_b -18.569702 NA NA NA NA NA NA
gamma_residence_b 0.070208 NA NA NA NA NA NA
gamma_acknowledgement_b -0.306767 NA NA NA NA NA NA
gamma_income_b 2.205548 NA NA NA NA NA NA
gamma_frequency_b 2.657856 NA NA NA NA NA NA
gamma_education_b -1.864127 NA NA NA NA NA NA
delta_c 3.235958 NA NA NA NA NA NA
gamma_residence_c -1.827196 NA NA NA NA NA NA
gamma_acknowledgement_c -1.089652 NA NA NA NA NA NA
gamma_income_c 0.494773 NA NA NA NA NA NA
gamma_education_c 1.236508 NA NA NA NA NA NA
gamma_frequency_c -1.017687 NA NA NA NA NA NA
delta_d 0.000000 NA NA NA NA NA NA
gamma_residence_d 0.000000 NA NA NA NA NA NA
gamma_acknowledgement_d 0.000000 NA NA NA NA NA NA
gamma_income_d 0.000000 NA NA NA NA NA NA
gamma_education_d 0.000000 NA NA NA NA NA NA
gamma_frequency_d 0.000000 NA NA NA NA NA NA
Summary of class allocation for LC model component :
Mean prob.
Class_1 0.77594
Class_2 0.03955
Class_3 0.16864
Class_4 0.01587