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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By hand:
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_2_cat),]
strong_extended_data$macro_okved_code_group <-factor(strong_extended_data$macro_okved_code_group)
strong_extended_data$category_by_size_2_cat <-factor(strong_extended_data$category_by_size_2_cat)
# wel;, i can live with this model, it looks legit.
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
## -1.5792 -0.7123 -0.5016 -0.2054 2.5067
##
## Coefficients:
## Estimate Std. Error z value
## (Intercept) -0.61723 0.59213 -1.042
## macro_okved_code_groupFinancial_insurance -0.01786 0.82623 -0.022
## macro_okved_code_groupmanufacturing 0.80236 0.46073 1.742
## macro_okved_code_groupother_categories -0.28843 0.55454 -0.520
## macro_okved_code_groupreal_estate 1.17991 0.53559 2.203
## macro_okved_code_grouprural 0.22187 0.67722 0.328
## macro_okved_code_groupScience 1.42270 0.50020 2.844
## macro_okved_code_groupTrading -0.06188 0.51695 -0.120
## macro_okved_code_groupTransportation -1.33991 1.16472 -1.150
## spark_stock_ticket -1.18296 0.85016 -1.391
## category_by_size_2_catSmall -0.79715 0.38216 -2.086
## in_association_or_sro 0.64009 0.29478 2.171
## case_publications -0.70082 0.35699 -1.963
## capture -0.69387 0.34677 -2.001
## corruption 0.01236 0.52407 0.024
## barriers 0.72293 0.39079 1.850
## reaction_not_passed_by_applicant -0.58117 0.49601 -1.172
## reaction_consultation 2.55377 0.78542 3.251
## reaction_target_letters_control 0.61308 0.36438 1.683
## Pr(>|z|)
## (Intercept) 0.29723
## macro_okved_code_groupFinancial_insurance 0.98275
## macro_okved_code_groupmanufacturing 0.08159 .
## macro_okved_code_groupother_categories 0.60298
## macro_okved_code_groupreal_estate 0.02759 *
## macro_okved_code_grouprural 0.74320
## macro_okved_code_groupScience 0.00445 **
## macro_okved_code_groupTrading 0.90472
## macro_okved_code_groupTransportation 0.24997
## spark_stock_ticket 0.16409
## category_by_size_2_catSmall 0.03699 *
## in_association_or_sro 0.02990 *
## case_publications 0.04963 *
## capture 0.04540 *
## corruption 0.98118
## barriers 0.06433 .
## reaction_not_passed_by_applicant 0.24132
## reaction_consultation 0.00115 **
## reaction_target_letters_control 0.09246 .
## ---
## 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: 340.21 on 337 degrees of freedom
## AIC: 378.21
##
## Number of Fisher Scoring iterations: 5
car::vif(logit_1)
## GVIF Df GVIF^(1/(2*Df))
## macro_okved_code_group 1.761003 8 1.036001
## spark_stock_ticket 1.222414 1 1.105629
## category_by_size_2_cat 1.352411 1 1.162932
## in_association_or_sro 1.180877 1 1.086681
## case_publications 1.155345 1 1.074870
## capture 1.194040 1 1.092721
## corruption 1.056409 1 1.027817
## barriers 1.153644 1 1.074078
## reaction_not_passed_by_applicant 1.043862 1 1.021695
## reaction_consultation 1.220188 1 1.104621
## reaction_target_letters_control 1.140547 1 1.067964
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_2_cat 1 5.5582 345 369.80 0.018394
## in_association_or_sro 1 3.2220 344 366.57 0.072656
## case_publications 1 4.4656 343 362.11 0.034584
## capture 1 3.3378 342 358.77 0.067707
## corruption 1 0.0011 341 358.77 0.973767
## barriers 1 2.7330 340 356.04 0.098293
## reaction_not_passed_by_applicant 1 3.1527 339 352.88 0.075803
## reaction_consultation 1 9.9157 338 342.97 0.001639
## reaction_target_letters_control 1 2.7620 337 340.21 0.096525
##
## NULL
## macro_okved_code_group **
## spark_stock_ticket
## category_by_size_2_cat *
## in_association_or_sro .
## case_publications *
## capture .
## corruption
## barriers .
## reaction_not_passed_by_applicant .
## reaction_consultation **
## reaction_target_letters_control .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
AIC Maximization:
strong_extended_vars <-c(
c(
#"federal_districts",
#"largest_fed_districts",
#"macro_okved_code",
#"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_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_2_cat),]
strong_extended_data$macro_okved_code_group <-factor(strong_extended_data$macro_okved_code_group)
strong_extended_data$category_by_size_2_cat <-factor(strong_extended_data$category_by_size_2_cat)
logit_1<-glm(target_strong_extended~., family = binomial,data = strong_extended_data)
logit_2<-stepAIC(logit_1)
## Start: AIC=401.35
## target_strong_extended ~ spark_stock_ticket + 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 +
## reaction_not_passed_by_applicant + reaction_target_letters_control +
## reaction_not_passed_by_bac + to_ombudsman + macro_okved_code_group
##
## Df Deviance AIC
## - reaction_not_passed_by_bac 1 349.35 399.35
## - have_court_case 1 349.35 399.35
## - corruption 1 349.35 399.35
## - to_ombudsman 1 349.43 399.43
## - administrative_position 1 349.55 399.55
## - in_political_party 1 349.56 399.56
## - is_guilty 1 349.59 399.59
## - criminal_prosecution 1 349.59 399.59
## - administrative_connections 1 349.84 399.84
## - spark_stock_ticket 1 350.02 400.02
## - barriers 1 350.09 400.09
## <none> 349.35 401.35
## - reaction_target_letters_control 1 351.36 401.36
## - reaction_not_passed_by_applicant 1 351.65 401.65
## - capture 1 351.70 401.70
## - case_publications 1 352.43 402.43
## - in_association_or_sro 1 353.97 403.97
## - category_by_size_2_cat 1 354.26 404.26
## - macro_okved_code_group 8 368.57 404.57
##
## Step: AIC=399.35
## target_strong_extended ~ spark_stock_ticket + 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 +
## reaction_not_passed_by_applicant + reaction_target_letters_control +
## to_ombudsman + macro_okved_code_group
##
## Df Deviance AIC
## - have_court_case 1 349.35 397.35
## - corruption 1 349.35 397.35
## - to_ombudsman 1 349.43 397.43
## - administrative_position 1 349.55 397.55
## - in_political_party 1 349.57 397.57
## - is_guilty 1 349.59 397.59
## - criminal_prosecution 1 349.59 397.59
## - administrative_connections 1 349.84 397.84
## - spark_stock_ticket 1 350.03 398.03
## - barriers 1 350.09 398.09
## <none> 349.35 399.35
## - reaction_target_letters_control 1 351.38 399.38
## - reaction_not_passed_by_applicant 1 351.67 399.67
## - capture 1 351.71 399.71
## - case_publications 1 352.43 400.43
## - in_association_or_sro 1 353.98 401.98
## - category_by_size_2_cat 1 354.27 402.27
## - macro_okved_code_group 8 368.66 402.66
##
## Step: AIC=397.35
## target_strong_extended ~ spark_stock_ticket + category_by_size_2_cat +
## administrative_position + administrative_connections + in_political_party +
## in_association_or_sro + case_publications + criminal_prosecution +
## capture + corruption + barriers + is_guilty + reaction_not_passed_by_applicant +
## reaction_target_letters_control + to_ombudsman + macro_okved_code_group
##
## Df Deviance AIC
## - corruption 1 349.35 395.35
## - to_ombudsman 1 349.43 395.43
## - administrative_position 1 349.57 395.57
## - in_political_party 1 349.57 395.57
## - criminal_prosecution 1 349.60 395.60
## - is_guilty 1 349.78 395.78
## - administrative_connections 1 349.84 395.84
## - spark_stock_ticket 1 350.04 396.04
## - barriers 1 350.10 396.10
## <none> 349.35 397.35
## - reaction_target_letters_control 1 351.39 397.39
## - reaction_not_passed_by_applicant 1 351.67 397.67
## - capture 1 351.73 397.73
## - case_publications 1 352.45 398.45
## - in_association_or_sro 1 353.98 399.98
## - category_by_size_2_cat 1 354.28 400.28
## - macro_okved_code_group 8 368.72 400.72
##
## Step: AIC=395.35
## target_strong_extended ~ spark_stock_ticket + category_by_size_2_cat +
## administrative_position + administrative_connections + in_political_party +
## in_association_or_sro + case_publications + criminal_prosecution +
## capture + barriers + is_guilty + reaction_not_passed_by_applicant +
## reaction_target_letters_control + to_ombudsman + macro_okved_code_group
##
## Df Deviance AIC
## - to_ombudsman 1 349.44 393.44
## - administrative_position 1 349.57 393.57
## - in_political_party 1 349.57 393.57
## - criminal_prosecution 1 349.68 393.68
## - is_guilty 1 349.79 393.79
## - administrative_connections 1 349.84 393.84
## - spark_stock_ticket 1 350.05 394.05
## - barriers 1 350.16 394.16
## <none> 349.35 395.35
## - reaction_target_letters_control 1 351.41 395.41
## - reaction_not_passed_by_applicant 1 351.67 395.67
## - capture 1 351.79 395.79
## - case_publications 1 352.51 396.51
## - in_association_or_sro 1 353.98 397.98
## - category_by_size_2_cat 1 354.28 398.28
## - macro_okved_code_group 8 368.79 398.79
##
## Step: AIC=393.44
## target_strong_extended ~ spark_stock_ticket + category_by_size_2_cat +
## administrative_position + administrative_connections + in_political_party +
## in_association_or_sro + case_publications + criminal_prosecution +
## capture + barriers + is_guilty + reaction_not_passed_by_applicant +
## reaction_target_letters_control + macro_okved_code_group
##
## Df Deviance AIC
## - in_political_party 1 349.64 391.64
## - administrative_position 1 349.65 391.65
## - criminal_prosecution 1 349.71 391.71
## - is_guilty 1 349.90 391.90
## - administrative_connections 1 349.94 391.94
## - spark_stock_ticket 1 350.13 392.13
## - barriers 1 350.18 392.18
## <none> 349.44 393.44
## - reaction_not_passed_by_applicant 1 351.68 393.68
## - reaction_target_letters_control 1 351.71 393.71
## - capture 1 351.84 393.84
## - case_publications 1 352.56 394.56
## - in_association_or_sro 1 354.11 396.11
## - category_by_size_2_cat 1 354.40 396.40
## - macro_okved_code_group 8 369.13 397.13
##
## Step: AIC=391.64
## target_strong_extended ~ spark_stock_ticket + category_by_size_2_cat +
## administrative_position + administrative_connections + in_association_or_sro +
## case_publications + criminal_prosecution + capture + barriers +
## is_guilty + reaction_not_passed_by_applicant + reaction_target_letters_control +
## macro_okved_code_group
##
## Df Deviance AIC
## - criminal_prosecution 1 349.94 389.94
## - is_guilty 1 350.07 390.07
## - administrative_connections 1 350.09 390.09
## - administrative_position 1 350.21 390.21
## - barriers 1 350.36 390.36
## - spark_stock_ticket 1 350.37 390.37
## <none> 349.64 391.64
## - reaction_target_letters_control 1 351.82 391.82
## - reaction_not_passed_by_applicant 1 351.90 391.90
## - capture 1 352.03 392.03
## - case_publications 1 352.73 392.73
## - in_association_or_sro 1 354.40 394.40
## - category_by_size_2_cat 1 354.69 394.69
## - macro_okved_code_group 8 369.20 395.20
##
## Step: AIC=389.94
## target_strong_extended ~ spark_stock_ticket + category_by_size_2_cat +
## administrative_position + administrative_connections + in_association_or_sro +
## case_publications + capture + barriers + is_guilty + reaction_not_passed_by_applicant +
## reaction_target_letters_control + macro_okved_code_group
##
## Df Deviance AIC
## - is_guilty 1 350.35 388.35
## - administrative_connections 1 350.38 388.38
## - administrative_position 1 350.47 388.47
## - spark_stock_ticket 1 350.69 388.69
## - reaction_target_letters_control 1 351.93 389.93
## <none> 349.94 389.94
## - capture 1 352.14 390.14
## - reaction_not_passed_by_applicant 1 352.16 390.16
## - barriers 1 352.79 390.79
## - case_publications 1 353.35 391.35
## - in_association_or_sro 1 354.57 392.57
## - category_by_size_2_cat 1 355.07 393.07
## - macro_okved_code_group 8 369.95 393.95
##
## Step: AIC=388.35
## target_strong_extended ~ spark_stock_ticket + category_by_size_2_cat +
## administrative_position + administrative_connections + in_association_or_sro +
## case_publications + capture + barriers + reaction_not_passed_by_applicant +
## reaction_target_letters_control + macro_okved_code_group
##
## Df Deviance AIC
## - administrative_connections 1 350.80 386.80
## - administrative_position 1 351.01 387.01
## - spark_stock_ticket 1 351.19 387.19
## - reaction_target_letters_control 1 352.13 388.13
## <none> 350.35 388.35
## - reaction_not_passed_by_applicant 1 352.58 388.58
## - capture 1 352.67 388.67
## - barriers 1 353.19 389.19
## - case_publications 1 353.72 389.72
## - in_association_or_sro 1 355.04 391.04
## - category_by_size_2_cat 1 355.47 391.47
## - macro_okved_code_group 8 370.19 392.19
##
## Step: AIC=386.8
## target_strong_extended ~ spark_stock_ticket + category_by_size_2_cat +
## administrative_position + in_association_or_sro + case_publications +
## capture + barriers + reaction_not_passed_by_applicant + reaction_target_letters_control +
## macro_okved_code_group
##
## Df Deviance AIC
## - administrative_position 1 351.13 385.13
## - spark_stock_ticket 1 351.53 385.53
## - reaction_target_letters_control 1 352.58 386.58
## <none> 350.80 386.80
## - capture 1 353.04 387.04
## - reaction_not_passed_by_applicant 1 353.11 387.11
## - barriers 1 353.45 387.45
## - case_publications 1 354.89 388.89
## - in_association_or_sro 1 355.19 389.19
## - category_by_size_2_cat 1 355.71 389.71
## - macro_okved_code_group 8 370.63 390.63
##
## Step: AIC=385.13
## target_strong_extended ~ spark_stock_ticket + category_by_size_2_cat +
## in_association_or_sro + case_publications + capture + barriers +
## reaction_not_passed_by_applicant + reaction_target_letters_control +
## macro_okved_code_group
##
## Df Deviance AIC
## - spark_stock_ticket 1 351.86 383.86
## - reaction_target_letters_control 1 352.94 384.94
## <none> 351.13 385.13
## - reaction_not_passed_by_applicant 1 353.47 385.47
## - barriers 1 353.65 385.65
## - capture 1 353.66 385.66
## - case_publications 1 354.98 386.98
## - in_association_or_sro 1 355.49 387.49
## - category_by_size_2_cat 1 356.50 388.50
## - macro_okved_code_group 8 371.16 389.16
##
## Step: AIC=383.86
## target_strong_extended ~ category_by_size_2_cat + in_association_or_sro +
## case_publications + capture + barriers + reaction_not_passed_by_applicant +
## reaction_target_letters_control + macro_okved_code_group
##
## Df Deviance AIC
## - reaction_target_letters_control 1 353.55 383.55
## <none> 351.86 383.86
## - capture 1 354.18 384.18
## - reaction_not_passed_by_applicant 1 354.27 384.27
## - barriers 1 354.59 384.59
## - case_publications 1 355.83 385.83
## - in_association_or_sro 1 355.96 385.96
## - category_by_size_2_cat 1 356.53 386.53
## - macro_okved_code_group 8 371.58 387.58
##
## Step: AIC=383.55
## target_strong_extended ~ category_by_size_2_cat + in_association_or_sro +
## case_publications + capture + barriers + reaction_not_passed_by_applicant +
## macro_okved_code_group
##
## Df Deviance AIC
## - capture 1 355.18 383.18
## <none> 353.55 383.55
## - barriers 1 356.33 384.33
## - reaction_not_passed_by_applicant 1 356.72 384.72
## - case_publications 1 357.17 385.17
## - in_association_or_sro 1 357.80 385.80
## - category_by_size_2_cat 1 358.20 386.20
## - macro_okved_code_group 8 372.65 386.65
##
## Step: AIC=383.18
## target_strong_extended ~ category_by_size_2_cat + in_association_or_sro +
## case_publications + barriers + reaction_not_passed_by_applicant +
## macro_okved_code_group
##
## Df Deviance AIC
## <none> 355.18 383.18
## - reaction_not_passed_by_applicant 1 358.67 384.67
## - case_publications 1 358.87 384.87
## - barriers 1 359.01 385.01
## - category_by_size_2_cat 1 359.26 385.26
## - in_association_or_sro 1 359.38 385.38
## - macro_okved_code_group 8 374.39 386.39
summary(logit_2)
##
## Call:
## glm(formula = target_strong_extended ~ category_by_size_2_cat +
## in_association_or_sro + case_publications + barriers + reaction_not_passed_by_applicant +
## macro_okved_code_group, family = binomial, data = strong_extended_data)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.4992 -0.7111 -0.5439 -0.2892 2.4107
##
## Coefficients:
## Estimate Std. Error z value
## (Intercept) -0.67881 0.55190 -1.230
## category_by_size_2_catSmall -0.71456 0.34968 -2.043
## in_association_or_sro 0.58461 0.28534 2.049
## case_publications -0.66019 0.33963 -1.944
## barriers 0.74107 0.37321 1.986
## reaction_not_passed_by_applicant -0.83767 0.48050 -1.743
## macro_okved_code_groupFinancial_insurance 0.24014 0.75156 0.320
## macro_okved_code_groupmanufacturing 0.66854 0.45108 1.482
## macro_okved_code_groupother_categories -0.20177 0.53818 -0.375
## macro_okved_code_groupreal_estate 1.11602 0.51839 2.153
## macro_okved_code_grouprural 0.21715 0.66811 0.325
## macro_okved_code_groupScience 1.34919 0.48810 2.764
## macro_okved_code_groupTrading 0.04175 0.48815 0.086
## macro_okved_code_groupTransportation -1.03931 1.11085 -0.936
## Pr(>|z|)
## (Intercept) 0.21871
## category_by_size_2_catSmall 0.04100 *
## in_association_or_sro 0.04048 *
## case_publications 0.05191 .
## barriers 0.04707 *
## reaction_not_passed_by_applicant 0.08128 .
## macro_okved_code_groupFinancial_insurance 0.74933
## macro_okved_code_groupmanufacturing 0.13832
## macro_okved_code_groupother_categories 0.70772
## macro_okved_code_groupreal_estate 0.03133 *
## macro_okved_code_grouprural 0.74516
## macro_okved_code_groupScience 0.00571 **
## macro_okved_code_groupTrading 0.93183
## macro_okved_code_groupTransportation 0.34948
## ---
## 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: 355.18 on 342 degrees of freedom
## AIC: 383.18
##
## Number of Fisher Scoring iterations: 5
car::vif(logit_2)
## GVIF Df GVIF^(1/(2*Df))
## category_by_size_2_cat 1.217471 1 1.103391
## in_association_or_sro 1.158629 1 1.076396
## case_publications 1.127886 1 1.062020
## barriers 1.092392 1 1.045176
## reaction_not_passed_by_applicant 1.011663 1 1.005815
## macro_okved_code_group 1.465866 8 1.024191
anova(logit_2, 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
## category_by_size_2_cat 1 5.0364 354 390.89 0.02482
## in_association_or_sro 1 4.2433 353 386.65 0.03941
## case_publications 1 5.0902 352 381.56 0.02406
## barriers 1 3.8445 351 377.71 0.04991
## reaction_not_passed_by_applicant 1 3.3220 350 374.39 0.06836
## macro_okved_code_group 8 19.2082 342 355.18 0.01379
##
## NULL
## category_by_size_2_cat *
## in_association_or_sro *
## case_publications *
## barriers *
## reaction_not_passed_by_applicant .
## macro_okved_code_group *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
hoslem.test(strong_extended_data$target_strong_extended, fitted(logit_2))
##
## Hosmer and Lemeshow goodness of fit (GOF) test
##
## data: strong_extended_data$target_strong_extended, fitted(logit_2)
## X-squared = 5.0693, df = 8, p-value = 0.7501
TARGET 1 - IS WORKING Baseline
So we have sone interesting findings in baseline model, some significant coefficient, but the VIF-values indicates that there is an association between predictors and anova analysis shows that we still can drop something from our model.
is_working_vars <-c(
"largest_fed_districts",
"macro_okved_code_group",
#"spark_web_site",
"spark_stock_ticket",
"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",
"age_till_application_date",
#missing data
# "category_by_size_missing",
"category_by_size_melse",
# "category_by_size_2_cat",
# "auth_capital_group",
"is_working")
is_working_data <-dataset[is_working_vars]
is_working_data <-is_working_data[!is.na(is_working_data$category_by_size_melse),]
#missmap(is_working_data)
is_working_data$largest_fed_districts <-factor(is_working_data$largest_fed_districts)
is_working_data$macro_okved_code_group <-factor(is_working_data$macro_okved_code_group)
is_working_data$max_bac_stage <-factor(is_working_data$max_bac_stage)
is_working_data$category_by_size_melse <-factor(is_working_data$category_by_size_melse)
logit_1<-glm(is_working~., family = binomial,data = is_working_data)
summary(logit_1)
##
## Call:
## glm(formula = is_working ~ ., family = binomial, data = is_working_data)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.3634 -0.9705 0.3693 0.9707 2.0453
##
## Coefficients:
## Estimate Std. Error z value
## (Intercept) 0.804615 0.979971 0.821
## largest_fed_districtsFar_Siberia 0.161500 0.454986 0.355
## largest_fed_districtsMoscow 0.069991 0.400392 0.175
## largest_fed_districtsMoscow_region -0.177274 0.511318 -0.347
## largest_fed_districtsNorth_West -1.056985 0.522054 -2.025
## largest_fed_districtsSouth -0.296690 0.452169 -0.656
## largest_fed_districtsUrals -0.111428 0.549592 -0.203
## largest_fed_districtsVolga -0.118955 0.398085 -0.299
## macro_okved_code_groupFinancial_insurance -1.273100 0.710281 -1.792
## macro_okved_code_groupmanufacturing 0.153912 0.389049 0.396
## macro_okved_code_groupother_categories 0.904042 0.418949 2.158
## macro_okved_code_groupreal_estate 2.002286 0.550880 3.635
## macro_okved_code_grouprural 1.117428 0.579049 1.930
## macro_okved_code_groupScience -0.070828 0.432942 -0.164
## macro_okved_code_groupTrading -0.645869 0.383366 -1.685
## macro_okved_code_groupTransportation -0.135346 0.615500 -0.220
## spark_stock_ticket 0.742437 0.752120 0.987
## administrative_position 0.372981 0.490115 0.761
## administrative_connections 0.001203 0.300344 0.004
## in_political_party 0.333565 0.440890 0.757
## in_association_or_sro 0.936775 0.253990 3.688
## case_publications -0.438888 0.287586 -1.526
## criminal_prosecution 0.004732 0.409720 0.012
## capture -0.180519 0.331844 -0.544
## corruption -0.083031 0.461792 -0.180
## barriers 0.674081 0.504171 1.337
## have_court_case 0.454674 0.331653 1.371
## is_guilty -0.197583 0.337439 -0.586
## max_bac_stage1 -1.296368 0.826747 -1.568
## max_bac_stage2 -1.380739 0.825638 -1.672
## max_bac_stage3 -0.597084 0.968782 -0.616
## max_bac_stage4 -1.182060 0.724176 -1.632
## max_bac_stage5 -1.393615 0.870705 -1.601
## max_bac_stage6 -1.324138 0.840208 -1.576
## reaction_not_passed_by_applicant -0.730758 0.346721 -2.108
## reaction_consultation 1.303142 0.891620 1.462
## reaction_target_letters_control -0.022513 0.509017 -0.044
## reaction_not_passed_by_bac 1.510867 1.170180 1.291
## to_ombudsman 0.284756 0.413872 0.688
## age_till_application_date 0.018376 0.020545 0.894
## category_by_size_melseMicro -0.075628 0.270716 -0.279
## Pr(>|z|)
## (Intercept) 0.411612
## largest_fed_districtsFar_Siberia 0.722623
## largest_fed_districtsMoscow 0.861232
## largest_fed_districtsMoscow_region 0.728817
## largest_fed_districtsNorth_West 0.042902 *
## largest_fed_districtsSouth 0.511728
## largest_fed_districtsUrals 0.839333
## largest_fed_districtsVolga 0.765079
## macro_okved_code_groupFinancial_insurance 0.073071 .
## macro_okved_code_groupmanufacturing 0.692392
## macro_okved_code_groupother_categories 0.030937 *
## macro_okved_code_groupreal_estate 0.000278 ***
## macro_okved_code_grouprural 0.053636 .
## macro_okved_code_groupScience 0.870048
## macro_okved_code_groupTrading 0.092040 .
## macro_okved_code_groupTransportation 0.825952
## spark_stock_ticket 0.323581
## administrative_position 0.446652
## administrative_connections 0.996803
## in_political_party 0.449306
## in_association_or_sro 0.000226 ***
## case_publications 0.126983
## criminal_prosecution 0.990785
## capture 0.586450
## corruption 0.857308
## barriers 0.181220
## have_court_case 0.170396
## is_guilty 0.558186
## max_bac_stage1 0.116873
## max_bac_stage2 0.094459 .
## max_bac_stage3 0.537680
## max_bac_stage4 0.102620
## max_bac_stage5 0.109474
## max_bac_stage6 0.115034
## reaction_not_passed_by_applicant 0.035064 *
## reaction_consultation 0.143866
## reaction_target_letters_control 0.964723
## reaction_not_passed_by_bac 0.196655
## to_ombudsman 0.491435
## age_till_application_date 0.371103
## category_by_size_melseMicro 0.779967
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 598.13 on 431 degrees of freedom
## Residual deviance: 491.23 on 391 degrees of freedom
## AIC: 573.23
##
## Number of Fisher Scoring iterations: 4
car::vif(logit_1)
## GVIF Df GVIF^(1/(2*Df))
## largest_fed_districts 2.004913 7 1.050941
## macro_okved_code_group 2.627436 8 1.062235
## spark_stock_ticket 1.172922 1 1.083015
## administrative_position 1.913633 1 1.383341
## administrative_connections 1.657343 1 1.287378
## in_political_party 1.697974 1 1.303063
## in_association_or_sro 1.253176 1 1.119453
## case_publications 1.404285 1 1.185025
## criminal_prosecution 2.843178 1 1.686173
## capture 1.784746 1 1.335944
## corruption 1.260777 1 1.122843
## barriers 2.325873 1 1.525081
## have_court_case 2.301463 1 1.517058
## is_guilty 2.244000 1 1.497999
## max_bac_stage 5.706634 6 1.156197
## reaction_not_passed_by_applicant 1.253997 1 1.119820
## reaction_consultation 1.328460 1 1.152588
## reaction_target_letters_control 2.807208 1 1.675472
## reaction_not_passed_by_bac 1.081891 1 1.040140
## to_ombudsman 1.615043 1 1.270844
## age_till_application_date 1.299009 1 1.139741
## category_by_size_melse 1.390360 1 1.179135
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