Factors of successful protection from pressure on business
Concept and economic essence of property rights. Justification and development of the business protection model against possible damage to business activities caused by the influence various external and internal market factors and economic conditions.
| Рубрика | Экономико-математическое моделирование |
| Вид | дипломная работа |
| Язык | английский |
| Дата добавления | 11.08.2020 |
| Размер файла | 5,0 M |
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anova(logit_1, test="Chisq")
## Analysis of Deviance Table
##
## Model: binomial, link: logit
##
## Response: target_light_extended
##
## Terms added sequentially (first to last)
##
##
## Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL 355 488.55
## macro_okved_code_group 8 20.5329 347 468.02 0.0084974 **
## spark_stock_ticket 1 0.4270 346 467.59 0.5134728
## category_by_size_missing 3 13.3925 343 454.20 0.0038603 **
## administrative_position 1 1.6401 342 452.56 0.2003081
## administrative_connections 1 0.5145 341 452.05 0.4731845
## in_political_party 1 0.0945 340 451.95 0.7585476
## in_association_or_sro 1 4.9590 339 446.99 0.0259545 *
## case_publications 1 11.3855 338 435.61 0.0007402 ***
## criminal_prosecution 1 0.0735 337 435.53 0.7862407
## capture 1 5.5168 336 430.02 0.0188342 *
## corruption 1 0.1114 335 429.91 0.7385734
## barriers 1 0.1073 334 429.80 0.7431859
## have_court_case 1 1.9298 333 427.87 0.1647800
## is_guilty 1 0.3340 332 427.54 0.5633164
## reviewed_by_bac 1 1.5678 331 425.97 0.2105283
## cop_stage 2 4.2938 329 421.67 0.1168429
## to_ombudsman 1 2.5916 328 419.08 0.1074337
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
By hand:
light_extended_vars <-c(
c(
# "federal_districts",
#"largest_fed_districts",
#"macro_okved_code",
"macro_okved_code_group",
#"spark_web_site",
#"spark_stock_ticket",
#"category_by_size_missing",
"category_by_size_melse",
# "category_by_size_2_cat",
#"administrative_position",
#"administrative_connections",
#"in_political_party",
"in_association_or_sro",
"case_publications",
#"criminal_prosecution",
"capture", "corruption", "barriers",
#"have_court_case",
#"is_guilty",
# "reviewed_by_bac",
# "supported_by_bac_public_council",
# "max_bac_stage",
"cop_stage",
#"reaction_not_passed_by_applicant",
#"reaction_consultation",
#"reaction_target_letters_control",
#"reaction_not_passed_by_bac",
"to_ombudsman",
"target_light_extended")
)
light_extended_data <-dataset[light_extended_vars]
light_extended_data =light_extended_data[!is.na(light_extended_data$target_light_extended),]
light_extended_data =light_extended_data[!is.na(light_extended_data$category_by_size_melse),]
light_extended_data$macro_okved_code_group <-factor(light_extended_data$macro_okved_code_group)
light_extended_data$category_by_size_melse <-factor(light_extended_data$category_by_size_melse)
light_extended_data$cop_stage <-factor(light_extended_data$cop_stage)
logit_1<-glm(target_light_extended~., family = binomial,data = light_extended_data)
summary(logit_1)
##
## Call:
## glm(formula = target_light_extended ~ ., family = binomial, data = light_extended_data)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.9343 -1.0775 0.5488 0.9847 1.8389
##
## Coefficients:
## Estimate Std. Error z value
## (Intercept) 1.44809 0.52909 2.737
## macro_okved_code_groupFinancial_insurance -0.61208 0.67255 -0.910
## macro_okved_code_groupmanufacturing 0.37316 0.40262 0.927
## macro_okved_code_groupother_categories 0.44716 0.42851 1.044
## macro_okved_code_groupreal_estate 1.72276 0.51806 3.325
## macro_okved_code_grouprural 0.92011 0.58612 1.570
## macro_okved_code_groupScience 1.41442 0.50626 2.794
## macro_okved_code_groupTrading 0.35883 0.40714 0.881
## macro_okved_code_groupTransportation 0.03762 0.62455 0.060
## category_by_size_melseMicro -0.85986 0.26166 -3.286
## in_association_or_sro 0.55634 0.26328 2.113
## case_publications -1.05904 0.33757 -3.137
## capture -0.73300 0.27856 -2.631
## corruption 0.11972 0.44487 0.269
## barriers -0.13623 0.39479 -0.345
## cop_stageInformation_collection 0.02222 0.37063 0.060
## cop_stageResolution -0.65624 0.30175 -2.175
## to_ombudsman 0.66487 0.38949 1.707
## Pr(>|z|)
## (Intercept) 0.006202 **
## macro_okved_code_groupFinancial_insurance 0.362774
## macro_okved_code_groupmanufacturing 0.354007
## macro_okved_code_groupother_categories 0.296708
## macro_okved_code_groupreal_estate 0.000883 ***
## macro_okved_code_grouprural 0.116455
## macro_okved_code_groupScience 0.005209 **
## macro_okved_code_groupTrading 0.378133
## macro_okved_code_groupTransportation 0.951962
## category_by_size_melseMicro 0.001016 **
## in_association_or_sro 0.034595 *
## case_publications 0.001705 **
## capture 0.008504 **
## corruption 0.787840
## barriers 0.730032
## cop_stageInformation_collection 0.952200
## cop_stageResolution 0.029644 *
## to_ombudsman 0.087815 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 488.55 on 355 degrees of freedom
## Residual deviance: 427.53 on 338 degrees of freedom
## AIC: 463.53
##
## Number of Fisher Scoring iterations: 3
car::vif(logit_1)
## GVIF Df GVIF^(1/(2*Df))
## macro_okved_code_group 1.583574 8 1.029147
## category_by_size_melse 1.161981 1 1.077952
## in_association_or_sro 1.168010 1 1.080745
## case_publications 1.141557 1 1.068437
## capture 1.187126 1 1.089553
## corruption 1.060162 1 1.029642
## barriers 1.271040 1 1.127404
## cop_stage 1.289682 2 1.065665
## to_ombudsman 1.341861 1 1.158387
anova(logit_1, test="Chisq")
## Analysis of Deviance Table
##
## Model: binomial, link: logit
##
## Response: target_light_extended
##
## Terms added sequentially (first to last)
##
##
## Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL 355 488.55
## macro_okved_code_group 8 20.5329 347 468.02 0.0084974 **
## category_by_size_melse 1 11.8728 346 456.15 0.0005696 ***
## in_association_or_sro 1 3.8046 345 452.34 0.0511127 .
## case_publications 1 11.2745 344 441.07 0.0007858 ***
## capture 1 5.5747 343 435.49 0.0182222 *
## corruption 1 0.1216 342 435.37 0.7272802
## barriers 1 0.0022 341 435.37 0.9622779
## cop_stage 2 4.8525 339 430.52 0.0883666 .
## to_ombudsman 1 2.9903 338 427.53 0.0837670 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
By AIC maximization
light_extended_vars <-c(
c(
#"spark_web_site",
"spark_stock_ticket",
#"category_by_size_missing",
"category_by_size_melse",
# "category_by_size_2_cat",
"administrative_position",
#"administrative_connections",
"in_political_party",
"in_association_or_sro",
"case_publications",
"criminal_prosecution",
"capture", "corruption", "barriers",
"have_court_case",
"is_guilty",
"reviewed_by_bac",
#"supported_by_bac_public_council",
# "max_bac_stage",
"cop_stage",
#"reaction_not_passed_by_applicant",
#"reaction_consultation",
#"reaction_target_letters_control",
#"reaction_not_passed_by_bac",
"to_ombudsman",
"macro_okved_code_group",
"target_light_extended")
)
light_extended_data <-dataset[light_extended_vars]
light_extended_data =light_extended_data[!is.na(light_extended_data$target_light_extended),]
light_extended_data =light_extended_data[!is.na(light_extended_data$category_by_size_melse),]
light_extended_data$macro_okved_code_group <-factor(light_extended_data$macro_okved_code_group)
light_extended_data$category_by_size_melse <-factor(light_extended_data$category_by_size_melse)
light_extended_data$cop_stage <-factor(light_extended_data$cop_stage)
logit_1<-glm(target_light_extended~., family = binomial,data = light_extended_data)
summary(logit_1)
##
## Call:
## glm(formula = target_light_extended ~ ., family = binomial, data = light_extended_data)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.9990 -1.0525 0.5197 0.9515 1.9745
##
## Coefficients:
## Estimate Std. Error z value
## (Intercept) 1.47803 0.74685 1.979
## spark_stock_ticket -1.24487 0.72366 -1.720
## category_by_size_melseMicro -0.94148 0.27384 -3.438
## administrative_position 0.64925 0.48025 1.352
## in_political_party -0.16522 0.42844 -0.386
## in_association_or_sro 0.62368 0.26972 2.312
## case_publications -1.09858 0.34717 -3.164
## criminal_prosecution -0.10080 0.42957 -0.235
## capture -0.78202 0.34583 -2.261
## corruption 0.18104 0.48196 0.376
## barriers -0.16747 0.53344 -0.314
## have_court_case 0.49645 0.35803 1.387
## is_guilty -0.22654 0.36763 -0.616
## reviewed_by_bac -0.03864 0.53243 -0.073
## cop_stageInformation_collection -0.03723 0.53661 -0.069
## cop_stageResolution -0.74449 0.49039 -1.518
## to_ombudsman 0.61925 0.40265 1.538
## macro_okved_code_groupFinancial_insurance -0.80264 0.70915 -1.132
## macro_okved_code_groupmanufacturing 0.39367 0.42099 0.935
## macro_okved_code_groupother_categories 0.48973 0.44058 1.112
## macro_okved_code_groupreal_estate 1.75574 0.52894 3.319
## macro_okved_code_grouprural 0.79448 0.59320 1.339
## macro_okved_code_groupScience 1.42457 0.51721 2.754
## macro_okved_code_groupTrading 0.39943 0.42124 0.948
## macro_okved_code_groupTransportation 0.16712 0.63903 0.262
## Pr(>|z|)
## (Intercept) 0.047812 *
## spark_stock_ticket 0.085391 .
## category_by_size_melseMicro 0.000586 ***
## administrative_position 0.176402
## in_political_party 0.699776
## in_association_or_sro 0.020760 *
## case_publications 0.001554 **
## criminal_prosecution 0.814476
## capture 0.023742 *
## corruption 0.707186
## barriers 0.753562
## have_court_case 0.165563
## is_guilty 0.537751
## reviewed_by_bac 0.942139
## cop_stageInformation_collection 0.944688
## cop_stageResolution 0.128973
## to_ombudsman 0.124066
## macro_okved_code_groupFinancial_insurance 0.257708
## macro_okved_code_groupmanufacturing 0.349740
## macro_okved_code_groupother_categories 0.266331
## macro_okved_code_groupreal_estate 0.000902 ***
## macro_okved_code_grouprural 0.180471
## macro_okved_code_groupScience 0.005881 **
## macro_okved_code_groupTrading 0.343022
## macro_okved_code_groupTransportation 0.793687
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 488.55 on 355 degrees of freedom
## Residual deviance: 419.99 on 331 degrees of freedom
## AIC: 469.99
##
## Number of Fisher Scoring iterations: 4
logit_2<-stepAIC(logit_1)
## Start: AIC=469.99
## target_light_extended ~ spark_stock_ticket + category_by_size_melse +
## administrative_position + in_political_party + in_association_or_sro +
## case_publications + criminal_prosecution + capture + corruption +
## barriers + have_court_case + is_guilty + reviewed_by_bac +
## cop_stage + to_ombudsman + macro_okved_code_group
##
## Df Deviance AIC
## - reviewed_by_bac 1 419.99 467.99
## - criminal_prosecution 1 420.04 468.04
## - barriers 1 420.09 468.09
## - corruption 1 420.13 468.13
## - in_political_party 1 420.14 468.14
## - is_guilty 1 420.37 468.37
## - administrative_position 1 421.86 469.86
## - have_court_case 1 421.94 469.94
## <none> 419.99 469.99
## - to_ombudsman 1 422.41 470.41
## - spark_stock_ticket 1 422.99 470.99
## - cop_stage 2 425.73 471.73
## - capture 1 425.26 473.26
## - in_association_or_sro 1 425.44 473.44
## - macro_okved_code_group 8 443.07 477.07
## - case_publications 1 430.75 478.75
## - category_by_size_melse 1 432.32 480.32
##
## Step: AIC=467.99
## target_light_extended ~ spark_stock_ticket + category_by_size_melse +
## administrative_position + in_political_party + in_association_or_sro +
## case_publications + criminal_prosecution + capture + corruption +
## barriers + have_court_case + is_guilty + cop_stage + to_ombudsman +
## macro_okved_code_group
##
## Df Deviance AIC
## - criminal_prosecution 1 420.06 466.06
## - barriers 1 420.10 466.10
## - corruption 1 420.13 466.13
## - in_political_party 1 420.14 466.14
## - is_guilty 1 420.39 466.39
## - administrative_position 1 421.87 467.87
## - have_court_case 1 421.96 467.96
## <none> 419.99 467.99
## - to_ombudsman 1 422.41 468.41
## - spark_stock_ticket 1 423.04 469.04
## - capture 1 425.44 471.44
## - in_association_or_sro 1 425.48 471.48
## - cop_stage 2 427.54 471.54
## - macro_okved_code_group 8 443.18 475.18
## - case_publications 1 430.80 476.80
## - category_by_size_melse 1 432.33 478.33
##
## Step: AIC=466.06
## target_light_extended ~ spark_stock_ticket + category_by_size_melse +
## administrative_position + in_political_party + in_association_or_sro +
## case_publications + capture + corruption + barriers + have_court_case +
## is_guilty + cop_stage + to_ombudsman + macro_okved_code_group
##
## Df Deviance AIC
## - barriers 1 420.10 464.10
## - in_political_party 1 420.20 464.20
## - corruption 1 420.29 464.29
## - is_guilty 1 420.47 464.47
## - administrative_position 1 421.90 465.90
## <none> 420.06 466.06
## - have_court_case 1 422.09 466.09
## - to_ombudsman 1 422.74 466.74
## - spark_stock_ticket 1 423.08 467.08
## - in_association_or_sro 1 425.49 469.49
## - cop_stage 2 427.59 469.59
## - capture 1 426.91 470.91
## - macro_okved_code_group 8 444.21 474.21
## - case_publications 1 431.16 475.16
## - category_by_size_melse 1 432.36 476.36
##
## Step: AIC=464.1
## target_light_extended ~ spark_stock_ticket + category_by_size_melse +
## administrative_position + in_political_party + in_association_or_sro +
## case_publications + capture + corruption + have_court_case +
## is_guilty + cop_stage + to_ombudsman + macro_okved_code_group
##
## Df Deviance AIC
## - in_political_party 1 420.24 462.24
## - corruption 1 420.35 462.35
## - is_guilty 1 420.54 462.54
## - administrative_position 1 421.95 463.95
## <none> 420.10 464.10
## - have_court_case 1 422.21 464.21
## - to_ombudsman 1 422.90 464.90
## - spark_stock_ticket 1 423.10 465.10
## - in_association_or_sro 1 425.57 467.57
## - cop_stage 2 427.62 467.62
## - capture 1 427.03 469.03
## - macro_okved_code_group 8 444.21 472.21
## - case_publications 1 431.17 473.17
## - category_by_size_melse 1 432.38 474.38
##
## Step: AIC=462.24
## target_light_extended ~ spark_stock_ticket + category_by_size_melse +
## administrative_position + in_association_or_sro + case_publications +
## capture + corruption + have_court_case + is_guilty + cop_stage +
## to_ombudsman + macro_okved_code_group
##
## Df Deviance AIC
## - corruption 1 420.47 460.47
## - is_guilty 1 420.71 460.71
## - administrative_position 1 422.21 462.21
## <none> 420.24 462.24
## - have_court_case 1 422.38 462.38
## - to_ombudsman 1 422.95 462.95
## - spark_stock_ticket 1 423.19 463.19
## - in_association_or_sro 1 425.64 465.64
## - cop_stage 2 427.77 465.77
## - capture 1 427.18 467.18
## - macro_okved_code_group 8 444.24 470.24
## - case_publications 1 431.44 471.44
## - category_by_size_melse 1 432.42 472.42
##
## Step: AIC=460.47
## target_light_extended ~ spark_stock_ticket + category_by_size_melse +
## administrative_position + in_association_or_sro + case_publications +
## capture + have_court_case + is_guilty + cop_stage + to_ombudsman +
## macro_okved_code_group
##
## Df Deviance AIC
## - is_guilty 1 420.92 458.92
## <none> 420.47 460.47
## - administrative_position 1 422.50 460.50
## - have_court_case 1 422.52 460.52
## - to_ombudsman 1 423.25 461.25
## - spark_stock_ticket 1 423.30 461.30
## - in_association_or_sro 1 425.85 463.85
## - cop_stage 2 428.01 464.01
## - capture 1 427.22 465.22
## - macro_okved_code_group 8 444.45 468.45
## - case_publications 1 432.11 470.11
## - category_by_size_melse 1 432.75 470.75
##
## Step: AIC=458.92
## target_light_extended ~ spark_stock_ticket + category_by_size_melse +
## administrative_position + in_association_or_sro + case_publications +
## capture + have_court_case + cop_stage + to_ombudsman + macro_okved_code_group
##
## Df Deviance AIC
## - have_court_case 1 422.81 458.81
## <none> 420.92 458.92
## - administrative_position 1 423.11 459.11
## - to_ombudsman 1 423.60 459.60
## - spark_stock_ticket 1 423.94 459.94
## - in_association_or_sro 1 426.35 462.35
## - cop_stage 2 428.40 462.40
## - capture 1 427.88 463.88
## - macro_okved_code_group 8 445.17 467.17
## - case_publications 1 432.48 468.48
## - category_by_size_melse 1 432.97 468.97
##
## Step: AIC=458.81
## target_light_extended ~ spark_stock_ticket + category_by_size_melse +
## administrative_position + in_association_or_sro + case_publications +
## capture + cop_stage + to_ombudsman + macro_okved_code_group
##
## Df Deviance AIC
## <none> 422.81 458.81
## - administrative_position 1 424.91 458.91
## - to_ombudsman 1 425.40 459.40
## - spark_stock_ticket 1 425.77 459.77
## - in_association_or_sro 1 428.16 462.16
## - cop_stage 2 430.50 462.50
## - capture 1 429.33 463.33
## - macro_okved_code_group 8 446.88 466.88
## - category_by_size_melse 1 434.09 468.09
## - case_publications 1 434.36 468.36
summary(logit_2)
##
## Call:
## glm(formula = target_light_extended ~ spark_stock_ticket + category_by_size_melse +
## administrative_position + in_association_or_sro + case_publications +
## capture + cop_stage + to_ombudsman + macro_okved_code_group,
## family = binomial, data = light_extended_data)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.9357 -1.0622 0.5301 0.9755 1.8390
##
## Coefficients:
## Estimate Std. Error z value
## (Intercept) 1.434658 0.526480 2.725
## spark_stock_ticket -1.224267 0.717167 -1.707
## category_by_size_melseMicro -0.889028 0.269579 -3.298
## administrative_position 0.549510 0.385022 1.427
## in_association_or_sro 0.612282 0.267207 2.291
## case_publications -1.112793 0.339810 -3.275
## capture -0.692848 0.273825 -2.530
## cop_stageInformation_collection 0.007352 0.372513 0.020
## cop_stageResolution -0.712629 0.307007 -2.321
## to_ombudsman 0.585337 0.367975 1.591
## macro_okved_code_groupFinancial_insurance -0.683462 0.689718 -0.991
## macro_okved_code_groupmanufacturing 0.485564 0.409709 1.185
## macro_okved_code_groupother_categories 0.516540 0.434271 1.189
## macro_okved_code_groupreal_estate 1.765532 0.523123 3.375
## macro_okved_code_grouprural 0.936868 0.584816 1.602
## macro_okved_code_groupScience 1.485117 0.510388 2.910
## macro_okved_code_groupTrading 0.410991 0.410179 1.002
## macro_okved_code_groupTransportation 0.213553 0.633143 0.337
## Pr(>|z|)
## (Intercept) 0.006430 **
## spark_stock_ticket 0.087805 .
## category_by_size_melseMicro 0.000974 ***
## administrative_position 0.153517
## in_association_or_sro 0.021940 *
## case_publications 0.001058 **
## capture 0.011398 *
## cop_stageInformation_collection 0.984255
## cop_stageResolution 0.020275 *
## to_ombudsman 0.111678
## macro_okved_code_groupFinancial_insurance 0.321720
## macro_okved_code_groupmanufacturing 0.235960
## macro_okved_code_groupother_categories 0.234266
## macro_okved_code_groupreal_estate 0.000738 ***
## macro_okved_code_grouprural 0.109158
## macro_okved_code_groupScience 0.003617 **
## macro_okved_code_groupTrading 0.316353
## macro_okved_code_groupTransportation 0.735898
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 488.55 on 355 degrees of freedom
## Residual deviance: 422.81 on 338 degrees of freedom
## AIC: 458.81
##
## Number of Fisher Scoring iterations: 4
car::vif(logit_2)
## GVIF Df GVIF^(1/(2*Df))
## spark_stock_ticket 1.126721 1 1.061471
## category_by_size_melse 1.213062 1 1.101391
## administrative_position 1.074237 1 1.036454
## in_association_or_sro 1.183608 1 1.087937
## case_publications 1.147771 1 1.071341
## capture 1.135363 1 1.065534
## cop_stage 1.314410 2 1.070737
## to_ombudsman 1.193214 1 1.092344
## macro_okved_code_group 1.620715 8 1.030639
anova(logit_2, test="Chisq")
## Analysis of Deviance Table
##
## Model: binomial, link: logit
##
## Response: target_light_extended
##
## Terms added sequentially (first to last)
##
##
## Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL 355 488.55
## spark_stock_ticket 1 0.4978 354 488.06 0.4804672
## category_by_size_melse 1 11.1542 353 476.90 0.0008384 ***
## administrative_position 1 1.6206 352 475.28 0.2030147
## in_association_or_sro 1 3.6751 351 471.61 0.0552321 .
## case_publications 1 11.4231 350 460.18 0.0007254 ***
## capture 1 4.4979 349 455.69 0.0339364 *
## cop_stage 2 4.6977 347 450.99 0.0954788 .
## to_ombudsman 1 4.1090 346 446.88 0.0426553 *
## macro_okved_code_group 8 24.0669 338 422.81 0.0022334 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
TARGET 4 - TARGET STRONG EXTENDED Baseline
strong_extended_vars <-c(
c(
#"federal_districts",
#"largest_fed_districts",
#"macro_okved_code",
"macro_okved_code_group",
#"spark_web_site",
"spark_stock_ticket",
"category_by_size_missing",
#"category_by_size_melse",
#"category_by_size_2_cat",
"administrative_position",
"administrative_connections",
"in_political_party",
"in_association_or_sro",
"case_publications",
"criminal_prosecution",
"capture",
"corruption",
"barriers",
"have_court_case",
"is_guilty",
# "reviewed_by_bac",
# "supported_by_bac_public_council",
# "max_bac_stage",
# "cop_stage",
"reaction_not_passed_by_applicant",
"reaction_consultation",
"reaction_target_letters_control",
# "reaction_not_passed_by_bac",
"to_ombudsman",
"target_strong_extended")
)
strong_extended_data <-dataset[strong_extended_vars]
strong_extended_data =strong_extended_data[!is.na(strong_extended_data$target_strong_extended),]
strong_extended_data =strong_extended_data[!is.na(strong_extended_data$category_by_size_missing),]
strong_extended_data$macro_okved_code_group <-factor(strong_extended_data$macro_okved_code_group)
strong_extended_data$category_by_size_missing <-factor(strong_extended_data$category_by_size_missing)
logit_1<-glm(target_strong_extended~., family = binomial,data = strong_extended_data)
summary(logit_1)
##
## Call:
## glm(formula = target_strong_extended ~ ., family = binomial,
## data = strong_extended_data)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.5179 -0.7133 -0.5049 -0.1931 2.4325
##
## Coefficients:
## Estimate Std. Error z value
## (Intercept) -0.25129 0.85921 -0.292
## macro_okved_code_groupFinancial_insurance 0.04822 0.85316 0.057
## macro_okved_code_groupmanufacturing 0.86318 0.49094 1.758
## macro_okved_code_groupother_categories -0.24455 0.57118 -0.428
## macro_okved_code_groupreal_estate 1.12305 0.54285 2.069
## macro_okved_code_grouprural 0.29233 0.69294 0.422
## macro_okved_code_groupScience 1.42021 0.50540 2.810
## macro_okved_code_groupTrading -0.03397 0.52445 -0.065
## macro_okved_code_groupTransportation -1.37261 1.17756 -1.166
## spark_stock_ticket -1.19797 0.88498 -1.354
## category_by_size_missingMedium -0.01702 0.68677 -0.025
## category_by_size_missingMicro -0.75481 0.54976 -1.373
## category_by_size_missingSmall -0.90844 0.60008 -1.514
## administrative_position 0.21246 0.53336 0.398
## administrative_connections -0.23835 0.34537 -0.690
## in_political_party 0.17642 0.45989 0.384
## in_association_or_sro 0.69565 0.30427 2.286
## case_publications -0.64669 0.37281 -1.735
## criminal_prosecution -0.41563 0.52751 -0.788
## capture -0.87882 0.43134 -2.037
## corruption -0.16717 0.59570 -0.281
## barriers 0.41658 0.60697 0.686
## have_court_case 0.02304 0.42099 0.055
## is_guilty -0.14760 0.43214 -0.342
## reaction_not_passed_by_applicant -0.58742 0.50631 -1.160
## reaction_consultation 2.64920 0.81557 3.248
## reaction_target_letters_control 0.68750 0.38723 1.775
## to_ombudsman -0.01794 0.45386 -0.040
## Pr(>|z|)
## (Intercept) 0.76993
## macro_okved_code_groupFinancial_insurance 0.95493
## macro_okved_code_groupmanufacturing 0.07871 .
## macro_okved_code_groupother_categories 0.66855
## macro_okved_code_groupreal_estate 0.03856 *
## macro_okved_code_grouprural 0.67312
## macro_okved_code_groupScience 0.00495 **
## macro_okved_code_groupTrading 0.94835
## macro_okved_code_groupTransportation 0.24376
## spark_stock_ticket 0.17584
## category_by_size_missingMedium 0.98023
## category_by_size_missingMicro 0.16975
## category_by_size_missingSmall 0.13006
## administrative_position 0.69038
## administrative_connections 0.49012
## in_political_party 0.70127
## in_association_or_sro 0.02224 *
## case_publications 0.08280 .
## criminal_prosecution 0.43075
## capture 0.04161 *
## corruption 0.77899
## barriers 0.49251
## have_court_case 0.95635
## is_guilty 0.73268
## reaction_not_passed_by_applicant 0.24597
## reaction_consultation 0.00116 **
## reaction_target_letters_control 0.07583 .
## to_ombudsman 0.96847
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 395.93 on 355 degrees of freedom
## Residual deviance: 338.42 on 328 degrees of freedom
## AIC: 394.42
##
## Number of Fisher Scoring iterations: 5
car::vif(logit_1)
## GVIF Df GVIF^(1/(2*Df))
## macro_okved_code_group 2.656922 8 1.062977
## spark_stock_ticket 1.327025 1 1.151966
## category_by_size_missing 2.157560 3 1.136738
## administrative_position 1.808570 1 1.344831
## administrative_connections 1.518568 1 1.232302
## in_political_party 1.569965 1 1.252982
## in_association_or_sro 1.252150 1 1.118995
## case_publications 1.249895 1 1.117987
## criminal_prosecution 3.316870 1 1.821228
## capture 1.836192 1 1.355062
## corruption 1.319348 1 1.148629
## barriers 2.775154 1 1.665879
## have_court_case 2.435723 1 1.560680
## is_guilty 2.378458 1 1.542225
## reaction_not_passed_by_applicant 1.080046 1 1.039253
## reaction_consultation 1.286877 1 1.134406
## reaction_target_letters_control 1.274127 1 1.128772
## to_ombudsman 1.426453 1 1.194342
anova(logit_1, test="Chisq")
## Analysis of Deviance Table
##
## Model: binomial, link: logit
##
## Response: target_strong_extended
##
## Terms added sequentially (first to last)
##
##
## Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL 355 395.93
## macro_okved_code_group 8 20.5731 347 375.35 0.008372
## spark_stock_ticket 1 0.0003 346 375.35 0.986495
## category_by_size_missing 3 5.6251 343 369.73 0.131342
## administrative_position 1 0.2227 342 369.51 0.637015
## administrative_connections 1 0.3773 341 369.13 0.539066
## in_political_party 1 0.0847 340 369.04 0.771090
## in_association_or_sro 1 3.5315 339 365.51 0.060213
## case_publications 1 4.3352 338 361.18 0.037331
## criminal_prosecution 1 0.4775 337 360.70 0.489572
## capture 1 4.5831 336 356.12 0.032289
## corruption 1 0.0934 335 356.02 0.759937
## barriers 1 1.1267 334 354.90 0.288479
## have_court_case 1 0.0759 333 354.82 0.782884
## is_guilty 1 0.1509 332 354.67 0.697687
## reaction_not_passed_by_applicant 1 3.0560 331 351.61 0.080439
## reaction_consultation 1 9.9838 330 341.63 0.001579
## reaction_target_letters_control 1 3.2054 329 338.43 0.073394
## to_ombudsman 1 0.0016 328 338.42 0.968456
##
## NULL
## macro_okved_code_group **
## spark_stock_ticket
## category_by_size_missing
## administrative_position
## administrative_connections
## in_political_party
## in_association_or_sro .
## case_publications *
## criminal_prosecution
## capture *
## corruption
## barriers
## have_court_case
## is_guilty
## reaction_not_passed_by_applicant .
## reaction_consultation **
## reaction_target_letters_control .
## to_ombudsman
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
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