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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## is_working_dums$is_working
## is_working_dums$largest_fed_districtsMoscow 0 1
## 0 201 178
## 1 54 49
tst =chisq.test(xtabs(~is_working_dums$largest_fed_districtsMoscow +is_working_dums$is_working))
tst$observed
## is_working_dums$is_working
## is_working_dums$largest_fed_districtsMoscow 0 1
## 0 201 178
## 1 54 49
tst$expected
## is_working_dums$is_working
## is_working_dums$largest_fed_districtsMoscow 0 1
## 0 200.5083 178.4917
## 1 54.4917 48.5083
And repeat the same steps for each target variable.
Target: target_light_clear
# check for distributions
light_clear_vars <-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",
"max_bac_stage", "supported_by_bac_public_council",
"reaction_not_passed_by_applicant", "reaction_consultation",
"reaction_target_letters_control", "to_ombudsman",
"reaction_not_passed_by_bac",
"auth_capital_group",
"cop_stage",
"target_light_clear")
light_clear_cs <-dataset[light_clear_vars]
# exclude missing data of target variables
light_clear_cs <-light_clear_cs[!is.na(light_clear_cs$target_light_clear),]
# this is a very log output, it just returns xtabs for each variable and a target
#for (i in 1:length(is_working_vars)){
# print(xtabs(~light_clear_cs$target_light_clear + light_clear_cs[,i]))
#}
# variables to worry about: federal_districts, macro_okved_code, max_bac_stage
light_clear_vars <-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",
"max_bac_stage", "supported_by_bac_public_council",
"reaction_not_passed_by_applicant", "reaction_consultation",
"reaction_target_letters_control", "to_ombudsman",
"reaction_not_passed_by_bac",
"auth_capital_group",
"cop_stage",
"target_light_clear")
light_clear_cs <-dataset[light_clear_vars]
# exclude missing data of target variables
light_clear_cs <-light_clear_cs[!is.na(light_clear_cs$target_light_clear),]
light_clear_cs_mat_stat =chisqmatrix_stat(light_clear_cs)
light_clear_cs_mat_stat <-format( data.frame(light_clear_cs_mat_stat)["target_light_clear"], scientific = F)
light_clear_cs_mat_pval =chisqmatrix_pval(light_clear_cs)
light_clear_cs_mat_pval <-format( data.frame(light_clear_cs_mat_pval)["target_light_clear"], scientific = F)
light_clear_cs_df <-data.frame(c(light_clear_cs_mat_stat, light_clear_cs_mat_pval))
rownames(light_clear_cs_df) <-rownames(light_clear_cs_mat_stat)
colnames(light_clear_cs_df) <-c("Statistic","P-value")
#write_xlsx(data.frame(light_clear_cs_df), 'light_clear_cs.xlsx')
light_clear_cs_df[2]
## P-value
## largest_fed_districts 0.5967922982
## macro_okved_code_group 0.0205476303
## spark_web_site 0.0004345725
## spark_stock_ticket 1.0000000000
## category_by_size_missing 0.0719540353
## category_by_size_melse 0.0175229116
## category_by_size_2_cat 0.2189897300
## administrative_position 0.2052253478
## administrative_connections 0.9941809904
## in_political_party 0.7465046958
## in_association_or_sro 0.0153927012
## case_publications 0.1401052728
## criminal_prosecution 0.6956983834
## capture 0.0035594639
## corruption 1.0000000000
## barriers 0.4254418022
## have_court_case 0.6749204382
## is_guilty 1.0000000000
## reviewed_by_bac 0.6323852679
## max_bac_stage 0.6140938849
## supported_by_bac_public_council 0.1167636343
## reaction_not_passed_by_applicant 0.5418697051
## reaction_consultation 0.4917820492
## reaction_target_letters_control 0.7383852546
## to_ombudsman 0.2213205774
## reaction_not_passed_by_bac 1.0000000000
## auth_capital_group 0.8882452180
## cop_stage 0.2112345131
## target_light_clear NA
#check - looks that code worked ok
chisq.test(xtabs(~dataset$target_light_clear +dataset$macro_okved_code_group))
##
## Pearson's Chi-squared test
##
## data: xtabs(~dataset$target_light_clear + dataset$macro_okved_code_group)
## X-squared = 18.092, df = 8, p-value = 0.02055
tst =chisq.test(xtabs(~dataset$target_light_clear +dataset$is_guilty))
tst$observed
## dataset$is_guilty
## dataset$target_light_clear 0 1
## 0 114 70
## 1 110 66
tst$expected
## dataset$is_guilty
## dataset$target_light_clear 0 1
## 0 114.4889 69.51111
## 1 109.5111 66.48889
It is time to check for categorical data with several levels.
light_clear_dummies <-light_clear_cs[c(#"federal_districts",
"largest_fed_districts", "macro_okved_code_group", "max_bac_stage", "cop_stage", "category_by_size_missing", "category_by_size_melse", "category_by_size_2_cat" )]
light_clear_dummies$max_bac_stage <-as.factor(light_clear_dummies$max_bac_stage)
dums <-dummyVars(" ~ .", data = light_clear_dummies)
light_clear_dums <-data.frame(predict(dums, newdata = light_clear_dummies))
light_clear_dums$target_light_clear <-light_clear_cs$target_light_clear
light_clear_dums_pval =chisqmatrix_pval(light_clear_dums)
light_clear_dums_pval <-format( data.frame(light_clear_dums_pval)["target_light_clear"], scientific = F)
light_clear_dums_stat =chisqmatrix_stat(light_clear_dums)
light_clear_dums_stat <-format( data.frame(light_clear_dums_stat)["target_light_clear"], scientific = F)
light_clear_dums_df <-data.frame(c(light_clear_dums_stat, light_clear_dums_pval))
rownames(light_clear_dums_df) <-rownames(light_clear_dums_stat)
colnames(light_clear_dums_df) <-c("Statistic","P-value")
#write_xlsx(data.frame(light_clear_dums_df), 'light_clear_cs_dums.xlsx')
light_clear_dums_df[2]
## P-value
## largest_fed_districtsCentral 1.00000000
## largest_fed_districtsFar_Siberia 1.00000000
## largest_fed_districtsMoscow 0.57377098
## largest_fed_districtsMoscow_region 0.16044629
## largest_fed_districtsNorth_West 0.22704050
## largest_fed_districtsSouth 0.74947410
## largest_fed_districtsUrals 0.55454707
## largest_fed_districtsVolga 1.00000000
## macro_okved_code_groupBuilding 0.14137979
## macro_okved_code_groupFinancial_insurance 0.07398948
## macro_okved_code_groupmanufacturing 0.51397087
## macro_okved_code_groupother_categories 0.36000192
## macro_okved_code_groupreal_estate 0.04457033
## macro_okved_code_grouprural 0.73488154
## macro_okved_code_groupScience 0.03005041
## macro_okved_code_groupTrading 0.45092390
## macro_okved_code_groupTransportation 0.86905732
## max_bac_stage.0 0.67359688
## max_bac_stage.1 0.74948683
## max_bac_stage.2 0.90441209
## max_bac_stage.3 0.73956188
## max_bac_stage.4 0.05609622
## max_bac_stage.5 0.57901134
## max_bac_stage.6 0.46403398
## cop_stageCouncil_discussion 0.27831740
## cop_stageInformation_collection 0.45373043
## cop_stageResolution 0.09740124
## category_by_size_missingBig 0.84373157
## category_by_size_missingMedium 0.18403864
## category_by_size_missingMicro 0.01752291
## category_by_size_missingSmall 0.11067617
## category_by_size_melseElse 0.01752291
## category_by_size_melseMicro 0.01752291
## category_by_size_2_catBig 0.21898973
## category_by_size_2_catSmall 0.21898973
## target_light_clear NA
# check if code worked ok
# check high p_values whether ther are erros in code or not
# looks like ok the values are pretty close to expected
xtabs(~light_clear_dums$macro_okved_code_groupScience +light_clear_dums$target_light_clear)
## light_clear_dums$target_light_clear
## light_clear_dums$macro_okved_code_groupScience 0 1
## 0 174 154
## 1 10 22
tst =chisq.test(xtabs(~light_clear_dums$macro_okved_code_groupScience +light_clear_dums$target_light_clear))
tst$observed
## light_clear_dums$target_light_clear
## light_clear_dums$macro_okved_code_groupScience 0 1
## 0 174 154
## 1 10 22
tst$expected
## light_clear_dums$target_light_clear
## light_clear_dums$macro_okved_code_groupScience 0 1
## 0 167.64444 160.35556
## 1 16.35556 15.64444
xtabs(~light_clear_dums$largest_fed_districtsMoscow +light_clear_dums$target_light_clear)
## light_clear_dums$target_light_clear
## light_clear_dums$largest_fed_districtsMoscow 0 1
## 0 143 142
## 1 41 34
tst =chisq.test(xtabs(~light_clear_dums$largest_fed_districtsMoscow +light_clear_dums$target_light_clear))
tst$observed
## light_clear_dums$target_light_clear
## light_clear_dums$largest_fed_districtsMoscow 0 1
## 0 143 142
## 1 41 34
tst$expected
## light_clear_dums$target_light_clear
## light_clear_dums$largest_fed_districtsMoscow 0 1
## 0 145.66667 139.33333
## 1 38.33333 36.66667
Target: target_light_extended
# check for distributions
light_extended_vars <-c(
#"federal_districts",
"largest_fed_districts",
#"macro_okved_code",
"macro_okved_code_group",
"spark_web_site",
"spark_stock_ticket",
"category_by_size_missing",
"category_by_size_melse",
"category_by_size_2_cat",
"administrative_position",
"administrative_connections",
"in_political_party",
"in_association_or_sro",
"case_publications",
"criminal_prosecution",
"capture", "corruption", "barriers",
"have_court_case", "is_guilty", "reviewed_by_bac",
"max_bac_stage", "supported_by_bac_public_council",
"reaction_not_passed_by_applicant", "reaction_consultation",
"reaction_target_letters_control", "to_ombudsman",
"reaction_not_passed_by_bac",
"auth_capital_group",
"cop_stage",
"target_light_extended")
light_extended_cs <-dataset[light_extended_vars]
# exclude missing data of target variables
light_extended_cs <-light_extended_cs[!is.na(light_extended_cs$target_light_extended),]
# this is a very log output, it just returns xtabs for each variable and a target
#for (i in 1:length(is_working_vars)){
# print(xtabs(~light_extended_cs$target_light_extended + light_extended_cs[,i]))
#}
# variables to worry about: federal_districts, macro_okved_code, reaction_not_passed_by_bac
light_extended_vars <-c(
# "federal_districts",
"largest_fed_districts",
# "macro_okved_code",
"macro_okved_code_group",
"spark_web_site",
"spark_stock_ticket",
"category_by_size_missing",
"category_by_size_melse",
"category_by_size_2_cat",
"administrative_position",
"administrative_connections",
"in_political_party",
"in_association_or_sro",
"case_publications",
"criminal_prosecution",
"capture", "corruption", "barriers",
"have_court_case", "is_guilty", "reviewed_by_bac",
"max_bac_stage", "supported_by_bac_public_council",
"reaction_not_passed_by_applicant", "reaction_consultation",
"reaction_target_letters_control", "to_ombudsman",
"reaction_not_passed_by_bac",
"auth_capital_group",
"cop_stage",
"target_light_extended")
light_extended_cs <-dataset[light_extended_vars]
# exclude missing data of target variables
light_extended_cs <-light_extended_cs[!is.na(light_extended_cs$target_light_extended),]
light_extended_cs_mat_stat =chisqmatrix_stat(light_extended_cs)
light_extended_cs_mat_stat <-format( data.frame(light_extended_cs_mat_stat)["target_light_extended"], scientific = F)
light_extended_cs_mat_pval =chisqmatrix_pval(light_extended_cs)
light_extended_cs_mat_pval <-format( data.frame(light_extended_cs_mat_pval)["target_light_extended"], scientific = F)
light_extended_cs_df <-data.frame(c(light_extended_cs_mat_stat, light_extended_cs_mat_pval))
rownames(light_extended_cs_df) <-rownames(light_extended_cs_mat_stat)
colnames(light_extended_cs_df) <-c("Statistic","P-value")
#write_xlsx(data.frame(light_extended_cs_df), 'light_extended_cs.xlsx')
light_extended_cs_df[2]
## P-value
## largest_fed_districts 0.40839317261
## macro_okved_code_group 0.00604937767
## spark_web_site 0.00002727188
## spark_stock_ticket 0.83651266939
## category_by_size_missing 0.01322400277
## category_by_size_melse 0.00274021416
## category_by_size_2_cat 0.21242355323
## administrative_position 0.34944717127
## administrative_connections 0.91675337150
## in_political_party 0.62304212321
## in_association_or_sro 0.00816162576
## case_publications 0.01112536082
## criminal_prosecution 0.67704214429
## capture 0.00381863178
## corruption 1.00000000000
## barriers 0.36766267753
## have_court_case 0.42885944656
## is_guilty 1.00000000000
## reviewed_by_bac 0.81884704855
## max_bac_stage 0.71531027537
## supported_by_bac_public_council 0.23763136934
## reaction_not_passed_by_applicant 0.53254210174
## reaction_consultation 0.68440521975
## reaction_target_letters_control 0.91096615924
## to_ombudsman 0.22184866378
## reaction_not_passed_by_bac 1.00000000000
## auth_capital_group 0.92089894328
## cop_stage 0.25867496221
## target_light_extended NA
#check - looks that code worked ok
chisq.test(xtabs(~dataset$target_light_extended +dataset$macro_okved_code_group))
##
## Pearson's Chi-squared test
##
## data: xtabs(~dataset$target_light_extended + dataset$macro_okved_code_group)
## X-squared = 21.447, df = 8, p-value = 0.006049
tst =chisq.test(xtabs(~dataset$target_light_extended +dataset$is_guilty))
tst$observed
## dataset$is_guilty
## dataset$target_light_extended 0 1
## 0 114 70
## 1 128 80
tst$expected
## dataset$is_guilty
## dataset$target_light_extended 0 1
## 0 113.5918 70.40816
## 1 128.4082 79.59184
It is time to check for categorical data with several levels.
light_extended_dummies <-light_extended_cs[c(
#"federal_districts",
"largest_fed_districts",
#"macro_okved_code",
"macro_okved_code_group", "max_bac_stage", "cop_stage", "category_by_size_missing", "category_by_size_melse", "category_by_size_2_cat" )]
light_extended_dummies$max_bac_stage <-as.factor(light_extended_dummies$max_bac_stage)
dums <-dummyVars(" ~ .", data = light_extended_dummies)
light_extended_dums <-data.frame(predict(dums, newdata = light_extended_dummies))
light_extended_dums$target_light_extended <-light_extended_cs$target_light_extended
light_extended_dums_pval =chisqmatrix_pval(light_extended_dums)
light_extended_dums_pval <-format( data.frame(light_extended_dums_pval)["target_light_extended"], scientific = F)
light_extended_dums_stat =chisqmatrix_stat(light_extended_dums)
light_extended_dums_stat <-format( data.frame(light_extended_dums_stat)["target_light_extended"], scientific = F)
light_extended_dums_df <-data.frame(c(light_extended_dums_stat, light_extended_dums_pval))
rownames(light_extended_dums_df) <-rownames(light_extended_dums_stat)
colnames(light_extended_dums_df) <-c("Statistic","P-value")
#write_xlsx(data.frame(light_extended_dums_df), 'light_extended_dums.xlsx')
light_extended_dums_df[2]
## P-value
## largest_fed_districtsCentral 0.978140031
## largest_fed_districtsFar_Siberia 0.992589979
## largest_fed_districtsMoscow 0.458983283
## largest_fed_districtsMoscow_region 0.095345164
## largest_fed_districtsNorth_West 0.198391788
## largest_fed_districtsSouth 0.768685128
## largest_fed_districtsUrals 0.385254299
## largest_fed_districtsVolga 1.000000000
## macro_okved_code_groupBuilding 0.135740588
## macro_okved_code_groupFinancial_insurance 0.030840554
## macro_okved_code_groupmanufacturing 0.672489344
## macro_okved_code_groupother_categories 0.605533043
## macro_okved_code_groupreal_estate 0.017445468
## macro_okved_code_grouprural 0.541873299
## macro_okved_code_groupScience 0.024955324
## macro_okved_code_groupTrading 0.404307936
## macro_okved_code_groupTransportation 0.612668697
## max_bac_stage.0 0.624449893
## max_bac_stage.1 0.574128222
## max_bac_stage.2 0.965381635
## max_bac_stage.3 0.872470089
## max_bac_stage.4 0.089875421
## max_bac_stage.5 0.716274639
## max_bac_stage.6 0.569288459
## cop_stageCouncil_discussion 0.407972694
## cop_stageInformation_collection 0.375015378
## cop_stageResolution 0.126521044
## category_by_size_missingBig 0.857503102
## category_by_size_missingMedium 0.169497797
## category_by_size_missingMicro 0.002740214
## category_by_size_missingSmall 0.021530300
## category_by_size_melseElse 0.002740214
## category_by_size_melseMicro 0.002740214
## category_by_size_2_catBig 0.212423553
## category_by_size_2_catSmall 0.212423553
## target_light_extended NA
# check if code worked ok
# check high p_values whether ther are erros in code or not
# looks like ok the values are pretty close to expected
xtabs(~light_extended_dums$macro_okved_code_groupScience +light_extended_dums$target_light_extended)
## light_extended_dums$target_light_extended
## light_extended_dums$macro_okved_code_groupScience 0 1
## 0 174 182
## 1 10 26
tst =chisq.test(xtabs(~light_extended_dums$macro_okved_code_groupScience +light_extended_dums$target_light_extended))
tst$observed
## light_extended_dums$target_light_extended
## light_extended_dums$macro_okved_code_groupScience 0 1
## 0 174 182
## 1 10 26
tst$expected
## light_extended_dums$target_light_extended
## light_extended_dums$macro_okved_code_groupScience 0 1
## 0 167.10204 188.89796
## 1 16.89796 19.10204
xtabs(~light_extended_dums$largest_fed_districtsMoscow +light_extended_dums$target_light_extended)
## light_extended_dums$target_light_extended
## light_extended_dums$largest_fed_districtsMoscow 0 1
## 0 143 169
## 1 41 39
tst =chisq.test(xtabs(~light_extended_dums$largest_fed_districtsMoscow +light_extended_dums$target_light_extended))
tst$observed
## light_extended_dums$target_light_extended
## light_extended_dums$largest_fed_districtsMoscow 0 1
## 0 143 169
## 1 41 39
tst$expected
## light_extended_dums$target_light_extended
## light_extended_dums$largest_fed_districtsMoscow 0 1
## 0 146.44898 165.55102
## 1 37.55102 42.44898
Target: target_strong_extended
strong_extended_vars <-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",
"max_bac_stage", "supported_by_bac_public_council",
"reaction_not_passed_by_applicant", "reaction_consultation",
"reaction_target_letters_control", "to_ombudsman",
"reaction_not_passed_by_bac",
"auth_capital_group",
"cop_stage",
"target_strong_extended")
strong_extended_cs <-dataset[strong_extended_vars]
# exclude missing data of target variables
strong_extended_cs <-strong_extended_cs[!is.na(strong_extended_cs$target_strong_extended),]
# this is a very log output, it just returns xtabs for each variable and a target
# for (i in 1:length(strong_extended_vars)){
# print(xtabs(~strong_extended_cs$target_strong_extended + strong_extended_cs[,i]))
# }
# variables to worry about: macro_okved_code_group (some low categories), auth_capital_group
strong_extended_vars <-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",
"max_bac_stage", "supported_by_bac_public_council",
"reaction_not_passed_by_applicant", "reaction_consultation",
"reaction_target_letters_control", "to_ombudsman",
"reaction_not_passed_by_bac",
"auth_capital_group",
"cop_stage",
"target_strong_extended")
strong_extended_cs <-dataset[strong_extended_vars]
# exclude missing data of target variables
strong_extended_cs <-strong_extended_cs[!is.na(strong_extended_cs$target_strong_extended),]
strong_extended_cs_mat_stat =chisqmatrix_stat(strong_extended_cs)
strong_extended_cs_mat_stat <-format( data.frame(strong_extended_cs_mat_stat)["target_strong_extended"], scientific = F)
target_strong_cs_mat_pval =chisqmatrix_pval(strong_extended_cs)
target_strong_cs_mat_pval <-format( data.frame(target_strong_cs_mat_pval)["target_strong_extended"], scientific = F)
strong_extended_cs_df <-data.frame(c(strong_extended_cs_mat_stat, target_strong_cs_mat_pval))
rownames(strong_extended_cs_df) <-rownames(strong_extended_cs_mat_stat)
colnames(strong_extended_cs_df) <-c("Statistic","P-value")
# write_xlsx(data.frame(strong_extended_cs_df), 'strong_extended_cs.xlsx')
strong_extended_cs_df[2]
## P-value
## largest_fed_districts 0.2150360448
## macro_okved_code_group 0.0079194198
## spark_web_site 0.0003237673
## spark_stock_ticket 1.0000000000
## category_by_size_missing 0.1438133617
## category_by_size_melse 0.2263052628
## category_by_size_2_cat 0.0309375079
## administrative_position 0.6204024134
## administrative_connections 0.8772617872
## in_political_party 0.7271179854
## in_association_or_sro 0.0178374213
## case_publications 0.0440158847
## criminal_prosecution 0.2841037763
## capture 0.0561751806
## corruption 1.0000000000
## barriers 0.0345784864
## have_court_case 1.0000000000
## is_guilty 0.9369415164
## reviewed_by_bac 1.0000000000
## max_bac_stage 0.6420572726
## supported_by_bac_public_council 1.0000000000
## reaction_not_passed_by_applicant 0.2078206642
## reaction_consultation 0.0328155060
## reaction_target_letters_control 0.5880917327
## to_ombudsman 0.9275712728
## reaction_not_passed_by_bac 1.0000000000
## auth_capital_group 0.5121089485
## cop_stage 0.9825437801
## target_strong_extended NA
Looks like a problems with distribution here. So while analysing - stick to the dummy variables.
#check - looks that code worked ok
chisq.test(xtabs(~dataset$target_strong_extended +dataset$macro_okved_code_group))
## Warning in chisq.test(xtabs(~dataset$target_strong_extended +
## dataset$macro_okved_code_group)): Chi-squared approximation may be
## incorrect
##
## Pearson's Chi-squared test
##
## data: xtabs(~dataset$target_strong_extended + dataset$macro_okved_code_group)
## X-squared = 20.723, df = 8, p-value = 0.007919
tst =chisq.test(xtabs(~dataset$target_strong_extended +dataset$is_guilty))
tst$observed
## dataset$is_guilty
## dataset$target_strong_extended 0 1
## 0 185 116
## 1 57 34
tst$expected
## dataset$is_guilty
## dataset$target_strong_extended 0 1
## 0 185.82143 115.17857
## 1 56.17857 34.82143
It is time to check for categorical data with several levels.
strong_extended_dummies <-strong_extended_cs[c(
#"federal_districts",
"largest_fed_districts",
#"macro_okved_code",
"macro_okved_code_group", "max_bac_stage", "cop_stage", "category_by_size_missing", "category_by_size_melse", "category_by_size_2_cat" )]
strong_extended_dummies$max_bac_stage <-as.factor(strong_extended_dummies$max_bac_stage)
dums <-dummyVars(" ~ .", data = strong_extended_dummies)
strong_extended_dums <-data.frame(predict(dums, newdata = strong_extended_dummies))
strong_extended_dums$target_strong_extended <-strong_extended_cs$target_strong_extended
strong_extended_dums_pval =chisqmatrix_pval(strong_extended_dums)
strong_extended_dums_pval <-format( data.frame(strong_extended_dums_pval)["target_strong_extended"], scientific = F)
strong_extended_dums_stat =chisqmatrix_stat(strong_extended_dums)
strong_extended_dums_stat <-format( data.frame(strong_extended_dums_stat)["target_strong_extended"], scientific = F)
strong_extended_dums_df <-data.frame(c(strong_extended_dums_stat, strong_extended_dums_pval))
rownames(strong_extended_dums_df) <-rownames(strong_extended_dums_stat)
colnames(strong_extended_dums_df) <-c("Statistic","P-value")
#write_xlsx(data.frame(strong_extended_dums_df), 'strong_extended_cs_dums.xlsx')
strong_extended_dums_df[2]
## P-value
## largest_fed_districtsCentral 0.879857641
## largest_fed_districtsFar_Siberia 0.449858657
## largest_fed_districtsMoscow 0.567016227
## largest_fed_districtsMoscow_region 0.013451367
## largest_fed_districtsNorth_West 0.684956709
## largest_fed_districtsSouth 0.449858657
## largest_fed_districtsUrals 0.938092359
## largest_fed_districtsVolga 0.700121434
## macro_okved_code_groupBuilding 0.470209700
## macro_okved_code_groupFinancial_insurance 0.611935138
## macro_okved_code_groupmanufacturing 0.240605743
## macro_okved_code_groupother_categories 0.231483398
## macro_okved_code_groupreal_estate 0.109560809
## macro_okved_code_grouprural 0.842081008
## macro_okved_code_groupScience 0.003088035
## macro_okved_code_groupTrading 0.216097020
## macro_okved_code_groupTransportation 0.180646635
## max_bac_stage.0 0.259830627
## max_bac_stage.1 0.399437873
## max_bac_stage.2 1.000000000
## max_bac_stage.3 0.739848567
## max_bac_stage.4 0.797639824
## max_bac_stage.5 0.752324424
## max_bac_stage.6 0.707455436
## cop_stageCouncil_discussion 0.958172266
## cop_stageInformation_collection 1.000000000
## cop_stageResolution 1.000000000
## category_by_size_missingBig 0.180551008
## category_by_size_missingMedium 0.176442012
## category_by_size_missingMicro 0.226305263
## category_by_size_missingSmall 0.682311757
## category_by_size_melseElse 0.226305263
## category_by_size_melseMicro 0.226305263
## category_by_size_2_catBig 0.030937508
## category_by_size_2_catSmall 0.030937508
## target_strong_extended NA
# check if code worked ok
# check high p_values whether ther are erros in code or not
# looks like ok the values are pretty close to expected
xtabs(~strong_extended_dums$macro_okved_code_groupScience +strong_extended_dums$target_strong_extended)
## strong_extended_dums$target_strong_extended
## strong_extended_dums$macro_okved_code_groupScience 0 1
## 0 281 75
## 1 20 16
tst =chisq.test(xtabs(~strong_extended_dums$macro_okved_code_groupScience +strong_extended_dums$target_strong_extended))
tst$observed
## strong_extended_dums$target_strong_extended
## strong_extended_dums$macro_okved_code_groupScience 0 1
## 0 281 75
## 1 20 16
tst$expected
## strong_extended_dums$target_strong_extended
## strong_extended_dums$macro_okved_code_groupScience 0 1
## 0 273.35714 82.642857
## 1 27.64286 8.357143
xtabs(~strong_extended_dums$macro_okved_code_groupTransportation +strong_extended_dums$target_strong_extended)
## strong_extended_dums$target_strong_extended
## strong_extended_dums$macro_okved_code_groupTransportation 0 1
## 0 286 90
## 1 15 1
tst =chisq.test(xtabs(~strong_extended_dums$macro_okved_code_groupTransportation +strong_extended_dums$target_strong_extended))
## Warning in
## chisq.test(xtabs(~strong_extended_dums$macro_okved_code_groupTransportation
## + : Chi-squared approximation may be incorrect
tst$observed
## strong_extended_dums$target_strong_extended
## strong_extended_dums$macro_okved_code_groupTransportation 0 1
## 0 286 90
## 1 15 1
tst$expected
## strong_extended_dums$target_strong_extended
## strong_extended_dums$macro_okved_code_groupTransportation 0
## 0 288.71429
## 1 12.28571
## strong_extended_dums$target_strong_extended
## strong_extended_dums$macro_okved_code_groupTransportation 1
## 0 87.285714
## 1 3.714286
xtabs(~strong_extended_dums$largest_fed_districtsMoscow +strong_extended_dums$target_strong_extended)
## strong_extended_dums$target_strong_extended
## strong_extended_dums$largest_fed_districtsMoscow 0 1
## 0 242 70
## 1 59 21
tst =chisq.test(xtabs(~strong_extended_dums$largest_fed_districtsMoscow +strong_extended_dums$target_strong_extended))
tst$observed
## strong_extended_dums$target_strong_extended
## strong_extended_dums$largest_fed_districtsMoscow 0 1
## 0 242 70
## 1 59 21
tst$expected
## strong_extended_dums$target_strong_extended
## strong_extended_dums$largest_fed_districtsMoscow 0 1
## 0 239.57143 72.42857
## 1 61.42857 18.57143
Part 3. Modelling
TARGET 2 - TARGET LIGHT CLEAR Baseline
light_clear_vars <-c(
c(
"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_clear")
)
light_clear_data <-dataset[light_clear_vars]
light_clear_data <-light_clear_data[!is.na(light_clear_data$target_light_clear),]
light_clear_data <-light_clear_data[!is.na(light_clear_data$category_by_size_missing),]
light_clear_data$macro_okved_code_group <-factor(light_clear_data$macro_okved_code_group)
#light_clear_data$largest_fed_districts <- factor(light_clear_data$largest_fed_districts)
light_clear_data$category_by_size_missing <-factor(light_clear_data$category_by_size_missing)
light_clear_data$cop_stage <-factor(light_clear_data$cop_stage)
logit_1<-glm(target_light_clear~., family = binomial,data = light_clear_data)
summary(logit_1)
##
## Call:
## glm(formula = target_light_clear ~ ., family = binomial, data = light_clear_data)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.9222 -1.0905 0.4571 1.0470 2.0104
##
## Coefficients:
## Estimate Std. Error z value
## (Intercept) 1.24167 0.83073 1.495
## macro_okved_code_groupFinancial_insurance -0.48552 0.70260 -0.691
## macro_okved_code_groupmanufacturing 0.42557 0.46075 0.924
## macro_okved_code_groupother_categories 0.71754 0.46382 1.547
## macro_okved_code_groupreal_estate 1.67006 0.55638 3.002
## macro_okved_code_grouprural 0.84125 0.62102 1.355
## macro_okved_code_groupScience 1.51626 0.53473 2.836
## macro_okved_code_groupTrading 0.45803 0.43992 1.041
## macro_okved_code_groupTransportation 0.31485 0.64753 0.486
## spark_stock_ticket -1.24550 0.79960 -1.558
## category_by_size_missingMedium 0.20615 0.68941 0.299
## category_by_size_missingMicro -0.88162 0.52919 -1.666
## category_by_size_missingSmall -0.13956 0.57448 -0.243
## administrative_position 1.11948 0.54230 2.064
## administrative_connections -0.46918 0.32053 -1.464
## in_political_party -0.31557 0.46877 -0.673
## in_association_or_sro 0.61678 0.28510 2.163
## case_publications -0.71444 0.39023 -1.831
## criminal_prosecution -0.18693 0.44130 -0.424
## capture -0.90643 0.36114 -2.510
## corruption 0.18578 0.50033 0.371
## barriers -0.20690 0.55759 -0.371
## have_court_case 0.38369 0.37680 1.018
## is_guilty -0.18523 0.38364 -0.483
## cop_stageInformation_collection -0.09114 0.39690 -0.230
## cop_stageResolution -0.85645 0.32819 -2.610
## to_ombudsman 0.66012 0.41640 1.585
## Pr(>|z|)
## (Intercept) 0.13500
## macro_okved_code_groupFinancial_insurance 0.48955
## macro_okved_code_groupmanufacturing 0.35566
## macro_okved_code_groupother_categories 0.12186
## macro_okved_code_groupreal_estate 0.00269 **
## macro_okved_code_grouprural 0.17554
## macro_okved_code_groupScience 0.00457 **
## macro_okved_code_groupTrading 0.29780
## macro_okved_code_groupTransportation 0.62681
## spark_stock_ticket 0.11931
## category_by_size_missingMedium 0.76492
## category_by_size_missingMicro 0.09572 .
## category_by_size_missingSmall 0.80805
## administrative_position 0.03899 *
## administrative_connections 0.14326
## in_political_party 0.50082
## in_association_or_sro 0.03051 *
## case_publications 0.06713 .
## criminal_prosecution 0.67187
## capture 0.01208 *
## corruption 0.71040
## barriers 0.71060
## have_court_case 0.30854
## is_guilty 0.62923
## cop_stageInformation_collection 0.81838
## cop_stageResolution 0.00907 **
## to_ombudsman 0.11290
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 448.85 on 323 degrees of freedom
## Residual deviance: 390.96 on 297 degrees of freedom
## AIC: 444.96
##
## Number of Fisher Scoring iterations: 4
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