Note
Go to the end to download the full example code.
Discrete mixture with panel data
- Example of a discrete mixture of logit models, also called latent
class model. The datafile is organized as panel data. Compared to Discrete mixture with panel data, we integrate before the discrete mixture to show that it is equivalent.
- author:
Michel Bierlaire, EPFL
- date:
Mon Apr 10 11:55:26 2023
import biogeme.biogeme_logging as blog
import biogeme.biogeme as bio
from biogeme import models
from biogeme.expressions import (
Beta,
bioDraws,
PanelLikelihoodTrajectory,
MonteCarlo,
log,
ExpressionOrNumeric,
)
from biogeme.parameters import Parameters
See the data processing script: Panel data preparation for Swissmetro.
from swissmetro_panel import (
database,
CHOICE,
SM_AV,
CAR_AV_SP,
TRAIN_AV_SP,
TRAIN_TT_SCALED,
TRAIN_COST_SCALED,
SM_TT_SCALED,
SM_COST_SCALED,
CAR_TT_SCALED,
CAR_CO_SCALED,
)
logger = blog.get_screen_logger(level=blog.INFO)
logger.info('Example b15panel_discrete_bis.py')
Example b15panel_discrete_bis.py
Parameters to be estimated. One version for each latent class.
NUMBER_OF_CLASSES = 2
B_COST = [Beta(f'B_COST_class{i}', 0, None, None, 0) for i in range(NUMBER_OF_CLASSES)]
Define a random parameter, normally distributed across individuals, designed to be used for Monte-Carlo simulation.
B_TIME = [Beta(f'B_TIME_class{i}', 0, None, None, 0) for i in range(NUMBER_OF_CLASSES)]
It is advised not to use 0 as starting value for the following parameter.
B_TIME_S = [
Beta(f'B_TIME_S_class{i}', 1, None, None, 0) for i in range(NUMBER_OF_CLASSES)
]
B_TIME_RND: list[ExpressionOrNumeric] = [
B_TIME[i] + B_TIME_S[i] * bioDraws(f'B_TIME_RND_class{i}', 'NORMAL_ANTI')
for i in range(NUMBER_OF_CLASSES)
]
We do the same for the constants, to address serial correlation.
ASC_CAR = [
Beta(f'ASC_CAR_class{i}', 0, None, None, 0) for i in range(NUMBER_OF_CLASSES)
]
ASC_CAR_S = [
Beta(f'ASC_CAR_S_class{i}', 1, None, None, 0) for i in range(NUMBER_OF_CLASSES)
]
ASC_CAR_RND = [
ASC_CAR[i] + ASC_CAR_S[i] * bioDraws(f'ASC_CAR_RND_class{i}', 'NORMAL_ANTI')
for i in range(NUMBER_OF_CLASSES)
]
ASC_TRAIN = [
Beta(f'ASC_TRAIN_class{i}', 0, None, None, 0) for i in range(NUMBER_OF_CLASSES)
]
ASC_TRAIN_S = [
Beta(f'ASC_TRAIN_S_class{i}', 1, None, None, 0) for i in range(NUMBER_OF_CLASSES)
]
ASC_TRAIN_RND = [
ASC_TRAIN[i] + ASC_TRAIN_S[i] * bioDraws(f'ASC_TRAIN_RND_class{i}', 'NORMAL_ANTI')
for i in range(NUMBER_OF_CLASSES)
]
ASC_SM = [Beta(f'ASC_SM_class{i}', 0, None, None, 1) for i in range(NUMBER_OF_CLASSES)]
ASC_SM_S = [
Beta(f'ASC_SM_S_class{i}', 1, None, None, 0) for i in range(NUMBER_OF_CLASSES)
]
ASC_SM_RND = [
ASC_SM[i] + ASC_SM_S[i] * bioDraws(f'ASC_SM_RND_class{i}', 'NORMAL_ANTI')
for i in range(NUMBER_OF_CLASSES)
]
Class memebership probability.
prob_class0 = Beta('prob_class0', 0.5, 0, 1, 0)
prob_class1 = 1 - prob_class0
In class 0, it is assumed that the time coefficient is zero.
B_TIME_RND[0] = 0
Utility functions.
V1 = [
ASC_TRAIN_RND[i] + B_TIME_RND[i] * TRAIN_TT_SCALED + B_COST[i] * TRAIN_COST_SCALED
for i in range(NUMBER_OF_CLASSES)
]
V2 = [
ASC_SM_RND[i] + B_TIME_RND[i] * SM_TT_SCALED + B_COST[i] * SM_COST_SCALED
for i in range(NUMBER_OF_CLASSES)
]
V3 = [
ASC_CAR_RND[i] + B_TIME_RND[i] * CAR_TT_SCALED + B_COST[i] * CAR_CO_SCALED
for i in range(NUMBER_OF_CLASSES)
]
V = [{1: V1[i], 2: V2[i], 3: V3[i]} for i in range(NUMBER_OF_CLASSES)]
Associate the availability conditions with the alternatives
av = {1: TRAIN_AV_SP, 2: SM_AV, 3: CAR_AV_SP}
The choice model is a discrete mixture of logit, with availability conditions We calculate the conditional probability for each class.
prob = [
MonteCarlo(PanelLikelihoodTrajectory(models.logit(V[i], av, CHOICE)))
for i in range(NUMBER_OF_CLASSES)
]
Conditional to the random variables, likelihood for the individual.
probIndiv = prob_class0 * prob[0] + prob_class1 * prob[1]
We integrate over the random variables using Monte-Carlo.
logprob = log(probIndiv)
As the objective is to illustrate the syntax, we calculate the Monte-Carlo approximation with a small number of draws.
the_biogeme = bio.BIOGEME(database, logprob, number_of_draws=100, seed=1223)
the_biogeme.modelName = 'b15panel_discrete_bis'
Biogeme parameters read from biogeme.toml.
Estimate the parameters.
results = the_biogeme.estimate()
As the model is rather complex, we cancel the calculation of second derivatives. If you want to control the parameters, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
*** Initial values of the parameters are obtained from the file __b15panel_discrete_bis.iter
Cannot read file __b15panel_discrete_bis.iter. Statement is ignored.
The number of draws (100) is low. The results may not be meaningful.
As the model is rather complex, we cancel the calculation of second derivatives. If you want to control the parameters, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: BFGS with trust region for simple bounds
Iter. ASC_CAR_S_class ASC_CAR_S_class ASC_CAR_class0 ASC_CAR_class1 ASC_SM_S_class0 ASC_SM_S_class1 ASC_TRAIN_S_cla ASC_TRAIN_S_cla ASC_TRAIN_class ASC_TRAIN_class B_COST_class0 B_COST_class1 B_TIME_S_class1 B_TIME_class1 prob_class0 Function Relgrad Radius Rho
0 2 2 1 1 2 2 2 2 -1 -1 -1 -1 2 -1 5.6e-17 4e+03 0.033 1 0.46 +
1 2 2 1 1 2 2 2 2 -1 -1 -1 -1 2 -1 5.6e-17 4e+03 0.033 0.5 0.036 -
2 2 1.5 1 0.5 2 1.5 2 1.5 -1 -1.5 -1 -1.5 2.5 -1.5 5.6e-17 3.9e+03 0.026 0.5 0.61 +
3 2 1.5 1 0.28 2 1.5 2 1.5 -1 -1.6 -1 -1.7 2.7 -2 3.2e-05 3.8e+03 0.024 0.5 0.24 +
4 2 2 0.99 -0.032 1.9 1.7 2 1.9 -0.97 -1.2 -1 -2 2.8 -2.4 3.1e-05 3.8e+03 0.027 0.5 0.66 +
5 2 2.2 0.98 0.18 1.9 1.6 2.1 2 -0.94 -1.3 -1 -2.4 2.8 -2.9 3.2e-05 3.7e+03 0.017 0.5 0.67 +
6 2.1 2.7 0.96 0.15 1.8 1.6 2.1 2.3 -0.89 -1 -1.1 -2.5 3 -3.4 3.1e-05 3.7e+03 0.026 5 1.2 ++
7 2.1 2.7 0.96 0.15 1.8 1.6 2.1 2.3 -0.89 -1 -1.1 -2.5 3 -3.4 3.1e-05 3.7e+03 0.026 2.5 -1.1 -
8 2.3 3.6 0.82 0.26 1.3 1.8 2.4 2.8 -0.64 -0.16 -0.97 -3.2 3.4 -5.9 2.9e-05 3.6e+03 0.026 2.5 0.84 +
9 2.3 3.6 0.82 0.26 1.3 1.8 2.4 2.8 -0.64 -0.16 -0.97 -3.2 3.4 -5.9 2.9e-05 3.6e+03 0.026 1.2 -0.4 -
10 2.3 3.6 0.82 0.26 1.3 1.8 2.4 2.8 -0.64 -0.16 -0.97 -3.2 3.4 -5.9 2.9e-05 3.6e+03 0.026 0.62 -0.34 -
11 2.3 3.6 0.8 0.6 1.4 1.7 2.4 2.4 -0.63 -0.46 -0.9 -3.4 4.1 -5.5 3.1e-05 3.6e+03 0.0073 0.62 0.16 +
12 2.3 3.6 0.8 0.6 1.4 1.7 2.4 2.4 -0.63 -0.46 -0.9 -3.4 4.1 -5.5 3.1e-05 3.6e+03 0.0073 0.31 -1.8 -
13 2.4 3.6 0.77 0.32 1.4 1.4 2.4 2.1 -0.6 -0.42 -0.8 -3.2 4 -5.8 2.8e-05 3.6e+03 0.034 0.31 0.2 +
14 2.4 3.6 0.77 0.32 1.4 1.4 2.4 2.1 -0.6 -0.42 -0.8 -3.2 4 -5.8 2.8e-05 3.6e+03 0.034 0.16 0.039 -
15 2.4 3.6 0.76 0.36 1.4 1.5 2.4 2.1 -0.6 -0.38 -0.8 -3.2 4.1 -5.6 2.8e-05 3.6e+03 0.023 0.16 0.27 +
16 2.4 3.6 0.75 0.45 1.4 1.5 2.5 2.1 -0.58 -0.23 -0.8 -3.4 4 -5.7 3e-05 3.6e+03 0.009 0.16 0.61 +
17 2.4 3.6 0.75 0.45 1.4 1.5 2.5 2.1 -0.58 -0.23 -0.8 -3.4 4 -5.7 3e-05 3.6e+03 0.009 0.078 -0.066 -
18 2.4 3.7 0.73 0.43 1.3 1.5 2.5 2 -0.56 -0.28 -0.79 -3.3 4 -5.7 2.9e-05 3.6e+03 0.0017 0.078 0.77 +
19 2.4 3.7 0.73 0.43 1.3 1.5 2.5 2 -0.56 -0.28 -0.79 -3.3 4 -5.7 2.9e-05 3.6e+03 0.0017 0.039 -0.51 -
20 2.4 3.7 0.73 0.43 1.3 1.5 2.5 2 -0.55 -0.28 -0.78 -3.3 4 -5.7 2.9e-05 3.6e+03 0.0022 0.039 0.82 +
21 2.4 3.7 0.73 0.43 1.3 1.5 2.5 2 -0.55 -0.28 -0.78 -3.3 4 -5.7 2.9e-05 3.6e+03 0.0022 0.02 -0.013 -
22 2.5 3.7 0.72 0.43 1.3 1.5 2.5 2 -0.54 -0.26 -0.78 -3.3 4 -5.7 2.9e-05 3.6e+03 0.0054 0.02 0.2 +
23 2.5 3.7 0.71 0.43 1.3 1.5 2.5 2 -0.53 -0.26 -0.77 -3.3 4 -5.7 3e-05 3.6e+03 0.00055 0.2 1 ++
24 2.6 3.7 0.63 0.42 1.3 1.7 2.5 1.8 -0.44 -0.25 -0.73 -3.4 4.1 -5.8 3e-05 3.6e+03 0.0014 2 2.5 ++
25 2.6 3.7 0.63 0.42 1.3 1.7 2.5 1.8 -0.44 -0.25 -0.73 -3.4 4.1 -5.8 3e-05 3.6e+03 0.0014 0.98 -3.7 -
26 2.6 3.7 0.63 0.42 1.3 1.7 2.5 1.8 -0.44 -0.25 -0.73 -3.4 4.1 -5.8 3e-05 3.6e+03 0.0014 0.49 -1 -
27 2.7 3.6 0.58 0.31 1.3 2.1 2.5 1.4 -0.39 -0.16 -0.72 -3.5 4.1 -5.8 3.2e-05 3.6e+03 0.0051 4.9 0.98 ++
28 2.7 3.6 0.58 0.31 1.3 2.1 2.5 1.4 -0.39 -0.16 -0.72 -3.5 4.1 -5.8 3.2e-05 3.6e+03 0.0051 2.4 -6.8 -
29 2.7 3.6 0.58 0.31 1.3 2.1 2.5 1.4 -0.39 -0.16 -0.72 -3.5 4.1 -5.8 3.2e-05 3.6e+03 0.0051 1.2 -1.2 -
30 2.7 3.6 0.58 0.31 1.3 2.1 2.5 1.4 -0.39 -0.16 -0.72 -3.5 4.1 -5.8 3.2e-05 3.6e+03 0.0051 0.61 -0.2 -
31 2.7 3.6 0.58 0.31 1.3 2.1 2.5 1.4 -0.39 -0.16 -0.72 -3.5 4.1 -5.8 3.2e-05 3.6e+03 0.0051 0.31 0.095 -
32 2.8 3.8 0.54 0.25 1.3 2 2.5 1.1 -0.35 -0.31 -0.72 -3.4 4.1 -5.9 3.1e-05 3.6e+03 0.0063 0.31 0.61 +
33 2.9 3.9 0.52 0.25 1.4 1.9 2.5 0.85 -0.33 -0.093 -0.74 -3.7 4.2 -5.9 3.1e-05 3.6e+03 0.0078 0.31 0.82 +
34 2.9 4 0.5 0.33 1.4 2.2 2.5 0.54 -0.31 -0.02 -0.74 -3.7 4.2 -5.9 3.3e-05 3.6e+03 0.0064 0.31 0.47 +
35 2.9 3.8 0.48 0.28 1.4 2 2.5 0.24 -0.29 0.057 -0.74 -3.8 4.2 -6.1 3.2e-05 3.6e+03 0.015 0.31 0.68 +
36 3 4 0.47 0.25 1.4 2 2.5 -0.067 -0.27 0.06 -0.7 -3.8 4.3 -6.1 3.2e-05 3.6e+03 0.0042 0.31 0.42 +
37 3 4 0.47 0.25 1.4 2 2.5 -0.067 -0.27 0.06 -0.7 -3.8 4.3 -6.1 3.2e-05 3.6e+03 0.0042 0.15 -0.14 -
38 3.1 3.9 0.47 0.25 1.4 2.2 2.5 -0.052 -0.25 -0.029 -0.68 -3.9 4.3 -6.1 3.2e-05 3.6e+03 0.0015 0.15 0.66 +
39 3.1 3.9 0.47 0.25 1.4 2.2 2.5 -0.052 -0.25 -0.029 -0.68 -3.9 4.3 -6.1 3.2e-05 3.6e+03 0.0015 0.076 -0.49 -
40 3.2 3.9 0.47 0.17 1.3 2.1 2.5 -0.1 -0.23 0.016 -0.65 -3.9 4.3 -6.2 3.2e-05 3.6e+03 0.0015 0.076 0.43 +
41 3.2 4 0.47 0.25 1.3 2.1 2.5 -0.13 -0.22 -0.0081 -0.61 -3.9 4.3 -6.1 3.3e-05 3.6e+03 0.0039 0.076 0.55 +
42 3.3 3.9 0.47 0.21 1.3 2.2 2.5 -0.16 -0.2 0.0045 -0.57 -3.9 4.3 -6.1 3.3e-05 3.6e+03 0.002 0.076 0.76 +
43 3.4 3.9 0.47 0.18 1.3 2.2 2.4 -0.21 -0.19 -0.026 -0.52 -3.9 4.3 -6.2 3.3e-05 3.6e+03 0.002 0.076 0.65 +
44 3.4 4 0.47 0.23 1.3 2.2 2.4 -0.28 -0.17 0.015 -0.49 -3.9 4.3 -6.1 3.4e-05 3.6e+03 0.0016 0.076 0.83 +
45 3.5 3.9 0.47 0.21 1.2 2.2 2.4 -0.35 -0.16 -0.026 -0.46 -3.9 4.3 -6.1 3.4e-05 3.6e+03 0.0018 0.76 0.95 ++
46 3.5 3.9 0.47 0.21 1.2 2.2 2.4 -0.35 -0.16 -0.026 -0.46 -3.9 4.3 -6.1 3.4e-05 3.6e+03 0.0018 0.38 -5.2 -
47 3.5 3.9 0.47 0.21 1.2 2.2 2.4 -0.35 -0.16 -0.026 -0.46 -3.9 4.3 -6.1 3.4e-05 3.6e+03 0.0018 0.19 -1.4 -
48 3.6 3.9 0.47 0.19 1.2 2.1 2.4 -0.54 -0.15 -0.0032 -0.44 -3.9 4.3 -6.1 3.5e-05 3.6e+03 0.0025 0.19 0.19 +
49 3.6 3.9 0.47 0.19 1.2 2.1 2.4 -0.54 -0.15 -0.0032 -0.44 -3.9 4.3 -6.1 3.5e-05 3.6e+03 0.0025 0.095 -0.7 -
50 3.7 3.9 0.48 0.26 1.2 2.2 2.4 -0.54 -0.15 -0.052 -0.44 -3.9 4.3 -6.1 3.6e-05 3.6e+03 0.0032 0.095 0.57 +
51 3.7 3.8 0.48 0.23 1.2 2.2 2.3 -0.55 -0.15 -0.064 -0.44 -3.9 4.3 -6.1 3.7e-05 3.6e+03 0.0026 0.095 0.35 +
52 3.8 3.9 0.48 0.22 1.2 2.2 2.3 -0.65 -0.14 -0.04 -0.45 -3.9 4.2 -6.1 3.7e-05 3.6e+03 0.00062 0.095 0.75 +
53 3.9 3.8 0.48 0.26 1.2 2.2 2.3 -0.62 -0.14 -0.057 -0.46 -3.9 4.2 -6 3.8e-05 3.6e+03 0.0013 0.95 0.96 ++
54 3.9 3.8 0.48 0.26 1.2 2.2 2.3 -0.62 -0.14 -0.057 -0.46 -3.9 4.2 -6 3.8e-05 3.6e+03 0.0013 0.48 -3.5 -
55 3.9 3.8 0.48 0.26 1.2 2.2 2.3 -0.62 -0.14 -0.057 -0.46 -3.9 4.2 -6 3.8e-05 3.6e+03 0.0013 0.24 -0.36 -
56 4.1 3.8 0.48 0.24 1.1 2.3 2.2 -0.73 -0.15 -0.089 -0.5 -3.9 4.2 -6 3.9e-05 3.6e+03 0.0017 0.24 0.5 +
57 4.1 3.8 0.48 0.24 1.1 2.3 2.2 -0.73 -0.15 -0.089 -0.5 -3.9 4.2 -6 3.9e-05 3.6e+03 0.0017 0.12 -1.9 -
58 4.1 3.8 0.48 0.24 1.1 2.3 2.2 -0.73 -0.15 -0.089 -0.5 -3.9 4.2 -6 3.9e-05 3.6e+03 0.0017 0.06 0.086 -
59 4.1 3.8 0.48 0.25 1.1 2.2 2.2 -0.71 -0.15 -0.096 -0.5 -3.9 4.2 -6 4e-05 3.6e+03 0.0013 0.6 1.1 ++
60 4.1 3.8 0.48 0.25 1.1 2.2 2.2 -0.71 -0.15 -0.096 -0.5 -3.9 4.2 -6 4e-05 3.6e+03 0.0013 0.3 -1.5 -
61 4.4 3.8 0.47 0.25 1.1 2.2 2 -0.56 -0.17 -0.062 -0.52 -3.8 4.2 -5.9 4e-05 3.6e+03 0.0016 0.3 0.36 +
62 4.4 3.8 0.47 0.25 1.1 2.2 2 -0.56 -0.17 -0.062 -0.52 -3.8 4.2 -5.9 4e-05 3.6e+03 0.0016 0.15 -0.52 -
63 4.6 3.8 0.47 0.22 1.1 2.2 2 -0.63 -0.18 -0.1 -0.56 -4 4.2 -6 4.1e-05 3.6e+03 0.0027 0.15 0.59 +
64 4.6 3.8 0.47 0.22 1.1 2.2 2 -0.63 -0.18 -0.1 -0.56 -4 4.2 -6 4.1e-05 3.6e+03 0.0027 0.075 -0.56 -
65 4.6 3.9 0.47 0.21 1.1 2.2 2 -0.65 -0.18 -0.095 -0.56 -3.9 4.2 -6 4.2e-05 3.6e+03 0.0016 0.075 0.62 +
66 4.6 3.8 0.47 0.29 1.1 2.2 1.9 -0.67 -0.2 -0.067 -0.57 -3.9 4.2 -6 4.3e-05 3.6e+03 0.00043 0.075 0.79 +
67 4.7 3.8 0.47 0.22 1.1 2.2 1.9 -0.65 -0.22 -0.1 -0.58 -3.9 4.2 -6 4.5e-05 3.6e+03 0.0012 0.75 1 ++
68 5.5 3.9 0.44 0.29 0.94 2.2 1.5 -0.52 -0.36 -0.069 -0.72 -3.9 4.3 -6.2 4.8e-05 3.6e+03 0.0034 0.75 0.8 +
69 5.5 3.9 0.44 0.29 0.94 2.2 1.5 -0.52 -0.36 -0.069 -0.72 -3.9 4.3 -6.2 4.8e-05 3.6e+03 0.0034 0.37 -3.6 -
70 5.5 3.9 0.44 0.29 0.94 2.2 1.5 -0.52 -0.36 -0.069 -0.72 -3.9 4.3 -6.2 4.8e-05 3.6e+03 0.0034 0.19 -0.92 -
71 5.6 3.8 0.45 0.26 0.94 2.2 1.4 -0.49 -0.41 -0.047 -0.72 -3.9 4.2 -6 5.2e-05 3.6e+03 0.0014 0.19 0.77 +
72 5.6 3.9 0.45 0.15 0.95 2.2 1.4 -0.68 -0.44 -0.13 -0.72 -3.9 4.2 -6 5.6e-05 3.6e+03 0.0021 0.19 0.5 +
73 5.8 3.8 0.47 0.27 0.95 2.2 1.2 -0.69 -0.55 -0.065 -0.76 -3.8 4.2 -6 5.9e-05 3.6e+03 0.002 0.19 0.75 +
74 6 3.8 0.49 0.25 0.91 2.2 1 -0.76 -0.61 -0.11 -0.72 -3.9 4.2 -6 6.3e-05 3.6e+03 0.0014 1.9 1.1 ++
75 6 3.8 0.49 0.25 0.91 2.2 1 -0.76 -0.61 -0.11 -0.72 -3.9 4.2 -6 6.3e-05 3.6e+03 0.0014 0.93 -1.3 -
76 6 3.8 0.49 0.25 0.91 2.2 1 -0.76 -0.61 -0.11 -0.72 -3.9 4.2 -6 6.3e-05 3.6e+03 0.0014 0.47 -0.69 -
77 6.5 4 0.42 0.097 0.72 2.2 0.73 -0.37 -0.82 -0.088 -0.75 -4 4.3 -6.1 7.2e-05 3.6e+03 0.0024 0.47 0.2 +
78 6.9 3.9 0.43 0.18 0.59 2.1 0.52 -0.54 -1.1 -0.075 -0.46 -3.9 4.2 -5.9 8.2e-05 3.6e+03 0.0014 4.7 1.2 ++
79 6.9 3.9 0.43 0.18 0.59 2.1 0.52 -0.54 -1.1 -0.075 -0.46 -3.9 4.2 -5.9 8.2e-05 3.6e+03 0.0014 2.3 -4 -
80 6.9 3.9 0.43 0.18 0.59 2.1 0.52 -0.54 -1.1 -0.075 -0.46 -3.9 4.2 -5.9 8.2e-05 3.6e+03 0.0014 1.2 -1.8 -
81 6.9 3.9 0.43 0.18 0.59 2.1 0.52 -0.54 -1.1 -0.075 -0.46 -3.9 4.2 -5.9 8.2e-05 3.6e+03 0.0014 0.58 -1.2 -
82 6.9 3.9 0.43 0.18 0.59 2.1 0.52 -0.54 -1.1 -0.075 -0.46 -3.9 4.2 -5.9 8.2e-05 3.6e+03 0.0014 0.29 -0.099 -
83 7.1 3.8 0.4 0.32 0.44 2.2 0.33 -0.83 -1.2 -0.064 -0.73 -3.9 4.2 -6.1 8.8e-05 3.6e+03 0.0017 0.29 0.51 +
84 7.3 3.8 0.35 0.25 0.31 2.2 0.23 -0.71 -1.5 -0.086 -0.57 -3.9 4.2 -6 9.6e-05 3.6e+03 0.0013 2.9 1.1 ++
85 7.3 3.8 0.35 0.25 0.31 2.2 0.23 -0.71 -1.5 -0.086 -0.57 -3.9 4.2 -6 9.6e-05 3.6e+03 0.0013 1.5 -0.28 -
86 7.3 3.8 0.35 0.25 0.31 2.2 0.23 -0.71 -1.5 -0.086 -0.57 -3.9 4.2 -6 9.6e-05 3.6e+03 0.0013 0.73 -0.74 -
87 8 3.6 0.062 0.23 -0.29 2.1 -0.19 -0.78 -2.1 -0.17 -1.2 -3.8 4 -5.8 0.00012 3.6e+03 0.0043 0.73 0.62 +
88 8.7 3.7 -0.6 0.34 -0.92 2.3 -0.49 -0.8 -2.9 -0.12 -1.2 -4 4.2 -6 0.00014 3.6e+03 0.0036 7.3 1.2 ++
89 11 3.4 -3.3 0.63 -1.7 2.4 -1.1 -1.4 -4.9 -0.19 -1.6 -4.2 4.3 -6.1 0.00023 3.6e+03 0.0068 7.3 0.48 +
90 12 3.3 -2.6 0.6 -0.77 2.2 -1.3 -1.7 -6.3 -0.35 -2.3 -4.1 4.1 -5.9 0.00025 3.6e+03 0.003 73 0.9 ++
91 12 3.3 -2.6 0.6 -0.77 2.2 -1.3 -1.7 -6.3 -0.35 -2.3 -4.1 4.1 -5.9 0.00025 3.6e+03 0.003 1.7 -1.5 -
92 12 3.3 -2.6 0.6 -0.77 2.2 -1.3 -1.7 -6.3 -0.35 -2.3 -4.1 4.1 -5.9 0.00025 3.6e+03 0.003 0.85 -1.1 -
93 12 3.3 -2.6 0.6 -0.77 2.2 -1.3 -1.7 -6.3 -0.35 -2.3 -4.1 4.1 -5.9 0.00025 3.6e+03 0.003 0.43 -0.96 -
94 12 3.3 -2.6 0.6 -0.77 2.2 -1.3 -1.7 -6.3 -0.35 -2.3 -4.1 4.1 -5.9 0.00025 3.6e+03 0.003 0.21 -0.53 -
95 12 3.5 -2.6 0.53 -0.79 2 -1.3 -1.6 -6.3 -0.4 -2.3 -4.1 4.1 -5.9 0.00026 3.6e+03 0.01 0.21 0.62 +
96 12 3.5 -2.7 0.46 -0.94 1.9 -1.2 -1.8 -6.1 -0.42 -2.2 -4.1 3.9 -5.6 0.00028 3.6e+03 0.0044 0.21 0.88 +
97 12 3.4 -3 0.53 -1.1 1.9 -1.2 -1.6 -6 -0.32 -2.1 -4 3.9 -5.6 0.00029 3.6e+03 0.0042 2.1 1.1 ++
98 13 3.5 -4.4 0.54 -1.3 1.8 -1.2 -1.7 -5.3 -0.32 -2.1 -4.2 4 -5.7 0.00033 3.6e+03 0.0029 2.1 0.7 +
99 13 3.5 -4.6 0.56 -0.87 1.8 -0.23 -1.7 -5.2 -0.3 -2.5 -4.2 4.1 -5.8 0.00035 3.6e+03 0.0027 21 1.4 ++
100 13 3.5 -4.6 0.56 -0.87 1.8 -0.23 -1.7 -5.2 -0.3 -2.5 -4.2 4.1 -5.8 0.00035 3.6e+03 0.0027 0.95 -0.55 -
101 14 3.3 -4.7 0.61 0.081 1.7 -0.23 -1.8 -4.8 -0.35 -2.5 -3.9 4 -5.7 0.00039 3.6e+03 0.0035 0.95 0.69 +
102 14 3.4 -4.9 0.54 -0.095 1.6 -0.23 -1.8 -4.7 -0.37 -2.5 -3.9 3.9 -5.6 0.00042 3.6e+03 0.0013 9.5 1.4 ++
103 14 3.4 -4.9 0.54 -0.095 1.6 -0.23 -1.8 -4.7 -0.37 -2.5 -3.9 3.9 -5.6 0.00042 3.6e+03 0.0013 0.31 -0.4 -
104 14 3.4 -4.9 0.54 -0.095 1.6 -0.23 -1.8 -4.7 -0.37 -2.5 -3.9 3.9 -5.6 0.00042 3.6e+03 0.0013 0.15 -0.033 -
105 14 3.4 -5 0.53 -0.1 1.5 -0.24 -2 -4.7 -0.39 -2.3 -4 3.9 -5.6 0.00047 3.6e+03 0.0025 1.5 1.2 ++
106 14 3.4 -5 0.53 -0.1 1.5 -0.24 -2 -4.7 -0.39 -2.3 -4 3.9 -5.6 0.00047 3.6e+03 0.0025 0.36 -0.2 -
107 14 3.6 -5.1 0.51 -0.039 1.5 -0.23 -2 -4.3 -0.33 -2.3 -4.2 4 -5.8 0.00051 3.6e+03 0.0022 0.36 0.53 +
108 15 3.6 -5 0.51 0.069 1.3 -0.59 -2.1 -4.7 -0.37 -2.3 -4.1 4 -5.7 0.00058 3.6e+03 0.0024 3.6 1.1 ++
109 15 3.5 -4.9 0.52 -0.1 1.2 -0.072 -2.1 -4.9 -0.35 -2.4 -4 4 -5.7 0.00066 3.6e+03 0.00087 36 1 ++
110 15 3.5 -4.9 0.52 -0.1 1.2 -0.072 -2.1 -4.9 -0.35 -2.4 -4 4 -5.7 0.00066 3.6e+03 0.00087 1.4 -0.63 -
111 15 3.5 -4.9 0.52 -0.1 1.2 -0.072 -2.1 -4.9 -0.35 -2.4 -4 4 -5.7 0.00066 3.6e+03 0.00087 0.72 -0.37 -
112 16 3.7 -4.6 0.54 -0.43 1.2 -0.68 -2 -4.9 -0.22 -2.4 -4.1 4.2 -5.9 0.00081 3.6e+03 0.0022 0.72 0.64 +
113 16 3.8 -4.6 0.48 0.26 0.96 -0.17 -2 -4.6 -0.17 -2.3 -4.1 4.2 -5.9 0.001 3.6e+03 0.0052 0.72 0.88 +
114 16 3.8 -4.6 0.48 0.26 0.96 -0.17 -2 -4.6 -0.17 -2.3 -4.1 4.2 -5.9 0.001 3.6e+03 0.0052 0.36 -0.56 -
115 16 3.8 -4.6 0.47 0.016 0.65 -0.13 -2.4 -4.5 -0.28 -2.6 -3.8 4.2 -5.9 0.0014 3.6e+03 0.0045 0.36 0.2 +
116 16 3.7 -4.4 0.57 -0.23 0.99 -0.32 -2.2 -4.9 -0.22 -2.6 -4 4.2 -6 0.0013 3.6e+03 0.0054 0.36 0.58 +
117 16 3.8 -4.5 0.56 -0.074 0.9 -0.26 -2.2 -4.8 -0.17 -2.4 -4.1 4.3 -6 0.0014 3.6e+03 0.0037 3.6 1.2 ++
118 16 3.7 -4.6 0.55 0.005 0.78 -0.38 -2.2 -4.8 -0.18 -2.3 -4 4.2 -6 0.0018 3.6e+03 0.0045 36 1.6 ++
119 17 3.6 -4.7 0.55 -0.15 0.68 -0.54 -2.4 -4.7 -0.26 -2.3 -4 4.2 -5.9 0.0025 3.6e+03 0.0025 3.6e+02 1.3 ++
120 17 3.6 -4.1 0.58 -0.12 0.68 -0.36 -2.4 -4.6 -0.23 -2.3 -4.1 4.2 -5.9 0.0034 3.6e+03 0.0014 3.6e+03 1.6 ++
121 16 3.6 -2.6 0.63 0.081 0.83 -0.41 -2.3 -3.1 -0.16 -1.5 -4.1 4.3 -6.1 0.0063 3.6e+03 0.0043 3.6e+04 1 ++
122 17 3.6 -1.1 0.52 -0.069 0.88 -0.72 -2.2 -3.6 -0.26 -1.5 -4.2 4.2 -6 0.0088 3.6e+03 0.003 3.6e+05 0.98 ++
123 17 3.5 -1.3 0.5 -0.13 0.97 -0.39 -2.2 -3.7 -0.32 -1.6 -4.2 4.1 -5.8 0.011 3.6e+03 0.0024 3.6e+06 1.7 ++
124 17 3.3 -1.9 0.46 -0.58 1.4 0.43 -2 -3.4 -0.39 -1.7 -4.1 3.9 -5.6 0.019 3.6e+03 0.0022 3.6e+07 1.3 ++
125 17 3.3 -1.9 0.46 -0.58 1.4 0.43 -2 -3.4 -0.39 -1.7 -4.1 3.9 -5.6 0.019 3.6e+03 0.0022 23 -15 -
126 17 3.3 -1.9 0.46 -0.58 1.4 0.43 -2 -3.4 -0.39 -1.7 -4.1 3.9 -5.6 0.019 3.6e+03 0.0022 12 -16 -
127 17 3.3 -1.9 0.46 -0.58 1.4 0.43 -2 -3.4 -0.39 -1.7 -4.1 3.9 -5.6 0.019 3.6e+03 0.0022 5.8 -16 -
128 17 3.3 -1.9 0.46 -0.58 1.4 0.43 -2 -3.4 -0.39 -1.7 -4.1 3.9 -5.6 0.019 3.6e+03 0.0022 2.9 -7.2 -
129 17 3.3 -1.9 0.46 -0.58 1.4 0.43 -2 -3.4 -0.39 -1.7 -4.1 3.9 -5.6 0.019 3.6e+03 0.0022 1.5 -3.3 -
130 17 3.3 -1.9 0.46 -0.58 1.4 0.43 -2 -3.4 -0.39 -1.7 -4.1 3.9 -5.6 0.019 3.6e+03 0.0022 0.73 -0.29 -
131 17 3.4 -1.8 0.57 -1.3 1.8 0.85 -1.8 -3.1 -0.28 -1.6 -4.2 4 -5.8 0.03 3.6e+03 0.0094 0.73 0.47 +
132 17 3.4 -1.8 0.57 -1.3 1.8 0.85 -1.8 -3.1 -0.28 -1.6 -4.2 4 -5.8 0.03 3.6e+03 0.0094 0.36 -0.31 -
133 17 3.6 -1.5 0.71 -1.1 1.6 0.49 -2 -3.3 -0.23 -1.7 -4.3 4.2 -6.1 0.031 3.6e+03 0.01 3.6 1.3 ++
134 17 3.6 -1.5 0.71 -1.1 1.6 0.49 -2 -3.3 -0.23 -1.7 -4.3 4.2 -6.1 0.031 3.6e+03 0.01 1.8 -13 -
135 17 3.6 -1.5 0.71 -1.1 1.6 0.49 -2 -3.3 -0.23 -1.7 -4.3 4.2 -6.1 0.031 3.6e+03 0.01 0.91 -8.3 -
136 17 3.6 -1.5 0.71 -1.1 1.6 0.49 -2 -3.3 -0.23 -1.7 -4.3 4.2 -6.1 0.031 3.6e+03 0.01 0.45 -5.7 -
137 17 3.6 -1.5 0.71 -1.1 1.6 0.49 -2 -3.3 -0.23 -1.7 -4.3 4.2 -6.1 0.031 3.6e+03 0.01 0.23 -1.5 -
138 17 3.5 -1.6 0.68 -0.98 1.5 0.49 -1.9 -3.2 -0.28 -1.9 -4.4 4.2 -6 0.033 3.6e+03 0.012 0.23 0.17 +
139 17 3.5 -1.6 0.59 -1.2 1.5 0.53 -1.9 -3.2 -0.21 -1.7 -4.4 4.2 -6 0.036 3.6e+03 0.0032 0.23 0.88 +
140 17 3.7 -1.6 0.63 -1.3 1.4 0.31 -1.9 -3.4 -0.11 -1.7 -4.6 4.4 -6.2 0.039 3.6e+03 0.0026 0.23 0.4 +
141 16 3.6 -1.5 0.63 -1.2 1.4 0.46 -1.9 -3.4 -0.15 -1.7 -4.5 4.3 -6.1 0.039 3.6e+03 0.0036 2.3 1.1 ++
142 16 3.6 -1.5 0.63 -1.2 1.4 0.46 -1.9 -3.4 -0.15 -1.7 -4.5 4.3 -6.1 0.039 3.6e+03 0.0036 1.1 -1.4 -
143 16 3.6 -1.5 0.63 -1.2 1.4 0.46 -1.9 -3.4 -0.15 -1.7 -4.5 4.3 -6.1 0.039 3.6e+03 0.0036 0.57 -0.89 -
144 16 3.6 -1.5 0.63 -1.2 1.4 0.46 -1.9 -3.4 -0.15 -1.7 -4.5 4.3 -6.1 0.039 3.6e+03 0.0036 0.28 -1.2 -
145 16 3.6 -1.5 0.63 -1.2 1.4 0.46 -1.9 -3.4 -0.15 -1.7 -4.5 4.3 -6.1 0.039 3.6e+03 0.0036 0.14 -0.37 -
146 16 3.5 -1.5 0.62 -1.4 1.5 0.46 -1.8 -3.3 -0.15 -1.8 -4.5 4.3 -6 0.042 3.6e+03 0.0032 0.14 0.38 +
147 16 3.5 -1.4 0.63 -1.3 1.4 0.38 -1.8 -3.3 -0.15 -1.7 -4.5 4.3 -6.1 0.043 3.6e+03 0.0011 1.4 1.4 ++
148 15 3.5 -1.6 0.68 -1.7 1.4 -0.15 -1.8 -2.8 -0.14 -1.6 -4.5 4.3 -6.1 0.049 3.6e+03 0.0016 14 2.2 ++
149 15 3.5 -1.6 0.68 -1.7 1.4 -0.15 -1.8 -2.8 -0.14 -1.6 -4.5 4.3 -6.1 0.049 3.6e+03 0.0016 2.2 -1.2 -
150 15 3.5 -1.6 0.68 -1.7 1.4 -0.15 -1.8 -2.8 -0.14 -1.6 -4.5 4.3 -6.1 0.049 3.6e+03 0.0016 1.1 -0.036 -
151 14 3.5 -0.81 0.73 -2.8 1.6 -1.1 -1.7 -2.5 -0.18 -1.8 -4.6 4.3 -6.1 0.069 3.6e+03 0.005 1.1 0.66 +
152 13 3.5 -0.46 0.78 -3.4 1.7 -0.77 -1.7 -2.7 -0.19 -2 -4.7 4.4 -6.3 0.089 3.6e+03 0.0012 1.1 0.76 +
153 12 3.5 -1 0.79 -2.9 1.5 -0.92 -1.8 -1.9 -0.16 -1.7 -4.7 4.4 -6.3 0.083 3.6e+03 0.0018 1.1 0.61 +
154 11 3.4 -0.8 0.74 -2.4 1.5 -0.38 -1.9 -1.9 -0.29 -1.5 -4.6 4.3 -6.1 0.079 3.6e+03 0.0014 11 1.2 ++
155 11 3.4 -0.8 0.74 -2.4 1.5 -0.38 -1.9 -1.9 -0.29 -1.5 -4.6 4.3 -6.1 0.079 3.6e+03 0.0014 5.6 -5.8 -
156 11 3.4 -0.8 0.74 -2.4 1.5 -0.38 -1.9 -1.9 -0.29 -1.5 -4.6 4.3 -6.1 0.079 3.6e+03 0.0014 2.8 -1.1 -
157 11 3.4 -0.8 0.74 -2.4 1.5 -0.38 -1.9 -1.9 -0.29 -1.5 -4.6 4.3 -6.1 0.079 3.6e+03 0.0014 1.4 -1.4 -
158 11 3.4 -0.8 0.74 -2.4 1.5 -0.38 -1.9 -1.9 -0.29 -1.5 -4.6 4.3 -6.1 0.079 3.6e+03 0.0014 0.7 -1.1 -
159 11 3.4 -0.8 0.74 -2.4 1.5 -0.38 -1.9 -1.9 -0.29 -1.5 -4.6 4.3 -6.1 0.079 3.6e+03 0.0014 0.35 -0.041 -
160 11 3.4 -0.77 0.74 -2.7 1.6 -0.47 -1.9 -1.7 -0.38 -1.6 -4.6 4.3 -6.2 0.086 3.6e+03 0.0015 0.35 0.68 +
161 10 3.4 -0.69 0.76 -2.6 1.6 -0.23 -2 -1.8 -0.38 -1.6 -4.6 4.3 -6.2 0.087 3.6e+03 0.0025 3.5 0.97 ++
162 10 3.4 -0.69 0.76 -2.6 1.6 -0.23 -2 -1.8 -0.38 -1.6 -4.6 4.3 -6.2 0.087 3.6e+03 0.0025 1.7 -1.2 -
163 10 3.4 -0.69 0.76 -2.6 1.6 -0.23 -2 -1.8 -0.38 -1.6 -4.6 4.3 -6.2 0.087 3.6e+03 0.0025 0.87 -0.082 -
164 9.6 3.4 -0.48 0.76 -2.3 1.6 -0.22 -2 -1.4 -0.42 -1.3 -4.6 4.3 -6.1 0.085 3.6e+03 0.0042 0.87 0.68 +
165 8.7 3.4 -0.73 0.77 -2.5 1.6 -0.22 -2 -1.7 -0.45 -1.4 -4.6 4.3 -6.1 0.088 3.6e+03 0.0038 8.7 1.1 ++
166 8.7 3.4 -0.73 0.77 -2.5 1.6 -0.22 -2 -1.7 -0.45 -1.4 -4.6 4.3 -6.1 0.088 3.6e+03 0.0038 1.6 -1.9 -
167 8.7 3.4 -0.73 0.77 -2.5 1.6 -0.22 -2 -1.7 -0.45 -1.4 -4.6 4.3 -6.1 0.088 3.6e+03 0.0038 0.81 -0.59 -
168 7.9 3.5 -1.5 0.84 -2.9 1.7 0.07 -2.1 -2 -0.42 -1.7 -4.9 4.5 -6.6 0.1 3.6e+03 0.0061 8.1 1.2 ++
169 7.9 3.5 -1.5 0.84 -2.9 1.7 0.07 -2.1 -2 -0.42 -1.7 -4.9 4.5 -6.6 0.1 3.6e+03 0.0061 4 -18 -
170 7.9 3.5 -1.5 0.84 -2.9 1.7 0.07 -2.1 -2 -0.42 -1.7 -4.9 4.5 -6.6 0.1 3.6e+03 0.0061 2 -3.9 -
171 7.9 3.5 -1.5 0.84 -2.9 1.7 0.07 -2.1 -2 -0.42 -1.7 -4.9 4.5 -6.6 0.1 3.6e+03 0.0061 1 -1.4 -
172 6.9 3.4 -1.6 0.81 -3.2 1.7 -0.0093 -2.1 -2.2 -0.51 -1.8 -4.8 4.4 -6.4 0.11 3.6e+03 0.0085 1 0.17 +
173 6.9 3.4 -1.6 0.81 -3.2 1.7 -0.0093 -2.1 -2.2 -0.51 -1.8 -4.8 4.4 -6.4 0.11 3.6e+03 0.0085 0.5 -1.1 -
174 6.8 3.2 -1.1 0.76 -2.8 1.6 0.28 -2.2 -1.7 -0.59 -1.5 -4.6 4.2 -6.1 0.1 3.6e+03 0.014 0.5 0.89 +
175 6.7 3.5 -1.6 0.76 -2.4 1.5 0.26 -2.3 -1.7 -0.49 -1.3 -4.7 4.4 -6.5 0.1 3.6e+03 0.015 0.5 0.52 +
176 6.5 3.5 -1.6 0.72 -2.9 1.3 0.0081 -2.4 -1.6 -0.53 -1.2 -4.7 4.2 -6.5 0.12 3.6e+03 0.012 5 1.1 ++
177 6.5 3.5 -1.6 0.72 -2.9 1.3 0.0081 -2.4 -1.6 -0.53 -1.2 -4.7 4.2 -6.5 0.12 3.6e+03 0.012 2.5 -2.8 -
178 6.5 3.5 -1.6 0.72 -2.9 1.3 0.0081 -2.4 -1.6 -0.53 -1.2 -4.7 4.2 -6.5 0.12 3.6e+03 0.012 1.3 -0.14 -
179 5.2 3.3 -1.7 0.72 -2.6 0.84 0.79 -2.7 -0.91 -0.69 -0.91 -4.5 3.7 -6.3 0.14 3.5e+03 0.011 1.3 0.17 +
180 4.5 3.3 -2.2 0.67 -2.5 0.71 0.21 -2.7 -1.3 -0.65 -0.76 -4.4 3.4 -6.1 0.13 3.5e+03 0.019 13 0.91 ++
181 4.5 3.3 -2.2 0.67 -2.5 0.71 0.21 -2.7 -1.3 -0.65 -0.76 -4.4 3.4 -6.1 0.13 3.5e+03 0.019 1.6 -5.2 -
182 4.5 3.3 -2.2 0.67 -2.5 0.71 0.21 -2.7 -1.3 -0.65 -0.76 -4.4 3.4 -6.1 0.13 3.5e+03 0.019 0.79 -0.22 -
183 4.4 3.6 -3 0.73 -2.6 0.69 0.12 -2.4 -1.1 -0.44 -0.6 -4.5 3.2 -6.4 0.14 3.5e+03 0.013 0.79 0.26 +
184 4.2 3.5 -3.8 0.93 -3.2 1.1 0.73 -2.2 -0.96 -0.38 -0.84 -4.8 3.4 -6.8 0.16 3.5e+03 0.0036 0.79 0.51 +
185 5 3.2 -3.6 0.92 -3 1.2 0.81 -2.2 -1.3 -0.44 -1.2 -4.5 3.4 -6.5 0.15 3.5e+03 0.0047 0.79 0.81 +
186 4.9 3.2 -4 0.98 -2.9 1.1 1.6 -2.1 -1.3 -0.27 -1.3 -4.6 3.4 -6.7 0.15 3.5e+03 0.0054 0.79 0.75 +
187 4.9 3.2 -4 0.98 -2.9 1.1 1.6 -2.1 -1.3 -0.27 -1.3 -4.6 3.4 -6.7 0.15 3.5e+03 0.0054 0.39 -1.2 -
188 4.9 3.2 -4 0.98 -2.9 1.1 1.6 -2.1 -1.3 -0.27 -1.3 -4.6 3.4 -6.7 0.15 3.5e+03 0.0054 0.2 0.039 -
189 5 3.1 -4.2 0.92 -3 1.2 1.5 -2.1 -1.1 -0.26 -1.2 -4.4 3.4 -6.8 0.15 3.5e+03 0.0014 0.2 0.69 +
190 5.2 3.1 -4.1 0.93 -2.9 1.3 1.6 -2.1 -1.2 -0.26 -1.2 -4.5 3.4 -6.7 0.15 3.5e+03 0.0014 2 0.93 ++
191 5.2 3.1 -4.1 0.93 -2.9 1.3 1.6 -2.1 -1.2 -0.26 -1.2 -4.5 3.4 -6.7 0.15 3.5e+03 0.0014 0.98 -3 -
192 5.2 3.1 -4.1 0.93 -2.9 1.3 1.6 -2.1 -1.2 -0.26 -1.2 -4.5 3.4 -6.7 0.15 3.5e+03 0.0014 0.49 -0.31 -
193 5.4 3 -4.6 1.1 -2.9 1.4 2.1 -1.9 -1.2 -0.17 -1.4 -4.5 3.3 -6.7 0.15 3.5e+03 0.0018 0.49 0.68 +
194 5.4 3 -4.6 1.1 -2.9 1.4 2.1 -1.9 -1.2 -0.17 -1.4 -4.5 3.3 -6.7 0.15 3.5e+03 0.0018 0.25 -0.91 -
195 5.3 3.1 -4.7 1 -2.6 1.4 2.2 -1.9 -1.2 -0.17 -1.3 -4.5 3.4 -6.9 0.15 3.5e+03 0.00076 0.25 0.9 +
196 5.3 3.1 -4.7 1 -2.6 1.4 2.2 -1.9 -1.2 -0.17 -1.3 -4.5 3.4 -6.9 0.15 3.5e+03 0.00076 0.12 -0.14 -
197 5.3 3.1 -4.8 1 -2.7 1.4 2.1 -2 -1.3 -0.24 -1.4 -4.5 3.5 -6.9 0.15 3.5e+03 0.001 0.12 0.74 +
198 5.3 3.1 -4.8 1 -2.6 1.5 2.2 -2 -1.3 -0.21 -1.5 -4.5 3.5 -6.9 0.15 3.5e+03 0.00074 0.12 0.61 +
199 5.3 3.1 -5 1 -2.6 1.4 2.2 -2 -1.4 -0.2 -1.5 -4.6 3.4 -6.9 0.15 3.5e+03 0.00089 0.12 0.7 +
200 5.4 3.1 -5 1 -2.7 1.4 2.2 -2 -1.4 -0.19 -1.5 -4.5 3.5 -6.9 0.15 3.5e+03 0.001 1.2 0.96 ++
201 5.4 3.1 -5 1 -2.7 1.4 2.2 -2 -1.4 -0.19 -1.5 -4.5 3.5 -6.9 0.15 3.5e+03 0.001 0.61 -5.8 -
202 5.9 3.1 -5.6 1.1 -2.7 1.4 2.2 -2 -1.6 -0.12 -1.7 -4.5 3.4 -7 0.15 3.5e+03 0.003 0.61 0.69 +
203 5.9 3.1 -5.6 1.1 -2.7 1.4 2.2 -2 -1.6 -0.12 -1.7 -4.5 3.4 -7 0.15 3.5e+03 0.003 0.31 -5.8 -
204 5.9 3.1 -5.6 1.1 -2.7 1.4 2.2 -2 -1.6 -0.12 -1.7 -4.5 3.4 -7 0.15 3.5e+03 0.003 0.15 -1 -
205 6 3.1 -5.7 1.1 -2.9 1.4 2.3 -2 -1.6 -0.12 -1.8 -4.6 3.5 -7 0.16 3.5e+03 0.0028 0.15 0.25 +
206 6 3.1 -5.7 1.1 -2.9 1.4 2.3 -2 -1.6 -0.12 -1.8 -4.6 3.5 -7 0.16 3.5e+03 0.0028 0.077 -0.46 -
207 6 3.1 -5.7 1.1 -2.8 1.4 2.3 -2 -1.7 -0.14 -1.7 -4.6 3.5 -7 0.15 3.5e+03 0.0017 0.77 1.1 ++
208 6 3.1 -5.7 1.1 -2.8 1.4 2.3 -2 -1.7 -0.14 -1.7 -4.6 3.5 -7 0.15 3.5e+03 0.0017 0.16 -3.2 -
209 6.1 3.1 -5.8 1.1 -2.9 1.4 2.3 -2 -1.7 -0.13 -1.8 -4.5 3.4 -6.9 0.15 3.5e+03 0.0013 0.16 0.7 +
210 6.1 3.1 -5.8 1.1 -2.9 1.4 2.3 -2 -1.7 -0.13 -1.8 -4.5 3.4 -6.9 0.15 3.5e+03 0.0013 0.078 -2.2 -
211 6.1 3.1 -5.8 1.1 -2.9 1.4 2.3 -2 -1.7 -0.13 -1.8 -4.5 3.4 -6.9 0.15 3.5e+03 0.0013 0.039 0.019 -
212 6.1 3.1 -5.9 1 -2.9 1.4 2.3 -2 -1.7 -0.16 -1.8 -4.5 3.4 -6.9 0.15 3.5e+03 0.0011 0.039 0.88 +
213 6.1 3.1 -5.9 1 -2.9 1.4 2.3 -2 -1.7 -0.17 -1.8 -4.5 3.4 -6.9 0.16 3.5e+03 0.00041 0.39 1.5 ++
214 6.1 3.1 -5.9 1 -2.9 1.4 2.3 -2 -1.7 -0.17 -1.8 -4.5 3.4 -6.9 0.16 3.5e+03 0.00041 0.19 -8 -
215 6.1 3.1 -5.9 1 -2.9 1.4 2.3 -2 -1.7 -0.17 -1.8 -4.5 3.4 -6.9 0.16 3.5e+03 0.00041 0.097 -3.4 -
216 6.1 3.1 -5.9 1 -2.9 1.4 2.3 -2 -1.7 -0.17 -1.8 -4.5 3.4 -6.9 0.16 3.5e+03 0.00041 0.049 -0.28 -
217 6.2 3 -5.9 1.1 -2.9 1.4 2.4 -2 -1.7 -0.17 -1.8 -4.5 3.4 -6.8 0.16 3.5e+03 0.0004 0.049 0.66 +
218 6.2 3.1 -5.9 1.1 -2.9 1.4 2.3 -2 -1.7 -0.16 -1.8 -4.5 3.4 -6.8 0.16 3.5e+03 0.0004 0.049 0.16 +
219 6.2 3.1 -6 1 -2.9 1.4 2.4 -2 -1.7 -0.21 -1.8 -4.5 3.4 -6.8 0.16 3.5e+03 0.0003 0.049 0.29 +
220 6.2 3.1 -6 1.1 -2.9 1.4 2.4 -2 -1.7 -0.16 -1.8 -4.5 3.4 -6.8 0.16 3.5e+03 0.00018 0.049 0.54 +
221 6.2 3.1 -6 1.1 -2.9 1.4 2.4 -2 -1.7 -0.16 -1.8 -4.5 3.4 -6.8 0.16 3.5e+03 0.00018 0.024 -9.6e-05 -
222 6.2 3.1 -6 1.1 -2.9 1.4 2.4 -2 -1.7 -0.17 -1.8 -4.5 3.4 -6.8 0.16 3.5e+03 0.00017 0.024 0.74 +
223 6.3 3.1 -6 1.1 -2.9 1.4 2.4 -2 -1.7 -0.17 -1.8 -4.5 3.4 -6.8 0.16 3.5e+03 0.00017 0.24 1.1 ++
224 6.3 3.1 -6 1.1 -2.9 1.4 2.4 -2 -1.7 -0.17 -1.8 -4.5 3.4 -6.8 0.16 3.5e+03 0.00017 0.12 -3.1 -
225 6.3 3.1 -6 1.1 -2.9 1.4 2.4 -2 -1.7 -0.17 -1.8 -4.5 3.4 -6.8 0.16 3.5e+03 0.00017 0.061 -1.1 -
226 6.3 3.1 -6 1.1 -2.9 1.4 2.4 -2 -1.7 -0.17 -1.8 -4.5 3.4 -6.8 0.16 3.5e+03 0.00017 0.03 -0.0069 -
227 6.3 3 -6.1 1.1 -2.9 1.4 2.4 -2 -1.7 -0.17 -1.8 -4.5 3.4 -6.8 0.16 3.5e+03 0.00017 0.03 0.65 +
228 6.3 3.1 -6.1 1.1 -2.9 1.4 2.4 -2 -1.8 -0.17 -1.8 -4.5 3.4 -6.8 0.16 3.5e+03 0.00017 0.03 0.76 +
229 6.3 3.1 -6.1 1.1 -2.9 1.4 2.4 -2 -1.8 -0.17 -1.8 -4.5 3.4 -6.8 0.16 3.5e+03 7.3e-05 0.03 0.44 +
Results saved in file b15panel_discrete_bis.html
Results saved in file b15panel_discrete_bis.pickle
print(results.short_summary())
Results for model b15panel_discrete_bis
Nbr of parameters: 15
Sample size: 752
Observations: 6768
Excluded data: 3960
Final log likelihood: -3538.404
Akaike Information Criterion: 7106.808
Bayesian Information Criterion: 7176.149
pandas_results = results.get_estimated_parameters()
pandas_results
Total running time of the script: (4 minutes 9.944 seconds)