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.
- author:
Michel Bierlaire, EPFL
- date:
Mon Apr 10 11:53:06 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.py')
Example b15panel_discrete.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 membership 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 = [
PanelLikelihoodTrajectory(models.logit(V[i], av, CHOICE))
for i in range(NUMBER_OF_CLASSES)
]
Conditional to the random variables, likelihood for the individual.
prob_indiv = prob_class0 * prob[0] + prob_class1 * prob[1]
We integrate over the random variables using Monte-Carlo.
logprob = log(MonteCarlo(prob_indiv))
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'
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.iter
Cannot read file __b15panel_discrete.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 0 4e+03 0.033 1 0.46 +
1 2 2 1 1 2 2 2 2 -1 -1 -1 -1 2 -1 0 4e+03 0.033 0.5 0.029 -
2 2 1.5 1 0.5 2 1.5 2 1.5 -1 -1.5 -1 -1.5 2.5 -1.5 0 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 2.9e-05 3.6e+03 0.009 0.16 0.6 +
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 2.9e-05 3.6e+03 0.009 0.078 -0.072 -
18 2.4 3.7 0.73 0.42 1.3 1.5 2.5 2 -0.56 -0.28 -0.79 -3.3 4 -5.7 2.9e-05 3.6e+03 0.0018 0.078 0.77 +
19 2.4 3.7 0.73 0.42 1.3 1.5 2.5 2 -0.56 -0.28 -0.79 -3.3 4 -5.7 2.9e-05 3.6e+03 0.0018 0.039 -0.6 -
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 0.0029 0.039 0.78 +
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 0.0029 0.02 -0.18 -
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 0.005 0.02 0.22 +
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 2.9e-05 3.6e+03 0.00048 0.2 1 ++
24 2.6 3.7 0.63 0.42 1.3 1.7 2.5 1.8 -0.44 -0.24 -0.72 -3.4 4.1 -5.8 3e-05 3.6e+03 0.003 2 2.6 ++
25 2.6 3.7 0.63 0.42 1.3 1.7 2.5 1.8 -0.44 -0.24 -0.72 -3.4 4.1 -5.8 3e-05 3.6e+03 0.003 0.98 -3.2 -
26 2.6 3.7 0.63 0.42 1.3 1.7 2.5 1.8 -0.44 -0.24 -0.72 -3.4 4.1 -5.8 3e-05 3.6e+03 0.003 0.49 -0.79 -
27 2.7 3.7 0.58 0.37 1.3 2.1 2.5 1.4 -0.39 -0.23 -0.72 -3.4 4.1 -5.8 3.2e-05 3.6e+03 0.0069 4.9 0.95 ++
28 2.7 3.7 0.58 0.37 1.3 2.1 2.5 1.4 -0.39 -0.23 -0.72 -3.4 4.1 -5.8 3.2e-05 3.6e+03 0.0069 2.4 -9.2 -
29 2.7 3.7 0.58 0.37 1.3 2.1 2.5 1.4 -0.39 -0.23 -0.72 -3.4 4.1 -5.8 3.2e-05 3.6e+03 0.0069 1.2 -5.8 -
30 2.7 3.7 0.58 0.37 1.3 2.1 2.5 1.4 -0.39 -0.23 -0.72 -3.4 4.1 -5.8 3.2e-05 3.6e+03 0.0069 0.61 -3.9 -
31 2.7 3.7 0.58 0.37 1.3 2.1 2.5 1.4 -0.39 -0.23 -0.72 -3.4 4.1 -5.8 3.2e-05 3.6e+03 0.0069 0.31 -0.15 -
32 2.8 3.7 0.53 0.2 1.3 2.1 2.5 1.1 -0.34 -0.14 -0.71 -3.6 4.1 -5.9 3e-05 3.6e+03 0.012 0.31 0.65 +
33 2.9 3.9 0.51 0.36 1.3 1.9 2.5 0.79 -0.32 -0.25 -0.72 -3.7 4.2 -5.9 3.1e-05 3.6e+03 0.0043 0.31 0.22 +
34 2.9 4 0.5 0.33 1.3 2.1 2.5 0.58 -0.31 0.051 -0.72 -3.7 4.2 -5.9 3.2e-05 3.6e+03 0.023 0.31 0.41 +
35 2.9 3.9 0.47 0.25 1.3 2.1 2.5 0.27 -0.27 0.02 -0.73 -3.8 4.3 -6.1 3.1e-05 3.6e+03 0.0036 3.1 1 ++
36 2.9 3.9 0.47 0.25 1.3 2.1 2.5 0.27 -0.27 0.02 -0.73 -3.8 4.3 -6.1 3.1e-05 3.6e+03 0.0036 1.5 -6.6 -
37 2.9 3.9 0.47 0.25 1.3 2.1 2.5 0.27 -0.27 0.02 -0.73 -3.8 4.3 -6.1 3.1e-05 3.6e+03 0.0036 0.76 -2.7 -
38 2.9 3.9 0.47 0.25 1.3 2.1 2.5 0.27 -0.27 0.02 -0.73 -3.8 4.3 -6.1 3.1e-05 3.6e+03 0.0036 0.38 -0.63 -
39 2.9 3.9 0.47 0.25 1.3 2.1 2.5 0.27 -0.27 0.02 -0.73 -3.8 4.3 -6.1 3.1e-05 3.6e+03 0.0036 0.19 0.028 -
40 3 3.9 0.46 0.32 1.3 2.1 2.5 0.083 -0.26 -0.029 -0.7 -3.8 4.3 -6.1 3.1e-05 3.6e+03 0.0036 0.19 0.45 +
41 3.2 4 0.44 0.22 1.3 2.2 2.5 -0.11 -0.21 0.081 -0.64 -3.9 4.4 -6.2 3.1e-05 3.6e+03 0.0047 0.19 0.36 +
42 3.2 4 0.44 0.22 1.3 2.2 2.5 -0.11 -0.21 0.081 -0.64 -3.9 4.4 -6.2 3.1e-05 3.6e+03 0.0047 0.095 -0.22 -
43 3.2 3.9 0.44 0.22 1.3 2.2 2.5 -0.086 -0.2 -0.015 -0.61 -3.9 4.4 -6.2 3.1e-05 3.6e+03 0.00095 0.95 0.91 ++
44 3.2 3.9 0.44 0.22 1.3 2.2 2.5 -0.086 -0.2 -0.015 -0.61 -3.9 4.4 -6.2 3.1e-05 3.6e+03 0.00095 0.48 -5.5 -
45 3.2 3.9 0.44 0.22 1.3 2.2 2.5 -0.086 -0.2 -0.015 -0.61 -3.9 4.4 -6.2 3.1e-05 3.6e+03 0.00095 0.24 -3.6 -
46 3.2 3.9 0.44 0.22 1.3 2.2 2.5 -0.086 -0.2 -0.015 -0.61 -3.9 4.4 -6.2 3.1e-05 3.6e+03 0.00095 0.12 -1.9 -
47 3.2 3.9 0.44 0.22 1.3 2.2 2.5 -0.086 -0.2 -0.015 -0.61 -3.9 4.4 -6.2 3.1e-05 3.6e+03 0.00095 0.06 -0.24 -
48 3.2 3.9 0.44 0.22 1.3 2.1 2.5 -0.12 -0.19 -0.011 -0.59 -3.9 4.4 -6.2 3.1e-05 3.6e+03 0.001 0.06 0.53 +
49 3.3 4 0.44 0.22 1.3 2.1 2.4 -0.16 -0.18 -0.011 -0.56 -3.9 4.3 -6.2 3.2e-05 3.6e+03 0.0038 0.6 1.2 ++
50 3.3 4 0.44 0.22 1.3 2.1 2.4 -0.16 -0.18 -0.011 -0.56 -3.9 4.3 -6.2 3.2e-05 3.6e+03 0.0038 0.3 -0.67 -
51 3.6 3.9 0.43 0.19 1.2 2.2 2.4 -0.39 -0.11 0.039 -0.39 -3.9 4.3 -6.1 3.3e-05 3.6e+03 0.0038 0.3 0.44 +
52 3.9 3.9 0.4 0.19 1.1 2.1 2.4 -0.68 -0.07 0.017 -0.33 -3.9 4.3 -6.1 3.4e-05 3.6e+03 0.0053 0.3 0.1 +
53 3.9 3.9 0.4 0.19 1.1 2.1 2.4 -0.68 -0.07 0.017 -0.33 -3.9 4.3 -6.1 3.4e-05 3.6e+03 0.0053 0.15 -1.3 -
54 3.9 3.9 0.4 0.34 1.1 2.2 2.4 -0.57 -0.065 -0.091 -0.35 -3.9 4.3 -6.1 3.6e-05 3.6e+03 0.0041 0.15 0.16 +
55 4 3.8 0.4 0.19 1.1 2.2 2.3 -0.62 -0.067 -0.079 -0.38 -3.9 4.3 -6.1 3.5e-05 3.6e+03 0.0021 0.15 0.59 +
56 4 3.8 0.4 0.19 1.1 2.2 2.3 -0.62 -0.067 -0.079 -0.38 -3.9 4.3 -6.1 3.5e-05 3.6e+03 0.0021 0.075 -1.4 -
57 4 3.9 0.4 0.25 1.1 2.2 2.3 -0.63 -0.068 -0.033 -0.4 -3.9 4.2 -6.1 3.6e-05 3.6e+03 0.0022 0.075 0.45 +
58 4 3.9 0.4 0.25 1.1 2.2 2.3 -0.61 -0.071 -0.11 -0.41 -3.9 4.2 -6.1 3.7e-05 3.6e+03 0.0024 0.075 0.21 +
59 4 3.9 0.4 0.25 1.1 2.2 2.3 -0.61 -0.071 -0.11 -0.41 -3.9 4.2 -6.1 3.7e-05 3.6e+03 0.0024 0.037 0.081 -
60 4 3.8 0.4 0.23 1.1 2.2 2.3 -0.63 -0.071 -0.07 -0.42 -3.9 4.2 -6.1 3.7e-05 3.6e+03 0.00069 0.37 1 ++
61 4.4 3.8 0.41 0.27 1.1 2.2 2.1 -0.7 -0.1 -0.075 -0.57 -3.9 4.2 -6 3.9e-05 3.6e+03 0.0026 0.37 0.74 +
62 4.4 3.8 0.41 0.27 1.1 2.2 2.1 -0.7 -0.1 -0.075 -0.57 -3.9 4.2 -6 3.9e-05 3.6e+03 0.0026 0.19 -0.6 -
63 4.6 3.8 0.4 0.27 1.2 2.1 2 -0.64 -0.14 -0.04 -0.59 -3.8 4.2 -6 3.9e-05 3.6e+03 0.0012 0.19 0.37 +
64 4.6 3.8 0.4 0.27 1.2 2.1 2 -0.64 -0.14 -0.04 -0.59 -3.8 4.2 -6 3.9e-05 3.6e+03 0.0012 0.093 -0.85 -
65 4.6 3.8 0.4 0.21 1.2 2.2 2 -0.69 -0.14 -0.1 -0.59 -3.9 4.2 -6 4e-05 3.6e+03 0.0025 0.093 0.75 +
66 4.7 3.8 0.4 0.27 1.2 2.2 2 -0.64 -0.16 -0.063 -0.59 -3.9 4.2 -6 4.1e-05 3.6e+03 0.002 0.093 0.55 +
67 4.8 3.9 0.4 0.23 1.1 2.2 1.9 -0.68 -0.17 -0.057 -0.6 -3.9 4.2 -6 4.2e-05 3.6e+03 0.0013 0.093 0.8 +
68 4.9 3.8 0.4 0.26 1.1 2.2 1.9 -0.7 -0.2 -0.048 -0.62 -3.8 4.2 -6 4.4e-05 3.6e+03 0.0014 0.093 0.57 +
69 5 3.9 0.4 0.19 1.1 2.2 1.9 -0.61 -0.22 -0.068 -0.63 -3.9 4.2 -6 4.7e-05 3.6e+03 0.0012 0.093 0.83 +
70 5.1 3.9 0.4 0.26 1.1 2.1 1.8 -0.67 -0.24 -0.07 -0.64 -3.9 4.2 -6 5e-05 3.6e+03 0.0018 0.093 0.19 +
71 5.2 3.9 0.41 0.25 1.1 2.2 1.8 -0.57 -0.28 -0.044 -0.63 -3.9 4.2 -6.1 5.4e-05 3.6e+03 0.00048 0.93 1.4 ++
72 6.1 3.8 0.45 0.18 0.87 2.2 1.3 -0.52 -0.7 -0.064 -0.73 -3.8 4.2 -5.9 6.9e-05 3.6e+03 0.0016 9.3 1.2 ++
73 6.1 3.8 0.45 0.18 0.87 2.2 1.3 -0.52 -0.7 -0.064 -0.73 -3.8 4.2 -5.9 6.9e-05 3.6e+03 0.0016 1.3 -2.4 -
74 6.1 3.8 0.45 0.18 0.87 2.2 1.3 -0.52 -0.7 -0.064 -0.73 -3.8 4.2 -5.9 6.9e-05 3.6e+03 0.0016 0.63 -2 -
75 6.1 3.8 0.45 0.18 0.87 2.2 1.3 -0.52 -0.7 -0.064 -0.73 -3.8 4.2 -5.9 6.9e-05 3.6e+03 0.0016 0.32 -0.53 -
76 6.4 3.9 0.45 0.17 0.74 2.3 1.1 -0.41 -0.81 -0.0044 -0.67 -3.9 4.4 -6.2 8.5e-05 3.6e+03 0.0031 0.32 0.51 +
77 6.7 3.9 0.43 0.21 0.71 2.2 0.91 -0.62 -1 -0.087 -0.67 -3.9 4.3 -6.1 9.1e-05 3.6e+03 0.0013 3.2 1.3 ++
78 8.9 3.7 0.48 0.28 -1.1 2 -1.3 -0.45 -3.5 -0.014 -0.43 -3.8 4.1 -5.8 0.00016 3.6e+03 0.0039 3.2 0.23 +
79 12 3.7 -0.024 0.25 -1.4 2.1 -3.2 -0.53 -3.2 -0.047 -1.1 -3.9 4.1 -5.8 0.00019 3.6e+03 0.0034 3.2 0.64 +
80 13 3.8 -1.2 0.32 -0.94 2.3 -3 -1 -2.8 -0.15 -1.3 -4.1 4.2 -6 0.0002 3.6e+03 0.0053 32 1.4 ++
81 14 3.6 -1.8 0.58 -0.68 2.3 -2.8 -1.4 -2.8 -0.22 -2.2 -4.1 4.4 -6.2 0.00024 3.6e+03 0.013 32 0.25 +
82 15 3.4 -2.6 0.65 -1.1 2.2 -3 -1.6 -4.4 -0.25 -1.8 -4 4.2 -6 0.00027 3.6e+03 0.0091 3.2e+02 0.94 ++
83 16 3.3 -2.5 0.56 -0.6 2 -2.4 -2 -5.2 -0.4 -2.1 -4 3.9 -5.7 0.00029 3.6e+03 0.0046 3.2e+02 0.11 +
84 16 3.3 -2.5 0.56 -0.6 2 -2.4 -2 -5.2 -0.4 -2.1 -4 3.9 -5.7 0.00029 3.6e+03 0.0046 0.69 -1.1 -
85 16 3.3 -2.5 0.56 -0.6 2 -2.4 -2 -5.2 -0.4 -2.1 -4 3.9 -5.7 0.00029 3.6e+03 0.0046 0.35 -0.82 -
86 16 3.5 -2.5 0.35 -0.63 1.7 -2.4 -1.7 -5.2 -0.38 -2.1 -4 4 -5.7 0.0003 3.6e+03 0.0099 0.35 0.4 +
87 16 3.4 -2.9 0.56 -0.81 1.8 -2.3 -1.8 -5.1 -0.36 -2 -4 4 -5.8 0.00031 3.6e+03 0.0024 3.5 1.1 ++
88 16 3.4 -3.1 0.57 -0.71 1.8 -0.74 -1.9 -4.7 -0.4 -2 -4.1 3.9 -5.6 0.00032 3.6e+03 0.0063 35 0.9 ++
89 18 3.4 -3.5 0.52 -0.78 1.8 -0.088 -1.8 -4.2 -0.42 -2.5 -4.1 3.9 -5.6 0.00034 3.6e+03 0.0056 35 0.53 +
90 18 3.4 -3.7 0.52 -0.39 1.7 -0.18 -1.8 -4.9 -0.41 -2.4 -4.1 4 -5.7 0.00037 3.6e+03 0.0013 3.5e+02 1 ++
91 19 3.4 -4.5 0.54 0.29 1.7 -0.78 -1.9 -4.7 -0.41 -2.3 -4.1 4.1 -5.8 0.00039 3.6e+03 0.004 3.5e+02 0.1 +
92 19 3.4 -4.6 0.64 -0.2 1.6 -0.4 -2 -5.2 -0.39 -2.6 -4 4 -5.7 0.00042 3.6e+03 0.0034 3.5e+03 1 ++
93 19 3.4 -4.9 0.58 0.081 1.5 0.55 -2 -5.1 -0.39 -2.7 -4 3.9 -5.6 0.00045 3.6e+03 0.0025 3.5e+03 0.43 +
94 20 3.5 -5 0.48 0.026 1.4 -0.32 -1.9 -5.7 -0.38 -3 -4 3.9 -5.6 0.00049 3.6e+03 0.0018 3.5e+03 0.5 +
95 20 3.5 -5.4 0.53 0.064 1.5 -0.5 -2 -5.5 -0.38 -2.9 -4 4 -5.7 0.0005 3.6e+03 0.0015 3.5e+04 1.1 ++
96 20 3.5 -5.6 0.58 -0.13 1.4 -0.15 -2 -5.5 -0.34 -3 -4 4 -5.7 0.00054 3.6e+03 0.0011 3.5e+05 1.3 ++
97 20 3.5 -5.8 0.62 0.088 1.4 -0.018 -2 -5.6 -0.28 -3 -4 4 -5.8 0.00063 3.6e+03 0.0016 3.5e+06 1.4 ++
98 22 3.7 -6.7 0.59 -0.22 1.3 -0.28 -1.9 -5.2 -0.1 -3.1 -4.1 4.2 -5.9 0.00093 3.6e+03 0.0022 3.5e+06 0.63 +
99 22 3.7 -6.4 0.57 -0.014 1.3 -0.27 -2 -5.7 -0.16 -3.2 -4.1 4.2 -5.9 0.001 3.6e+03 0.0026 3.5e+07 1.5 ++
100 22 3.7 -6 0.55 0.071 1.3 -0.26 -2 -6 -0.22 -3.1 -4.1 4.2 -5.9 0.0013 3.6e+03 0.0034 3.5e+08 1.6 ++
101 22 3.6 -5.1 0.55 0.073 1.4 -0.45 -2 -6.2 -0.29 -3.1 -4.1 4.1 -5.8 0.0019 3.6e+03 0.0025 3.5e+09 1.5 ++
102 22 3.5 -4.3 0.55 0.2 1.5 -0.91 -1.9 -5.8 -0.33 -2.8 -4 4.1 -5.8 0.0028 3.6e+03 0.0018 1e+10 1.5 ++
103 23 3.4 -3.3 0.58 0.39 1.6 -0.6 -1.9 -5.2 -0.33 -2.4 -4 4 -5.7 0.0043 3.6e+03 0.0012 1e+10 1.4 ++
104 23 3.4 -2.4 0.61 0.46 1.6 -0.38 -1.8 -4.4 -0.25 -2.2 -4.1 4 -5.7 0.0065 3.6e+03 0.0035 1e+10 1.5 ++
105 24 3.5 -1.4 0.61 0.14 1.5 0.16 -1.8 -3.7 -0.21 -2 -4.1 4.1 -5.8 0.0098 3.6e+03 0.0022 1e+10 1.3 ++
106 25 3.5 -0.87 0.61 -0.23 1.4 -0.21 -1.9 -3.5 -0.2 -1.8 -4.1 4.1 -5.8 0.013 3.6e+03 0.00077 1e+10 1.5 ++
107 25 3.5 -1 0.58 -0.5 1.3 -0.35 -2 -3.6 -0.23 -1.7 -4.1 4.1 -5.9 0.016 3.6e+03 0.0028 1e+10 1.3 ++
108 25 3.6 -0.57 0.53 -0.46 1.2 -0.14 -2.1 -3.4 -0.28 -1.6 -4.1 4.1 -5.9 0.021 3.6e+03 0.0021 1e+10 1.4 ++
109 25 3.6 -0.55 0.51 -0.65 1.1 -0.19 -2.2 -3.7 -0.34 -1.8 -4.1 4.1 -5.8 0.028 3.6e+03 0.002 1e+10 1.2 ++
110 25 3.5 0.53 0.53 -0.96 1 -0.022 -2.3 -3.2 -0.34 -1.5 -4.1 4.1 -5.9 0.036 3.6e+03 0.0036 1e+10 3.3 ++
111 25 3.5 0.53 0.53 -0.96 1 -0.022 -2.3 -3.2 -0.34 -1.5 -4.1 4.1 -5.9 0.036 3.6e+03 0.0036 28 -14 -
112 25 3.5 0.53 0.53 -0.96 1 -0.022 -2.3 -3.2 -0.34 -1.5 -4.1 4.1 -5.9 0.036 3.6e+03 0.0036 14 -8.2 -
113 25 3.5 0.53 0.53 -0.96 1 -0.022 -2.3 -3.2 -0.34 -1.5 -4.1 4.1 -5.9 0.036 3.6e+03 0.0036 6.9 -4.9 -
114 25 3.5 0.53 0.53 -0.96 1 -0.022 -2.3 -3.2 -0.34 -1.5 -4.1 4.1 -5.9 0.036 3.6e+03 0.0036 3.5 -3.1 -
115 25 3.5 0.53 0.53 -0.96 1 -0.022 -2.3 -3.2 -0.34 -1.5 -4.1 4.1 -5.9 0.036 3.6e+03 0.0036 1.7 -1.5 -
116 25 3.5 0.53 0.53 -0.96 1 -0.022 -2.3 -3.2 -0.34 -1.5 -4.1 4.1 -5.9 0.036 3.6e+03 0.0036 0.87 -0.37 -
117 25 3.6 1.4 0.54 -1.7 0.72 0.72 -2.5 -2.3 -0.28 -1.1 -4.3 4.2 -6 0.06 3.6e+03 0.0088 0.87 0.58 +
118 25 3.5 2.2 0.6 -1.8 1 0.4 -2.3 -2.4 -0.32 -1.3 -4.3 4.2 -6 0.054 3.6e+03 0.0017 0.87 0.38 +
119 25 3.5 2.2 0.6 -1.8 1 0.4 -2.3 -2.4 -0.32 -1.3 -4.3 4.2 -6 0.054 3.6e+03 0.0017 0.43 -0.56 -
120 25 3.5 2.2 0.58 -1.9 1.1 0.84 -2.3 -2.2 -0.33 -1.3 -4.4 4.2 -6 0.058 3.6e+03 0.002 0.43 0.56 +
121 25 3.5 1.8 0.54 -1.8 1.1 0.6 -2.2 -2 -0.34 -1.1 -4.4 4.2 -6 0.057 3.6e+03 0.0018 0.43 0.82 +
122 24 3.4 2.2 0.56 -1.8 1.3 0.5 -2.1 -1.9 -0.38 -1.1 -4.4 4.2 -6 0.054 3.6e+03 0.00088 0.43 0.61 +
123 24 3.4 2.2 0.56 -1.8 1.3 0.5 -2.1 -1.9 -0.38 -1.1 -4.4 4.2 -6 0.054 3.6e+03 0.00088 0.22 -1.5 -
124 24 3.4 2.1 0.6 -1.9 1.4 0.36 -2 -1.9 -0.38 -1.3 -4.4 4.2 -6 0.054 3.6e+03 0.0038 0.22 0.6 +
125 24 3.4 2.1 0.61 -2.1 1.4 0.39 -2 -1.7 -0.36 -1.2 -4.4 4.2 -6 0.059 3.6e+03 0.0012 2.2 1 ++
126 24 3.4 2.1 0.61 -2.1 1.4 0.39 -2 -1.7 -0.36 -1.2 -4.4 4.2 -6 0.059 3.6e+03 0.0012 0.83 -3.8 -
127 24 3.4 2.1 0.61 -2.1 1.4 0.39 -2 -1.7 -0.36 -1.2 -4.4 4.2 -6 0.059 3.6e+03 0.0012 0.41 -0.22 -
128 24 3.4 2.2 0.63 -2.1 1.7 0.1 -1.9 -1.3 -0.37 -1.2 -4.5 4.2 -6.1 0.058 3.6e+03 0.0015 0.41 0.79 +
129 24 3.4 2.2 0.63 -2.1 1.7 0.1 -1.9 -1.3 -0.37 -1.2 -4.5 4.2 -6.1 0.058 3.6e+03 0.0015 0.21 -3.4 -
130 24 3.4 2.2 0.63 -2.1 1.7 0.1 -1.9 -1.3 -0.37 -1.2 -4.5 4.2 -6.1 0.058 3.6e+03 0.0015 0.1 -0.8 -
131 24 3.4 2.1 0.64 -2.2 1.5 0.11 -2 -1.3 -0.38 -1.2 -4.5 4.2 -6.1 0.063 3.6e+03 0.00075 0.1 0.75 +
132 24 3.4 2.2 0.66 -2.1 1.6 0.16 -2 -1.3 -0.41 -1.2 -4.5 4.2 -6 0.062 3.6e+03 0.00074 0.1 0.8 +
133 24 3.3 2.2 0.69 -2.1 1.7 0.06 -2 -1.2 -0.44 -1.2 -4.5 4.2 -6 0.062 3.6e+03 0.00061 0.1 0.41 +
134 24 3.3 2.1 0.68 -2.1 1.6 0.098 -2 -1.3 -0.45 -1.2 -4.5 4.2 -6 0.063 3.6e+03 0.00062 1 1 ++
135 24 3.3 2.1 0.68 -2.1 1.6 0.098 -2 -1.3 -0.45 -1.2 -4.5 4.2 -6 0.063 3.6e+03 0.00062 0.099 -0.21 -
136 24 3.3 2.2 0.67 -2.1 1.6 0.079 -2 -1.2 -0.46 -1.2 -4.5 4.2 -6 0.067 3.6e+03 0.00025 0.099 0.71 +
137 24 3.3 2.1 0.67 -2.1 1.6 0.09 -2 -1.2 -0.5 -1.2 -4.5 4.2 -6 0.069 3.6e+03 0.0006 0.99 1 ++
138 24 3.3 2.1 0.67 -2.1 1.6 0.09 -2 -1.2 -0.5 -1.2 -4.5 4.2 -6 0.069 3.6e+03 0.0006 0.49 -0.11 -
139 24 3.3 2.1 0.67 -2.1 1.6 0.09 -2 -1.2 -0.5 -1.2 -4.5 4.2 -6 0.069 3.6e+03 0.0006 0.25 0.0021 -
140 24 3.3 2.1 0.67 -2.1 1.6 0.09 -2 -1.2 -0.5 -1.2 -4.5 4.2 -6 0.069 3.6e+03 0.0006 0.12 -0.032 -
141 24 3.2 2.2 0.69 -2.1 1.7 0.13 -2 -1.2 -0.54 -1.2 -4.5 4.2 -6 0.072 3.6e+03 0.0008 0.12 0.15 +
142 24 3.2 2.1 0.67 -2 1.7 0.087 -2 -1.2 -0.53 -1.2 -4.5 4.2 -6 0.072 3.6e+03 0.00075 1.2 1.2 ++
143 23 3.2 2 0.67 -2 1.8 0.069 -2 -1.1 -0.56 -1.2 -4.5 4.2 -6 0.074 3.6e+03 0.00097 12 1 ++
144 22 3.3 1.9 0.68 -1.9 1.7 0.14 -2 -1.2 -0.51 -1.1 -4.5 4.2 -6.1 0.071 3.6e+03 0.00071 12 0.33 +
145 22 3.2 1.8 0.67 -1.8 1.8 0.1 -2 -1.1 -0.56 -1.1 -4.5 4.2 -6 0.075 3.6e+03 0.0011 1.2e+02 1.2 ++
146 22 3.1 1.9 0.64 -1.8 2 0.043 -1.9 -1.1 -0.6 -1.1 -4.5 4.2 -6 0.08 3.6e+03 0.0019 1.2e+03 1.7 ++
147 22 3.1 1.9 0.64 -1.8 2 0.043 -1.9 -1.1 -0.6 -1.1 -4.5 4.2 -6 0.08 3.6e+03 0.0019 0.28 -34 -
148 22 3.1 1.9 0.64 -1.8 2 0.043 -1.9 -1.1 -0.6 -1.1 -4.5 4.2 -6 0.08 3.6e+03 0.0019 0.14 -7.3 -
149 22 3.1 1.9 0.64 -1.8 2 0.043 -1.9 -1.1 -0.6 -1.1 -4.5 4.2 -6 0.08 3.6e+03 0.0019 0.069 -0.26 -
150 22 3.1 1.9 0.63 -1.8 2 0.043 -1.9 -1.1 -0.59 -1.1 -4.6 4.2 -6 0.081 3.6e+03 0.0013 0.69 1.2 ++
151 22 3.1 1.9 0.63 -1.8 2 0.043 -1.9 -1.1 -0.59 -1.1 -4.6 4.2 -6 0.081 3.6e+03 0.0013 0.11 -4 -
152 22 3.1 1.9 0.63 -1.8 2 0.043 -1.9 -1.1 -0.59 -1.1 -4.6 4.2 -6 0.081 3.6e+03 0.0013 0.054 -0.38 -
153 22 3.1 1.9 0.64 -1.8 2.1 0.093 -1.8 -1.1 -0.54 -1.1 -4.6 4.2 -6.1 0.078 3.6e+03 0.00058 0.054 0.76 +
154 21 3.1 1.8 0.65 -1.8 2.1 0.085 -1.8 -1.1 -0.55 -1.1 -4.6 4.2 -6.1 0.078 3.6e+03 0.00047 0.054 0.19 +
155 21 3.1 1.8 0.65 -1.8 2.1 0.072 -1.8 -1.1 -0.56 -1.1 -4.6 4.2 -6.1 0.078 3.6e+03 0.00022 0.054 0.44 +
156 21 3.1 1.8 0.64 -1.8 2.1 0.086 -1.8 -1.1 -0.54 -1.1 -4.6 4.2 -6.1 0.078 3.6e+03 0.00044 0.054 0.53 +
157 21 3.1 1.8 0.64 -1.8 2.1 0.08 -1.8 -1.1 -0.55 -1.1 -4.6 4.2 -6.1 0.078 3.6e+03 0.00024 0.54 1.1 ++
158 21 3.1 1.7 0.65 -1.7 2.1 0.1 -1.8 -1.1 -0.55 -1.1 -4.6 4.2 -6.1 0.078 3.6e+03 0.0003 5.4 0.99 ++
159 15 3.2 1.5 0.65 -1.5 2 0.075 -1.9 -0.96 -0.59 -0.82 -4.6 4.2 -6.1 0.08 3.6e+03 0.004 5.4 0.4 +
160 15 3.2 1.5 0.65 -1.5 2 0.075 -1.9 -0.96 -0.59 -0.82 -4.6 4.2 -6.1 0.08 3.6e+03 0.004 2.7 -21 -
161 15 3.2 1.5 0.65 -1.5 2 0.075 -1.9 -0.96 -0.59 -0.82 -4.6 4.2 -6.1 0.08 3.6e+03 0.004 1.4 -20 -
162 15 3.2 1.5 0.65 -1.5 2 0.075 -1.9 -0.96 -0.59 -0.82 -4.6 4.2 -6.1 0.08 3.6e+03 0.004 0.68 -9.7 -
163 15 3.2 1.5 0.65 -1.5 2 0.075 -1.9 -0.96 -0.59 -0.82 -4.6 4.2 -6.1 0.08 3.6e+03 0.004 0.34 -2.9 -
164 15 3.2 1.5 0.65 -1.5 2 0.075 -1.9 -0.96 -0.59 -0.82 -4.6 4.2 -6.1 0.08 3.6e+03 0.004 0.17 -0.21 -
165 15 3.2 1.7 0.65 -1.6 2.1 -0.0026 -1.8 -1 -0.53 -0.82 -4.6 4.3 -6.2 0.08 3.6e+03 0.0034 0.17 0.87 +
166 15 3.2 1.7 0.65 -1.6 2.1 -0.0026 -1.8 -1 -0.53 -0.82 -4.6 4.3 -6.2 0.08 3.6e+03 0.0034 0.085 -0.025 -
167 15 3.2 1.7 0.67 -1.6 2.1 0.022 -1.8 -1 -0.53 -0.74 -4.6 4.2 -6.2 0.081 3.6e+03 0.0048 0.85 1.8 ++
168 15 3.2 1.7 0.67 -1.6 2.1 0.022 -1.8 -1 -0.53 -0.74 -4.6 4.2 -6.2 0.081 3.6e+03 0.0048 0.42 -1.6 -
169 15 3 1.7 0.72 -1.7 2.2 0.094 -1.8 -0.98 -0.54 -0.31 -4.7 4.1 -6.2 0.086 3.6e+03 0.023 4.2 1.4 ++
170 15 3 1.7 0.72 -1.7 2.2 0.094 -1.8 -0.98 -0.54 -0.31 -4.7 4.1 -6.2 0.086 3.6e+03 0.023 2.1 -29 -
171 15 3 1.7 0.72 -1.7 2.2 0.094 -1.8 -0.98 -0.54 -0.31 -4.7 4.1 -6.2 0.086 3.6e+03 0.023 1.1 -17 -
172 15 3 1.7 0.72 -1.7 2.2 0.094 -1.8 -0.98 -0.54 -0.31 -4.7 4.1 -6.2 0.086 3.6e+03 0.023 0.53 -7.9 -
173 15 3 1.7 0.72 -1.7 2.2 0.094 -1.8 -0.98 -0.54 -0.31 -4.7 4.1 -6.2 0.086 3.6e+03 0.023 0.26 -3.3 -
174 15 3 1.7 0.72 -1.7 2.2 0.094 -1.8 -0.98 -0.54 -0.31 -4.7 4.1 -6.2 0.086 3.6e+03 0.023 0.13 -1.8 -
175 15 3 1.7 0.72 -1.7 2.2 0.094 -1.8 -0.98 -0.54 -0.31 -4.7 4.1 -6.2 0.086 3.6e+03 0.023 0.066 0.013 -
176 15 2.9 1.7 0.73 -1.7 2.2 0.1 -1.9 -0.97 -0.48 -0.25 -4.7 4.1 -6.2 0.086 3.6e+03 0.0079 0.066 0.84 +
177 15 2.9 1.7 0.7 -1.7 2.2 0.078 -1.9 -0.96 -0.49 -0.31 -4.7 4.1 -6.2 0.084 3.6e+03 0.0019 0.66 1.1 ++
178 15 2.9 1.7 0.7 -1.7 2.2 0.078 -1.9 -0.96 -0.49 -0.31 -4.7 4.1 -6.2 0.084 3.6e+03 0.0019 0.33 -1 -
179 15 3.1 1.8 0.64 -1.7 2 -0.057 -2.1 -0.88 -0.56 -0.65 -4.6 4 -6.2 0.08 3.6e+03 0.0034 0.33 0.48 +
180 15 3 2.1 0.63 -1.8 1.9 -0.092 -2.2 -0.81 -0.8 -0.46 -4.5 3.9 -6.1 0.087 3.6e+03 0.0049 0.33 0.3 +
181 15 3.1 1.8 0.6 -1.6 2 -0.058 -2.1 -0.97 -0.67 -0.51 -4.5 4 -6.2 0.082 3.6e+03 0.0021 0.33 0.33 +
182 15 3.1 1.8 0.6 -1.6 2 -0.058 -2.1 -0.97 -0.67 -0.51 -4.5 4 -6.2 0.082 3.6e+03 0.0021 0.17 -1.4 -
183 15 3.1 1.8 0.6 -1.6 2 -0.058 -2.1 -0.97 -0.67 -0.51 -4.5 4 -6.2 0.082 3.6e+03 0.0021 0.083 -0.18 -
184 15 3 1.8 0.65 -1.7 2 -0.063 -2.1 -0.93 -0.62 -0.51 -4.6 4 -6.2 0.082 3.6e+03 0.0062 0.083 0.82 +
185 15 3 1.9 0.67 -1.7 2 -0.086 -2.2 -0.84 -0.65 -0.48 -4.7 3.9 -6.2 0.086 3.6e+03 0.012 0.83 2.3 ++
186 15 3 1.9 0.67 -1.7 2 -0.086 -2.2 -0.84 -0.65 -0.48 -4.7 3.9 -6.2 0.086 3.6e+03 0.012 0.41 -2.9 -
187 15 3 1.9 0.67 -1.7 2 -0.086 -2.2 -0.84 -0.65 -0.48 -4.7 3.9 -6.2 0.086 3.6e+03 0.012 0.21 -0.91 -
188 15 2.9 1.9 0.76 -1.8 1.8 -0.14 -2.2 -0.74 -0.67 -0.42 -4.8 3.7 -6.4 0.097 3.6e+03 0.0062 0.21 0.43 +
189 15 3 1.9 0.7 -2 1.7 -0.19 -2.2 -0.55 -0.56 -0.46 -4.7 3.6 -6.5 0.099 3.6e+03 0.0078 2.1 2.1 ++
190 15 3 1.9 0.7 -2 1.7 -0.19 -2.2 -0.55 -0.56 -0.46 -4.7 3.6 -6.5 0.099 3.6e+03 0.0078 1 -3.9 -
191 15 3 1.9 0.7 -2 1.7 -0.19 -2.2 -0.55 -0.56 -0.46 -4.7 3.6 -6.5 0.099 3.6e+03 0.0078 0.52 -1.4 -
192 15 3 1.9 0.7 -2 1.7 -0.19 -2.2 -0.55 -0.56 -0.46 -4.7 3.6 -6.5 0.099 3.6e+03 0.0078 0.26 -0.78 -
193 15 3 1.9 0.7 -2 1.7 -0.19 -2.2 -0.55 -0.56 -0.46 -4.7 3.6 -6.5 0.099 3.6e+03 0.0078 0.13 -0.4 -
194 15 3 1.9 0.7 -2 1.7 -0.19 -2.2 -0.55 -0.56 -0.46 -4.7 3.6 -6.5 0.099 3.6e+03 0.0078 0.065 -0.52 -
195 15 3 1.9 0.7 -2 1.7 -0.19 -2.2 -0.55 -0.56 -0.46 -4.7 3.6 -6.5 0.099 3.6e+03 0.0078 0.032 -0.46 -
196 15 3 1.9 0.69 -2 1.7 -0.19 -2.2 -0.55 -0.54 -0.46 -4.7 3.6 -6.5 0.091 3.6e+03 0.012 0.032 0.12 +
197 15 3 1.9 0.67 -2 1.7 -0.19 -2.2 -0.54 -0.51 -0.47 -4.6 3.6 -6.5 0.09 3.6e+03 0.0036 0.32 1.1 ++
198 15 3 1.9 0.67 -2 1.7 -0.19 -2.2 -0.54 -0.51 -0.47 -4.6 3.6 -6.5 0.09 3.6e+03 0.0036 0.16 -0.87 -
199 15 3 1.9 0.67 -2 1.7 -0.19 -2.2 -0.54 -0.51 -0.47 -4.6 3.6 -6.5 0.09 3.6e+03 0.0036 0.081 -0.15 -
200 15 3.1 1.9 0.63 -2.1 1.7 -0.2 -2.2 -0.53 -0.49 -0.48 -4.6 3.6 -6.5 0.11 3.6e+03 0.001 0.081 0.14 +
201 15 3.1 1.8 0.65 -2.2 1.7 -0.2 -2.3 -0.6 -0.47 -0.47 -4.5 3.6 -6.5 0.093 3.6e+03 0.0015 0.81 1 ++
202 15 3.1 1.8 0.65 -2.2 1.7 -0.2 -2.3 -0.6 -0.47 -0.47 -4.5 3.6 -6.5 0.093 3.6e+03 0.0015 0.4 -4 -
203 15 3.1 1.7 0.73 -2.6 1.4 -0.22 -2.2 -0.6 -0.37 -0.48 -4.5 3.5 -6.4 0.081 3.6e+03 0.0071 0.4 0.23 +
204 15 3.1 1.7 0.61 -2.6 1.6 -0.13 -2 -1 -0.37 -0.75 -4.5 3.5 -6.4 0.092 3.6e+03 0.0046 0.4 0.3 +
205 14 3.1 1.6 0.57 -2.8 1.6 -0.088 -2.4 -0.87 -0.6 -0.79 -4.5 3.5 -6.4 0.1 3.6e+03 0.0056 0.4 0.25 +
206 14 3.1 1.6 0.73 -3.2 1.5 0.02 -2.1 -0.82 -0.3 -0.87 -4.5 3.6 -6.6 0.095 3.5e+03 0.0042 0.4 0.59 +
207 14 3.1 1.5 0.66 -3.2 1.5 0.15 -2.1 -1.2 -0.36 -1.1 -4.5 3.6 -6.5 0.098 3.5e+03 0.0014 4 1.2 ++
208 14 3.1 1.5 0.66 -3.2 1.5 0.15 -2.1 -1.2 -0.36 -1.1 -4.5 3.6 -6.5 0.098 3.5e+03 0.0014 1.9 -0.035 -
209 12 3 -0.39 0.56 -2.8 1.6 0.33 -2.1 -1.8 -0.46 -1.5 -4.5 3.6 -6.4 0.1 3.5e+03 0.008 1.9 0.15 +
210 10 3.1 -0.36 0.64 -3.8 1.5 0.31 -2.2 -1.8 -0.39 -2 -4.5 3.6 -6.5 0.11 3.5e+03 0.003 1.9 0.24 +
211 8.5 3.1 -1.5 0.69 -2.9 1.6 0.34 -2.2 -1.6 -0.4 -1.6 -4.5 3.7 -6.7 0.11 3.5e+03 0.0027 19 1.2 ++
212 8.5 3.1 -1.5 0.69 -2.9 1.6 0.34 -2.2 -1.6 -0.4 -1.6 -4.5 3.7 -6.7 0.11 3.5e+03 0.0027 9.3 -3 -
213 8.5 3.1 -1.5 0.69 -2.9 1.6 0.34 -2.2 -1.6 -0.4 -1.6 -4.5 3.7 -6.7 0.11 3.5e+03 0.0027 4.6 -6.8 -
214 8.5 3.1 -1.5 0.69 -2.9 1.6 0.34 -2.2 -1.6 -0.4 -1.6 -4.5 3.7 -6.7 0.11 3.5e+03 0.0027 2.3 -0.92 -
215 6.2 3.2 -1.8 0.78 -3.1 1.4 0.33 -2.2 -1.7 -0.27 -1.4 -4.4 3.7 -6.7 0.12 3.5e+03 0.0036 2.3 0.72 +
216 6.2 3.2 -1.8 0.78 -3.1 1.4 0.33 -2.2 -1.7 -0.27 -1.4 -4.4 3.7 -6.7 0.12 3.5e+03 0.0036 0.64 -1.7 -
217 6.3 3.2 -1.2 0.83 -3.1 1.4 0.16 -2.2 -1.4 -0.32 -1.3 -4.6 3.7 -6.7 0.13 3.5e+03 0.0016 6.4 0.91 ++
218 6.3 3.2 -1.2 0.83 -3.1 1.4 0.16 -2.2 -1.4 -0.32 -1.3 -4.6 3.7 -6.7 0.13 3.5e+03 0.0016 3.2 -24 -
219 6.3 3.2 -1.2 0.83 -3.1 1.4 0.16 -2.2 -1.4 -0.32 -1.3 -4.6 3.7 -6.7 0.13 3.5e+03 0.0016 1.6 -24 -
220 6.3 3.2 -1.2 0.83 -3.1 1.4 0.16 -2.2 -1.4 -0.32 -1.3 -4.6 3.7 -6.7 0.13 3.5e+03 0.0016 0.8 -6.2 -
221 6.3 3.2 -1.2 0.83 -3.1 1.4 0.16 -2.2 -1.4 -0.32 -1.3 -4.6 3.7 -6.7 0.13 3.5e+03 0.0016 0.4 -1.7 -
222 6.3 3.2 -1.2 0.83 -3.1 1.4 0.16 -2.2 -1.4 -0.32 -1.3 -4.6 3.7 -6.7 0.13 3.5e+03 0.0016 0.2 -0.38 -
223 6.2 3.2 -1.2 0.82 -3 1.5 0.13 -2.2 -1.5 -0.37 -1.5 -4.7 3.7 -6.7 0.13 3.5e+03 0.0014 0.2 0.55 +
224 6.1 3.2 -1.4 0.83 -3 1.5 0.16 -2.2 -1.5 -0.33 -1.5 -4.7 3.7 -6.8 0.13 3.5e+03 0.00043 2 0.93 ++
225 6.1 3.2 -1.4 0.83 -3 1.5 0.16 -2.2 -1.5 -0.33 -1.5 -4.7 3.7 -6.8 0.13 3.5e+03 0.00043 1 -15 -
226 6.1 3.2 -1.4 0.83 -3 1.5 0.16 -2.2 -1.5 -0.33 -1.5 -4.7 3.7 -6.8 0.13 3.5e+03 0.00043 0.5 -1.9 -
227 5.6 3.2 -1.3 0.85 -3 1.5 0.099 -2.2 -1.5 -0.32 -1.5 -4.7 3.7 -6.8 0.13 3.5e+03 0.00092 0.5 0.28 +
228 5.6 3.2 -1.3 0.85 -3 1.5 0.099 -2.2 -1.5 -0.32 -1.5 -4.7 3.7 -6.8 0.13 3.5e+03 0.00092 0.25 -2.5 -
229 5.6 3.2 -1.3 0.85 -3 1.5 0.099 -2.2 -1.5 -0.32 -1.5 -4.7 3.7 -6.8 0.13 3.5e+03 0.00092 0.12 -0.21 -
230 5.7 3.2 -1.3 0.86 -2.8 1.5 0.052 -2.3 -1.4 -0.33 -1.5 -4.7 3.8 -6.9 0.13 3.5e+03 0.00026 0.12 0.89 +
231 5.8 3.2 -1.4 0.9 -2.9 1.5 -0.0046 -2.3 -1.4 -0.31 -1.6 -4.7 3.8 -6.9 0.14 3.5e+03 0.00099 1.2 1.5 ++
232 5.8 3.2 -1.4 0.9 -2.9 1.5 -0.0046 -2.3 -1.4 -0.31 -1.6 -4.7 3.8 -6.9 0.14 3.5e+03 0.00099 0.62 -11 -
233 5.8 3.2 -1.4 0.9 -2.9 1.5 -0.0046 -2.3 -1.4 -0.31 -1.6 -4.7 3.8 -6.9 0.14 3.5e+03 0.00099 0.31 -4.5 -
234 5.7 3.2 -1.7 0.95 -2.9 1.5 -0.26 -2.3 -1.4 -0.33 -1.9 -4.8 3.9 -7.1 0.15 3.5e+03 0.0038 3.1 0.91 ++
235 6.3 3.2 -1.7 0.93 -3 1.5 -0.17 -2.3 -1.5 -0.3 -2 -4.7 3.8 -7 0.14 3.5e+03 0.003 3.1 0.41 +
236 6.3 3.2 -1.7 0.93 -3 1.5 -0.17 -2.3 -1.5 -0.3 -2 -4.7 3.8 -7 0.14 3.5e+03 0.003 1.6 -9.7 -
237 6.3 3.2 -1.7 0.93 -3 1.5 -0.17 -2.3 -1.5 -0.3 -2 -4.7 3.8 -7 0.14 3.5e+03 0.003 0.78 -9 -
238 6.3 3.2 -1.7 0.93 -3 1.5 -0.17 -2.3 -1.5 -0.3 -2 -4.7 3.8 -7 0.14 3.5e+03 0.003 0.39 -2.4 -
239 6.2 3.2 -2.1 0.95 -2.8 1.5 -0.11 -2.3 -1.4 -0.35 -2 -4.8 3.8 -7 0.14 3.5e+03 0.0023 0.39 0.54 +
240 6.2 3.2 -2.1 0.95 -2.8 1.5 -0.11 -2.3 -1.4 -0.35 -2 -4.8 3.8 -7 0.14 3.5e+03 0.0023 0.19 -2.5 -
241 6.2 3.2 -2.1 0.95 -2.8 1.5 -0.11 -2.3 -1.4 -0.35 -2 -4.8 3.8 -7 0.14 3.5e+03 0.0023 0.097 -0.35 -
242 6.2 3.2 -2.1 0.94 -2.8 1.5 -0.12 -2.3 -1.4 -0.34 -2.1 -4.7 3.8 -7 0.14 3.5e+03 0.0014 0.97 1.2 ++
243 6.2 3.2 -2.1 0.94 -2.8 1.5 -0.12 -2.3 -1.4 -0.34 -2.1 -4.7 3.8 -7 0.14 3.5e+03 0.0014 0.1 -0.083 -
244 6.2 3.2 -2 0.93 -2.8 1.5 -0.027 -2.3 -1.4 -0.32 -2.2 -4.6 3.8 -6.9 0.15 3.5e+03 0.00025 1 0.93 ++
245 6.2 3.2 -2 0.93 -2.8 1.5 -0.027 -2.3 -1.4 -0.32 -2.2 -4.6 3.8 -6.9 0.15 3.5e+03 0.00025 0.5 -13 -
246 6.2 3.2 -2 0.93 -2.8 1.5 -0.027 -2.3 -1.4 -0.32 -2.2 -4.6 3.8 -6.9 0.15 3.5e+03 0.00025 0.25 -6.9 -
247 6.2 3.2 -2 0.93 -2.8 1.5 -0.027 -2.3 -1.4 -0.32 -2.2 -4.6 3.8 -6.9 0.15 3.5e+03 0.00025 0.13 -3.2 -
248 6.2 3.2 -2 0.93 -2.8 1.5 -0.027 -2.3 -1.4 -0.32 -2.2 -4.6 3.8 -6.9 0.15 3.5e+03 0.00025 0.063 -0.76 -
249 6.2 3.2 -2 0.91 -2.9 1.5 0.0075 -2.2 -1.3 -0.36 -2.1 -4.6 3.8 -6.9 0.15 3.5e+03 0.00067 0.063 0.47 +
250 6.3 3.2 -2 0.92 -2.8 1.5 0.041 -2.2 -1.3 -0.36 -2.1 -4.6 3.8 -6.9 0.15 3.5e+03 0.00026 0.063 0.63 +
251 6.3 3.2 -2 0.92 -2.8 1.5 0.041 -2.2 -1.3 -0.36 -2.1 -4.6 3.8 -6.9 0.15 3.5e+03 0.00026 0.032 -0.79 -
252 6.3 3.2 -2 0.92 -2.8 1.5 0.038 -2.2 -1.3 -0.36 -2.2 -4.6 3.8 -6.9 0.15 3.5e+03 0.00014 0.032 0.81 +
253 6.3 3.2 -2 0.92 -2.8 1.5 0.038 -2.2 -1.3 -0.36 -2.2 -4.6 3.8 -6.9 0.15 3.5e+03 7.5e-05 0.032 0.95 +
Results saved in file b15panel_discrete.html
Results saved in file b15panel_discrete.pickle
print(results.short_summary())
Results for model b15panel_discrete
Nbr of parameters: 15
Sample size: 752
Observations: 6768
Excluded data: 3960
Final log likelihood: -3541.148
Akaike Information Criterion: 7112.296
Bayesian Information Criterion: 7181.637
pandas_results = results.get_estimated_parameters()
pandas_results
Total running time of the script: (7 minutes 43.663 seconds)