library(tidyverse)
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library(ltm)
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library(dplyr)
library(stats)
library(fastDummies)
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## Thank you for using fastDummies!
## To acknowledge our work, please cite the package:
## Kaplan, J. & Schlegel, B. (2023). fastDummies: Fast Creation of Dummy (Binary) Columns and Rows from Categorical Variables. Version 1.7.1. URL: https://github.com/jacobkap/fastDummies, https://jacobkap.github.io/fastDummies/.
library(knitr)
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library(data.table)
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library(formattable)
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library(DT)
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df <- read.csv2("basak_sayisal_veriler_c.csv")
df <- df[-c(43),]
glimpse(df)
## Rows: 121
## Columns: 148
## $ X <int> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, …
## $ KOD <int> 75, 31, 54, 3, 70, 104, 87, 7, 37, 82, 81, …
## $ zaman <chr> "2-16-2024 23:24:14", "2.05.2024 14:40", "2…
## $ kisi_cinsiyet <chr> "Kadin", "Kadin", "Kadin", "Kadin", "Erkek"…
## $ isletme_kisi_egitim <chr> "Lisansustu", "Lisans", "Lisans", "Lisans",…
## $ kisi_bolum_poz <chr> "ust yonetim", "Kalite Kontrol-Planlama", "…
## $ kisi_kac_yildir <chr> "10 yildan fazla", "1 yildan 2", "10 yildan…
## $ isletme_sektor <chr> "uretim", "Makine-Metal", "Gıda ", "Makin…
## $ isletme_kume_1 <chr> NA, NA, NA, NA, NA, NA, NA, "Havacılık", NA…
## $ isletme_kume_2 <chr> NA, NA, NA, NA, "Havacılık", NA, NA, "Havac…
## $ isletme_isim <chr> "-", "3 Eksen Makina", "Ajinomoto istanbul …
## $ isletme_yabanciOrtak <chr> "Hayir", "Hayir", "Evet", "Evet", "Hayir", …
## $ isletme_yas <chr> "30 yildan fazla", "11 - 30 yil", "30 yilda…
## $ isletme_olcek <chr> "Buyuk", "Orta", "Buyuk", "Orta", "Orta", "…
## $ isletme_cal_say <chr> "250 kisi ve uzeri", "50 - 249 kisi", "50 -…
## $ far_sur_kaynak <int> 5, 4, 5, 5, 4, 4, 4, 5, 4, 3, 5, 5, 5, 5, 3…
## $ far_sur_gelecek <int> 5, 2, 5, 4, 4, 3, 3, 4, 4, 4, 5, 5, 5, 5, 3…
## $ far_sur_adil_is <int> 5, 3, 5, 5, 5, 4, 5, 4, 5, 4, 5, 5, 5, 5, 5…
## $ far_sur_toplum <int> 4, 2, 5, 5, 4, 4, 5, 4, 5, 3, 5, 5, 4, 5, 4…
## $ far_sur_cevre_koruma <int> 4, 3, 5, 4, 4, 3, 5, 4, 5, 4, 5, 5, 5, 5, 5…
## $ far_sur_paydas <int> 4, 3, 5, 5, 5, 3, 4, 5, 4, 3, 5, 5, 5, 5, 4…
## $ far_sur_eko_performans <int> 4, 5, 5, 5, 4, 4, 5, 5, 4, 3, 5, 5, 5, 5, 4…
## $ far_sur_calisan_hak <int> 5, 2, 5, 4, 5, 4, 3, 5, 3, 2, 5, 5, 5, 5, 5…
## $ far_sur_tarim <int> 1, 1, 5, 3, 3, 1, 4, 3, 5, 4, 4, 5, 5, 3, 4…
## $ far_cev_karbon <int> 5, 1, 3, 3, 4, 3, 4, 5, 3, 4, 5, 3, 4, 4, 3…
## $ far_cev_atik <int> 5, 3, 5, 3, 5, 3, 5, 5, 3, 4, 5, 4, 3, 5, 3…
## $ far_cev_enerji <int> 4, 3, 5, 5, 4, 3, 5, 4, 4, 4, 5, 3, 3, 4, 4…
## $ far_cev_su <int> 3, 2, 5, 3, 3, 3, 5, 4, 4, 4, 5, 3, 3, 4, 3…
## $ far_cev_iklim <int> 4, 1, 4, 2, 3, 3, 3, 4, 2, 3, 5, 3, 4, 4, 3…
## $ far_sos_egitim <int> 3, 2, 5, 2, 4, 2, 5, 4, 4, 2, 4, 4, 4, 5, 3…
## $ far_sos_cinsiyet <int> 3, 3, 5, 2, 3, 2, 3, 5, 3, 2, 4, 4, 4, 4, 5…
## $ far_sos_is_sagligi <int> 5, 3, 5, 4, 4, 3, 5, 5, 4, 4, 4, 5, 5, 5, 5…
## $ far_sos_tedarikci <int> 3, 1, 5, 2, 4, 1, 5, 4, 4, 3, 3, 5, 5, 3, 3…
## $ far_sos_sorumluluk <int> 4, 1, 5, 2, 3, 2, 5, 4, 3, 3, 5, 5, 5, 4, 3…
## $ far_sos_calisan <int> 4, 1, 5, 4, 4, 3, 5, 4, 3, 2, 5, 5, 5, 5, 5…
## $ far_sos_musteri <int> 5, 5, 5, 4, 5, 4, 5, 5, 4, 4, 5, 5, 5, 5, 5…
## $ far_sos_sosyal_hak <int> 3, 4, 5, 4, 4, 3, 3, 4, 4, 4, 3, 5, 4, 4, 5…
## $ far_sos_yetenek <int> 4, 4, 5, 4, 4, 4, 5, 4, 4, 2, 4, 5, 4, 4, 4…
## $ far_sos_istihdam <int> 4, 2, 5, 4, 5, 4, 5, 5, 5, 3, 5, 4, 4, 4, 3…
## $ far_sos_urun_guvenlik <int> 3, 2, 5, 4, 5, 5, 5, 5, 4, 3, 5, 5, 5, 4, 4…
## $ far_sos_inovasyon <int> 2, 2, 5, 4, 4, 5, 5, 5, 4, 3, 5, 5, 5, 3, 3…
## $ far_sos_motivasyon <int> 4, 3, 5, 4, 3, 3, 4, 4, 4, 2, 5, 5, 5, 4, 5…
## $ far_sos_kirilgan <int> 3, 2, 5, 3, 3, 2, 4, 3, 2, 2, 5, 5, 4, 3, 4…
## $ far_yon_belge <int> 5, 3, 5, 4, 5, 5, 4, 5, 4, 4, 4, 4, 5, 3, 4…
## $ far_yon_kultur <int> 3, 2, 5, 4, 4, 4, 5, 4, 4, 3, 4, 4, 4, 4, 4…
## $ far_yon_mevzuat <int> 5, 4, 5, 5, 5, 4, 5, 5, 4, 4, 4, 5, 5, 4, 5…
## $ far_yon_cevre_politika <int> 5, 3, 5, 3, 4, 3, 5, 5, 4, 4, 4, 5, 5, 5, 4…
## $ far_yon_sur_hedef <int> 3, 1, 5, 4, 4, 4, 5, 5, 5, 3, 5, 5, 5, 4, 4…
## $ far_eko_verimlilik <int> 5, 4, 5, 4, 4, 3, 4, 5, 3, 3, 4, 5, 5, 4, 4…
## $ far_eko_satin_alma <int> 3, 2, 5, 3, 3, 4, 3, 4, 3, 3, 4, 5, 5, 3, 3…
## $ far_eko_teknoloji <int> 4, 1, 5, 4, 4, 4, 4, 5, 3, 3, 4, 5, 5, 3, 3…
## $ far_birim_sur <int> 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0…
## $ far_birim_cevre <int> 1, 0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1…
## $ far_birim_isg <int> 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 0, 1, 0, 1, 1…
## $ far_birim_kalite <int> 1, 0, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1…
## $ far_birim_idari <int> 0, 0, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 0, 0, 1…
## $ far_birim_satin_alma <int> 0, 1, 0, 0, 0, 1, 1, 0, 0, 1, 1, 1, 0, 1, 1…
## $ far_birim_finans <int> 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1…
## $ far_birim_pazarlama <int> 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1…
## $ far_birim_muhasebe <int> 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1…
## $ far_birim_ik <int> 1, 0, 1, 1, 0, 0, 1, 1, 0, 1, 0, 1, 0, 1, 1…
## $ far_birim_iletisim <int> 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0…
## $ far_birim_lojistik <int> 0, 0, 1, 0, 0, 0, 1, 1, 1, 0, 1, 0, 0, 0, 1…
## $ far_birim_depolama <int> 0, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 1…
## $ far_itici_yonetim <int> 4, 1, 4, 4, 4, 4, 4, 4, 3, 3, 5, 5, 4, 5, 3…
## $ far_itici_mutakabat <int> 5, 1, 4, 3, 5, 2, 4, 4, 3, 3, 5, 4, 5, 1, 2…
## $ far_itici_oneri <int> 2, 3, 4, 3, 4, 2, 3, 4, 4, 3, 3, 5, 5, 4, 4…
## $ far_itici_duzeltici <int> 2, 4, 5, 3, 4, 3, 5, 4, 3, 3, 3, 5, 5, 4, 4…
## $ far_itici_sertifikasyon <int> 3, 4, 5, 4, 5, 4, 5, 5, 3, 4, 3, 4, 2, 5, 4…
## $ far_itici_ortaklik <int> 4, 3, 4, 4, 5, 2, 4, 5, 2, 4, 5, 1, 3, 1, 3…
## $ far_itici_sur_hedef <int> 3, 2, 5, 3, 4, 3, 4, 5, 3, 3, 4, 4, 5, 3, 2…
## $ far_itici_musteri <int> 5, 5, 4, 4, 4, 4, 2, 5, 2, 4, 5, 5, 4, 5, 4…
## $ far_itici_stk <int> 4, 1, 4, 3, 4, 3, 2, 3, 3, 3, 5, 5, 3, 3, 3…
## $ far_itici_verimlilik <int> 5, 1, 5, 3, 4, 3, 5, 5, 3, 3, 5, 5, 5, 1, 3…
## $ far_itici_mevzuat <int> 4, 3, 4, 4, 5, 4, 5, 4, 2, 4, 5, 5, 4, 5, 3…
## $ far_itici_merak <int> 3, 1, 5, 3, 5, 4, 4, 4, 3, 3, 2, 5, 2, 3, 3…
## $ far_engel_veri <int> 2, 4, 3, 5, 3, 2, 2, 2, 3, 4, 2, 4, 4, 4, 3…
## $ far_engel_maliyet <int> 4, 5, 4, 3, 3, 4, 5, 3, 4, 5, 2, 4, 5, 4, 5…
## $ far_engel_2_personel <int> 5, 5, 3, 5, 5, 5, 3, 3, 3, 3, 2, 4, 4, 5, 4…
## $ far_engel_direnc <int> 5, 5, 2, 3, 3, 4, 5, 2, 3, 5, 3, 5, 3, 4, 4…
## $ far_engel_kar <int> 4, 5, 2, 3, 3, 3, 5, 2, 4, 4, 1, 4, 5, 4, 5…
## $ far_engel_iletisim <int> 4, 5, 1, 4, 2, 3, 2, 1, 3, 5, 1, 5, 3, 3, 4…
## $ far_arac_iso14001 <int> 1, 0, 0, 1, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1…
## $ far_arac_iso45001 <int> 1, 1, 1, 0, 0, 0, 1, 0, 0, 1, 1, 1, 1, 0, 1…
## $ far_arac_iso9001 <int> 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1…
## $ far_arac_iso50001 <int> 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 1…
## $ far_arac_iso27001 <int> 1, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0…
## $ far_arac_ohsas18001 <int> 1, 0, 1, 1, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0…
## $ far_arac_brc <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ far_arac_iatf16949 <int> 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1…
## $ far_arac_global_compact <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ far_arac_diger <int> 1, 0, 1, 0, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0…
## $ far_cev_fay_enerji <int> 3, 3, 4, 5, 5, 2, 3, 5, 4, 3, 5, 5, 5, 4, 5…
## $ far_cev_fay_atik <int> 3, 4, 5, 3, 4, 3, 5, 5, 3, 3, 5, 5, 5, 4, 5…
## $ far_cev_fay_emisyon <int> 3, 4, 4, 3, 3, 2, 2, 4, 3, 3, 4, 5, 5, 4, 3…
## $ far_cev_fay_su <int> 3, 2, 4, 3, 4, 2, 3, 4, 4, 3, 5, 5, 5, 4, 5…
## $ far_sos_fay_nitelikli <int> 4, 2, 5, 4, 3, 5, 5, 4, 4, 3, 4, 5, 2, 4, 5…
## $ far_sos_fay_guvenli <int> 4, 2, 5, 4, 4, 3, 5, 5, 4, 3, 5, 5, 5, 4, 5…
## $ far_sos_fay_toplum_refah <int> 4, 1, 5, 4, 5, 2, 5, 4, 5, 3, 5, 5, 4, 4, 5…
## $ far_sos_fay_esit_calisma <int> 4, 1, 5, 4, 4, 2, 4, 4, 5, 3, 5, 5, 4, 4, 5…
## $ far_sos_fay_rekabet <int> 2, 3, 5, 4, 3, 4, 3, 5, 4, 3, 4, 5, 4, 4, 5…
## $ far_sos_fay_calisan_refah <int> 4, 1, 5, 4, 4, 3, 3, 4, 3, 3, 5, 5, 4, 4, 5…
## $ far_yon_yesil <int> 2, 1, 4, 3, 3, 3, 3, 4, 4, 2, 5, 4, 5, 3, 3…
## $ far_yon_marka <int> 2, 2, 5, 4, 3, 4, 5, 4, 5, 2, 5, 5, 5, 4, 5…
## $ far_eko_maliyet <int> 5, 4, 4, 4, 2, 4, 3, 4, 4, 3, 4, 5, 5, 3, 5…
## $ far_eko_karlilik <int> 5, 5, 4, 4, 2, 5, 3, 4, 4, 3, 4, 5, 4, 3, 5…
## $ far_pay_tedarikci <int> 3, 4, 4, 3, 3, 2, 2, 3, 4, 2, 3, 5, 4, 3, 5…
## $ far_pay_bilinc <int> 3, 4, 5, 3, 4, 3, 3, 4, 4, 2, 5, 5, 5, 4, 5…
## $ far_pay_top_fayda <int> 3, 1, 5, 4, 5, 2, 4, 4, 5, 2, 5, 5, 5, 3, 5…
## $ far_pay_musteri <int> 3, 5, 4, 4, 3, 3, 5, 4, 5, 2, 3, 5, 5, 4, 5…
## $ far_pay_ulke_ekonomi <int> 3, 2, 4, 4, 5, 3, 5, 4, 5, 2, 3, 5, 5, 4, 5…
## $ far_pay_cev_sagligi <int> 4, 1, 4, 3, 4, 2, 5, 4, 4, 2, 4, 5, 5, 5, 5…
## $ far_pay_dongu_ekonomi <int> 3, 2, 4, 4, 4, 4, 4, 4, 4, 2, 4, 5, 5, 4, 5…
## $ far_pay_yatirimci <int> 1, 4, 4, 4, 3, 2, 2, 4, 4, 2, 3, 5, 4, 5, 5…
## $ far_pay_etik <int> 3, 2, 5, 3, 5, 3, 5, 4, 5, 2, 3, 5, 5, 5, 5…
## $ far_pay_engelli <int> 3, 1, 5, 3, 3, 2, 2, 3, 4, 2, 5, 5, 4, 4, 5…
## $ far_pay_insan_haklari <int> 5, 2, 5, 3, 4, 3, 4, 4, 5, 2, 5, 5, 4, 5, 5…
## $ far_pay_diger_isletme <int> 3, 2, 5, 4, 4, 4, 5, 4, 4, 2, 5, 5, 5, 4, 5…
## $ Yon_plan_ust_yonetim <int> 3, 4, 5, 4, 4, 4, 3, 5, 4, 2, 5, 5, 4, 5, 5…
## $ Yon_plan_amac <int> 3, 2, 5, 4, 4, 4, 3, 5, 4, 2, 5, 5, 5, 5, 5…
## $ Yon_plan_stratejik_plan <int> 3, 2, 4, 4, 5, 4, 3, 5, 5, 2, 5, 5, 5, 5, 5…
## $ Yon_plan_risk_analiz <int> 4, 1, 4, 4, 4, 3, 3, 5, 4, 2, 5, 5, 5, 5, 5…
## $ Yon_plan_eylem_plan <int> 3, 2, 4, 4, 4, 3, 3, 5, 4, 2, 5, 5, 4, 5, 5…
## $ Yon_plan_kaynak <int> 3, 3, 4, 4, 5, 4, 3, 5, 5, 2, 5, 5, 4, 4, 5…
## $ Yon_plan_oncelik <int> 2, 1, 4, 3, 4, 3, 3, 5, 5, 2, 5, 5, 4, 4, 5…
## $ Yon_plan_kalkinma_amac <int> 4, 1, 4, 3, 4, 3, 3, 5, 5, 2, 5, 5, 4, 4, 5…
## $ Yon_uyg_veri <int> 3, 1, 4, 3, 3, 3, 4, 5, 4, 3, 4, 5, 4, 4, 4…
## $ Yon_uyg_birim <int> 3, 2, 4, 3, 3, 3, 4, 5, 4, 3, 4, 5, 5, 4, 4…
## $ Yon_uyg_teknolojik <int> 3, 1, 4, 3, 4, 4, 5, 5, 4, 3, 4, 5, 5, 4, 4…
## $ Yon_uyg_egitim <int> 4, 2, 4, 3, 4, 4, 4, 4, 4, 3, 4, 5, 5, 4, 5…
## $ Yon_uyg_yetkinlik <int> 2, 1, 5, 3, 5, 3, 4, 5, 5, 3, 4, 5, 4, 4, 5…
## $ Yon_uyg_motivasyon <int> 2, 1, 5, 3, 5, 3, 3, 4, 5, 3, 5, 5, 3, 3, 5…
## $ Yon_uyg_calisan_baglilik <int> 4, 1, 5, 4, 4, 2, 2, 4, 5, 3, 4, 5, 3, 3, 5…
## $ Yon_uyg_adil_is <int> 4, 1, 5, 3, 5, 3, 2, 4, 4, 3, 4, 5, 4, 3, 5…
## $ Yon_uyg_firsat_esitlik <int> 5, 1, 5, 3, 5, 3, 3, 4, 4, 3, 4, 5, 5, 3, 5…
## $ Yon_ilet_internet <int> 1, 1, 5, 1, 2, 2, 3, 4, 4, 2, 4, 5, 4, 1, 5…
## $ Yon_ilet_pano <int> 4, 1, 5, 2, 5, 2, 3, 4, 4, 2, 4, 5, 4, 2, 5…
## $ Yon_ilet_kulup <int> 1, 1, 4, 1, 3, 2, 2, 5, 5, 1, 5, 5, 4, 2, 5…
## $ Yon_ilet_birim <int> 1, 1, 4, 3, 4, 3, 5, 5, 4, 1, 5, 5, 5, 3, 5…
## $ Yon_ilet_rapor <int> 1, 1, 4, 4, 4, 1, 3, 5, 5, 1, 5, 5, 4, 4, 5…
## $ Yon_kont_tedarikci <int> 3, 2, 4, 3, 3, 3, 4, 4, 3, 2, 4, 5, 3, 3, 5…
## $ Yon_kont_gozden_gecirme <int> 4, 2, 5, 4, 4, 3, 5, 4, 3, 2, 4, 5, 4, 3, 5…
## $ Yon_kont_sertifika <int> 3, 3, 5, 4, 4, 4, 4, 5, 3, 2, 4, 5, 4, 4, 5…
## $ Yon_kont_surec_hedef <int> 3, 2, 5, 4, 5, 3, 4, 4, 2, 2, 4, 5, 4, 4, 5…
## $ Yon_kont_bilim_temelli <int> 4, 2, 4, 4, 4, 3, 5, 4, 2, 2, 4, 5, 4, 4, 5…
## $ Yon_iyiles_hafiza <int> 3, 1, 5, 4, 5, 4, 4, 4, 5, 2, 5, 5, 5, 3, 5…
## $ Yon_iyiles_calisan_gorus <int> 4, 4, 5, 3, 5, 3, 3, 4, 4, 2, 5, 5, 5, 4, 5…
## $ Yon_iyiles_duzeltici_rapor <int> 4, 3, 5, 4, 4, 3, 3, 4, 5, 2, 5, 5, 4, 4, 5…
extract_factors <- function (df,what,howmany,reduce=0,rotat="promax"){
cat("________________ START --> ", what, "_____________________")
cat("\n")
center <- function(x) { return (x - mean(x))}
df_sub <- df %>% dplyr::select(starts_with(what)) %>% mutate(across(everything(), center))
CA <- round(cronbach.alpha(df_sub) $ alpha,2)
cat("\n")
cat("cronbach_alpa =", CA)
cat("\n")
if (reduce != 0) df_sub=df_sub[,-reduce]
FA<- df_sub%>%factanal(.,howmany, scores ="regression",rotation=rotat)
print(FA $ loadings)
explained <- 1-FA $ uniquenesses
barplot(explained,cex.names=0.7, col=1:length(explained),
main="faktor analizining acikladigi oranlar", cex.main=0.8)
cat("\n")
cat("faktor analizining acikladigi oranlar:");cat("\n")
explained_props <- as.data.frame(1-FA $ uniquenesses)
colnames(explained_props) ="explained_variances"
print(explained_props);cat("\n")
cat("likelihood ratio test | p-value:", FA $ PVAL); cat("\n")
if(FA $ PVAL<0.05) print("factors are not sufficient")
else cat("\n", "factors are sufficient")
cat("\n")
cat("________________ END _____________________")
outcome <-list(FA,df_sub)
return(outcome)
}
dummy_func <- function (df,this_X,this_key) {
dummy <- list()
for (a in 1:length(this_X)) {
dummy[[a]] <- df %>%
dplyr::select(starts_with(this_key)) %>%
dplyr::select(c(this_X[a])) %>%
dummy_cols %>%
dplyr::select(where(is.numeric)) %>%
dplyr::select(c(-1)) }
dummies_as_df <- do.call(data.frame,dummy)
return(dummies_as_df)
}
show_model_details <- function(model_now){
cat("\n")
model_now %>%
cooks.distance %>%
plot(.,type="h",col="black",
main=paste(model_now $call[2],"cooks distances (verilerin modele etkileri)"),
cex.main = 0.6)
abline(h=1,lty=2,col="red")
text(1:dim(model.matrix(model_now))[1],cooks.distance(model_now),
rownames(model.matrix(model_now)),cex=0.7)
cat(rep("##",3),sep="")
paste("Y =", model_now $call[2]) %>% print
cat(rep("##",3),sep="")
cat("\n")
model_now %>% summary %>% print
}
make_model <- function(df,Y_df,dummies,this_key){
# c("verim_satin_tekno","maliyet_karlilik")
df_pilot <- cbind(Y_df,dummies)
model_sosyal_cevresel_boyut <- lm(df_pilot $verim_satin_tekno~.,data = df_pilot[,-c(2)])
model_sosyal_cevresel_boyut%>%show_model_details
model_verimlilik_boyutu <- lm(df_pilot $ maliyet_karlilik~.,data = df_pilot [,-c(1)])
model_verimlilik_boyutu%>%show_model_details}
df_or <- df
df %>%
dplyr::select(starts_with("isletme") | starts_with("kisi")) %>% names
## [1] "isletme_kisi_egitim" "isletme_sektor" "isletme_kume_1"
## [4] "isletme_kume_2" "isletme_isim" "isletme_yabanciOrtak"
## [7] "isletme_yas" "isletme_olcek" "isletme_cal_say"
## [10] "kisi_cinsiyet" "kisi_bolum_poz" "kisi_kac_yildir"
make_table <- function(df,D){
cat(rep("\n",3))
T <- df %>%
dplyr::select(starts_with("isletme") | starts_with("kisi")) %>%
dplyr::select(all_of(D)) %>% table
print(T)
}
olcutler <- list(1,2,6,7,9)
for (a in 1:length(olcutler)) {make_table(df,olcutler[[a]])}
##
##
##
## isletme_kisi_egitim
## Doktora Lisans Lisansustu Lise Yuksekokul
## 2 78 37 1 3
##
##
##
## isletme_sektor
## Cevre-Geri Donusum Gıda Kamu Tuzel Makine-Metal
## 8 10 1 41
## Mobilya Plastik-Kimya Tekstil-Ambalaj Ulasim-Lojistik
## 3 15 3 23
## uretim Xx Yapi Malzemeleri
## 1 1 15
##
##
##
## isletme_yabanciOrtak
## Evet Hayir
## 51 70
##
##
##
## isletme_yas
## 1 - 10 yil 11 - 30 yil 30 yildan fazla
## 15 51 55
##
##
##
## isletme_cal_say
## 10 - 49 kisi 250 kisi ve uzeri 50 - 249 kisi
## 19 45 57
df_sektor <-df %>% dplyr::filter(isletme_sektor != "Xx" &
isletme_sektor != "Mobilya" &
isletme_sektor != "Kamu Tuzel" &
isletme_sektor != "Tekstil-Ambalaj" &
isletme_sektor != "uretim")
df_sektor_egitim <- df_sektor %>% dplyr::filter(isletme_kisi_egitim != "Doktora" &
isletme_kisi_egitim != "Yuksekokul" &
isletme_kisi_egitim != "Lise")
df_all <- df
df <- df_sektor_egitim
see_eko <- extract_factors(df,"far_eko",2)
## ________________ START --> far_eko _____________________
##
## cronbach_alpa = 0.83
##
## Loadings:
## Factor1 Factor2
## far_eko_verimlilik 0.868
## far_eko_satin_alma 0.775 0.120
## far_eko_teknoloji 0.879 -0.116
## far_eko_maliyet 1.010
## far_eko_karlilik 0.868
##
## Factor1 Factor2
## SS loadings 2.129 1.802
## Proportion Var 0.426 0.360
## Cumulative Var 0.426 0.786
##
## faktor analizining acikladigi oranlar:
## explained_variances
## far_eko_verimlilik 0.7591640
## far_eko_satin_alma 0.6904724
## far_eko_teknoloji 0.7032006
## far_eko_maliyet 0.9950000
## far_eko_karlilik 0.7738261
##
## likelihood ratio test | p-value: 0.1959708
##
## factors are sufficient
## ________________ END _____________________
see_eko_scores <- see_eko[[1]] $ scores
cat("\n")
see_eko_scores %>% dim %>% print
## [1] 107 2
colnames(see_eko_scores) <- c("verim_satin_tekno","maliyet_karlilik")
see_eko_scores %>% boxplot(.,horizontal=TRUE,cex.axis=0.7,
col=1:dim(see_eko_scores)[2],
main = "Faktor analizinin turettigi bagimli degiskenler", cex.main=0.7)
du_df <- dummy_func(df,c(1),"isletme")
cat("\n")
df_sektor %>% dplyr:: select(isletme_sektor) %>% table %>% print
## isletme_sektor
## Cevre-Geri Donusum Gıda Makine-Metal Plastik-Kimya
## 8 10 41 15
## Ulasim-Lojistik Yapi Malzemeleri
## 23 15
make_model(df,Y_df=see_eko_scores,
dummies= du_df,
this_key="isletme")
## ######[1] "Y = df_pilot$verim_satin_tekno ~ ."
## ######
##
## Call:
## lm(formula = df_pilot$verim_satin_tekno ~ ., data = df_pilot[,
## -c(2)])
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.1607 -0.5124 0.0924 0.7074 2.0257
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.05881 0.11850 -0.496 0.621
## isletme_kisi_egitim_Lisansustu 0.19067 0.21338 0.894 0.374
##
## Residual standard error: 1.019 on 105 degrees of freedom
## Multiple R-squared: 0.007547, Adjusted R-squared: -0.001905
## F-statistic: 0.7985 on 1 and 105 DF, p-value: 0.3736
## ######[1] "Y = df_pilot$maliyet_karlilik ~ ."
## ######
##
## Call:
## lm(formula = df_pilot$maliyet_karlilik ~ ., data = df_pilot[,
## -c(1)])
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.7964 -0.6609 0.1847 0.8327 2.4076
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.005573 0.126139 0.044 0.965
## isletme_kisi_egitim_Lisansustu -0.018071 0.227135 -0.080 0.937
##
## Residual standard error: 1.085 on 105 degrees of freedom
## Multiple R-squared: 6.028e-05, Adjusted R-squared: -0.009463
## F-statistic: 0.00633 on 1 and 105 DF, p-value: 0.9367
du_df <- dummy_func(df,c(2),"isletme")
make_model(df,Y_df=see_eko_scores,
dummies= du_df,
this_key="isletme")
## ######[1] "Y = df_pilot$verim_satin_tekno ~ ."
## ######
##
## Call:
## lm(formula = df_pilot$verim_satin_tekno ~ ., data = df_pilot[,
## -c(2)])
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.4361 -0.5618 0.1534 0.7363 1.7502
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.3579 0.3581 -1.000 0.320
## isletme_sektor_Gıda... 0.8111 0.5064 1.602 0.112
## isletme_sektor_Makine.Metal 0.1315 0.3931 0.334 0.739
## isletme_sektor_Plastik.Kimya 0.3707 0.4434 0.836 0.405
## isletme_sektor_Ulasim.Lojistik 0.5745 0.4181 1.374 0.172
## isletme_sektor_Yapi.Malzemeleri 0.5654 0.4434 1.275 0.205
##
## Residual standard error: 1.013 on 101 degrees of freedom
## Multiple R-squared: 0.05774, Adjusted R-squared: 0.01109
## F-statistic: 1.238 on 5 and 101 DF, p-value: 0.2971
## ######[1] "Y = df_pilot$maliyet_karlilik ~ ."
## ######
##
## Call:
## lm(formula = df_pilot$maliyet_karlilik ~ ., data = df_pilot[,
## -c(1)])
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.4676 -0.7083 0.0684 0.7879 2.0792
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.3340 0.3840 0.870 0.387
## isletme_sektor_Gıda... -0.2990 0.5431 -0.550 0.583
## isletme_sektor_Makine.Metal -0.2120 0.4216 -0.503 0.616
## isletme_sektor_Plastik.Kimya -0.2041 0.4756 -0.429 0.669
## isletme_sektor_Ulasim.Lojistik -0.6572 0.4485 -1.465 0.146
## isletme_sektor_Yapi.Malzemeleri -0.5037 0.4756 -1.059 0.292
##
## Residual standard error: 1.086 on 101 degrees of freedom
## Multiple R-squared: 0.03612, Adjusted R-squared: -0.0116
## F-statistic: 0.7569 on 5 and 101 DF, p-value: 0.583
du_df <- dummy_func(df,c(6),"isletme")
make_model(df,Y_df=see_eko_scores,
dummies= du_df,
this_key="isletme")
## ######[1] "Y = df_pilot$verim_satin_tekno ~ ."
## ######
##
## Call:
## lm(formula = df_pilot$verim_satin_tekno ~ ., data = df_pilot[,
## -c(2)])
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.0980 -0.5280 0.1551 0.7036 2.0884
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.1612 0.1494 1.079 0.283
## isletme_yabanciOrtak_Hayir -0.2827 0.1979 -1.429 0.156
##
## Residual standard error: 1.013 on 105 degrees of freedom
## Multiple R-squared: 0.01906, Adjusted R-squared: 0.009723
## F-statistic: 2.041 on 1 and 105 DF, p-value: 0.1561
## ######[1] "Y = df_pilot$maliyet_karlilik ~ ."
## ######
##
## Call:
## lm(formula = df_pilot$maliyet_karlilik ~ ., data = df_pilot[,
## -c(1)])
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.8078 -0.6672 0.1733 0.8411 2.3961
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.02260 0.15997 -0.141 0.888
## isletme_yabanciOrtak_Hayir 0.03965 0.21186 0.187 0.852
##
## Residual standard error: 1.085 on 105 degrees of freedom
## Multiple R-squared: 0.0003335, Adjusted R-squared: -0.009187
## F-statistic: 0.03503 on 1 and 105 DF, p-value: 0.8519
du_df <- dummy_func(df,c(7),"isletme")
make_model(df,Y_df=see_eko_scores,
dummies= du_df,
this_key="isletme")
## ######[1] "Y = df_pilot$verim_satin_tekno ~ ."
## ######
##
## Call:
## lm(formula = df_pilot$verim_satin_tekno ~ ., data = df_pilot[,
## -c(2)])
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.9529 -0.5608 0.1036 0.6617 1.7005
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.03495 0.25744 -0.136 0.892
## isletme_yas_11...30.yil -0.23165 0.29726 -0.779 0.438
## isletme_yas_30.yildan.fazla 0.30135 0.29568 1.019 0.310
##
## Residual standard error: 0.9971 on 104 degrees of freedom
## Multiple R-squared: 0.0596, Adjusted R-squared: 0.04151
## F-statistic: 3.296 on 2 and 104 DF, p-value: 0.04095
## ######[1] "Y = df_pilot$maliyet_karlilik ~ ."
## ######
##
## Call:
## lm(formula = df_pilot$maliyet_karlilik ~ ., data = df_pilot[,
## -c(1)])
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.6837 -0.6031 0.0711 0.7675 2.1088
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.5775 0.2702 -2.137 0.03495 *
## isletme_yas_11...30.yil 0.8818 0.3120 2.826 0.00566 **
## isletme_yas_30.yildan.fazla 0.4704 0.3104 1.516 0.13266
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.047 on 104 degrees of freedom
## Multiple R-squared: 0.07852, Adjusted R-squared: 0.0608
## F-statistic: 4.431 on 2 and 104 DF, p-value: 0.01423
du_df <- dummy_func(df,c(6,7),"isletme")
make_model(df,Y_df=see_eko_scores,
dummies= du_df,
this_key="isletme")
## ######[1] "Y = df_pilot$verim_satin_tekno ~ ."
## ######
##
## Call:
## lm(formula = df_pilot$verim_satin_tekno ~ ., data = df_pilot[,
## -c(2)])
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.8013 -0.5651 0.1579 0.6809 1.8249
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.1384 0.2753 0.503 0.6164
## isletme_yabanciOrtak_Hayir -0.3250 0.1937 -1.678 0.0965 .
## isletme_yas_11...30.yil -0.2316 0.2947 -0.786 0.4336
## isletme_yas_30.yildan.fazla 0.3285 0.2936 1.119 0.2657
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9885 on 103 degrees of freedom
## Multiple R-squared: 0.08461, Adjusted R-squared: 0.05795
## F-statistic: 3.173 on 3 and 103 DF, p-value: 0.02737
## ######[1] "Y = df_pilot$maliyet_karlilik ~ ."
## ######
##
## Call:
## lm(formula = df_pilot$maliyet_karlilik ~ ., data = df_pilot[,
## -c(1)])
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.7052 -0.6269 0.0606 0.7539 2.0827
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.60740 0.29285 -2.074 0.04056 *
## isletme_yabanciOrtak_Hayir 0.05611 0.20603 0.272 0.78592
## isletme_yas_11...30.yil 0.88180 0.31345 2.813 0.00587 **
## isletme_yas_30.yildan.fazla 0.46572 0.31225 1.491 0.13889
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.051 on 103 degrees of freedom
## Multiple R-squared: 0.07919, Adjusted R-squared: 0.05237
## F-statistic: 2.953 on 3 and 103 DF, p-value: 0.0361
du_df <- dummy_func(df,c(9),"isletme")
make_model(df,Y_df=see_eko_scores,
dummies= du_df,
this_key="isletme")
## ######[1] "Y = df_pilot$verim_satin_tekno ~ ."
## ######
##
## Call:
## lm(formula = df_pilot$verim_satin_tekno ~ ., data = df_pilot[,
## -c(2)])
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.0274 -0.5925 0.1874 0.5926 1.6981
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.06724 0.24385 -0.276 0.783
## isletme_cal_say_50...249.kisi -0.12489 0.28228 -0.442 0.659
## isletme_cal_say_250.kisi.ve.uzeri 0.33598 0.29109 1.154 0.251
##
## Residual standard error: 1.005 on 104 degrees of freedom
## Multiple R-squared: 0.04376, Adjusted R-squared: 0.02537
## F-statistic: 2.38 on 2 and 104 DF, p-value: 0.09759
## ######[1] "Y = df_pilot$maliyet_karlilik ~ ."
## ######
##
## Call:
## lm(formula = df_pilot$maliyet_karlilik ~ ., data = df_pilot[,
## -c(1)])
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.7287 -0.7029 0.1104 0.8655 2.5021
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.08891 0.26379 -0.337 0.737
## isletme_cal_say_50...249.kisi 0.16884 0.30536 0.553 0.582
## isletme_cal_say_250.kisi.ve.uzeri 0.02680 0.31490 0.085 0.932
##
## Residual standard error: 1.088 on 104 degrees of freedom
## Multiple R-squared: 0.004919, Adjusted R-squared: -0.01422
## F-statistic: 0.257 on 2 and 104 DF, p-value: 0.7738
du_df <- dummy_func(df,c(1,2,6,7),"isletme")
make_model(df,Y_df=see_eko_scores,
dummies= du_df,
this_key="isletme")
## ######[1] "Y = df_pilot$verim_satin_tekno ~ ."
## ######
##
## Call:
## lm(formula = df_pilot$verim_satin_tekno ~ ., data = df_pilot[,
## -c(2)])
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.9929 -0.5043 0.1075 0.7071 1.7043
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.01526 0.42707 -0.036 0.9716
## isletme_kisi_egitim_Lisansustu 0.13337 0.21727 0.614 0.5408
## isletme_sektor_Gıda... 0.53331 0.52436 1.017 0.3117
## isletme_sektor_Makine.Metal -0.03330 0.39787 -0.084 0.9335
## isletme_sektor_Plastik.Kimya 0.24540 0.44119 0.556 0.5793
## isletme_sektor_Ulasim.Lojistik 0.44071 0.41939 1.051 0.2959
## isletme_sektor_Yapi.Malzemeleri 0.42728 0.44639 0.957 0.3409
## isletme_yabanciOrtak_Hayir -0.37023 0.19807 -1.869 0.0646 .
## isletme_yas_11...30.yil -0.28180 0.29927 -0.942 0.3487
## isletme_yas_30.yildan.fazla 0.20729 0.30503 0.680 0.4984
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9893 on 97 degrees of freedom
## Multiple R-squared: 0.1365, Adjusted R-squared: 0.05637
## F-statistic: 1.704 on 9 and 97 DF, p-value: 0.0984
## ######[1] "Y = df_pilot$maliyet_karlilik ~ ."
## ######
##
## Call:
## lm(formula = df_pilot$maliyet_karlilik ~ ., data = df_pilot[,
## -c(1)])
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.3343 -0.6713 0.0758 0.7195 1.9117
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.32197 0.45496 -0.708 0.48083
## isletme_kisi_egitim_Lisansustu 0.07360 0.23146 0.318 0.75116
## isletme_sektor_Gıda... -0.24461 0.55860 -0.438 0.66243
## isletme_sektor_Makine.Metal -0.28000 0.42386 -0.661 0.51043
## isletme_sektor_Plastik.Kimya -0.32050 0.47000 -0.682 0.49693
## isletme_sektor_Ulasim.Lojistik -0.80044 0.44678 -1.792 0.07632 .
## isletme_sektor_Yapi.Malzemeleri -0.63274 0.47554 -1.331 0.18645
## isletme_yabanciOrtak_Hayir 0.08402 0.21100 0.398 0.69135
## isletme_yas_11...30.yil 0.98516 0.31881 3.090 0.00261 **
## isletme_yas_30.yildan.fazla 0.58191 0.32495 1.791 0.07645 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.054 on 97 degrees of freedom
## Multiple R-squared: 0.1286, Adjusted R-squared: 0.04773
## F-statistic: 1.59 on 9 and 97 DF, p-value: 0.1288
du_df <- dummy_func(df,c(1,2,6,7,9),"isletme")
make_model(df,Y_df=see_eko_scores,
dummies= du_df,
this_key="isletme")
## ######[1] "Y = df_pilot$verim_satin_tekno ~ ."
## ######
##
## Call:
## lm(formula = df_pilot$verim_satin_tekno ~ ., data = df_pilot[,
## -c(2)])
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.83127 -0.52466 0.07088 0.66744 1.61857
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.08006 0.43132 0.186 0.8531
## isletme_kisi_egitim_Lisansustu 0.08476 0.21958 0.386 0.7003
## isletme_sektor_Gıda... 0.60979 0.56248 1.084 0.2811
## isletme_sektor_Makine.Metal 0.01285 0.43964 0.029 0.9767
## isletme_sektor_Plastik.Kimya 0.28724 0.45815 0.627 0.5322
## isletme_sektor_Ulasim.Lojistik 0.42921 0.45395 0.946 0.3468
## isletme_sektor_Yapi.Malzemeleri 0.45939 0.48372 0.950 0.3447
## isletme_yabanciOrtak_Hayir -0.39983 0.20099 -1.989 0.0495 *
## isletme_yas_11...30.yil -0.25690 0.33707 -0.762 0.4479
## isletme_yas_30.yildan.fazla 0.15010 0.35353 0.425 0.6721
## isletme_cal_say_50...249.kisi -0.24079 0.35461 -0.679 0.4988
## isletme_cal_say_250.kisi.ve.uzeri 0.08873 0.38348 0.231 0.8175
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9881 on 95 degrees of freedom
## Multiple R-squared: 0.1563, Adjusted R-squared: 0.05866
## F-statistic: 1.601 on 11 and 95 DF, p-value: 0.111
## ######[1] "Y = df_pilot$maliyet_karlilik ~ ."
## ######
##
## Call:
## lm(formula = df_pilot$maliyet_karlilik ~ ., data = df_pilot[,
## -c(1)])
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.3056 -0.6497 0.0985 0.7257 1.9400
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.33083 0.46453 -0.712 0.47809
## isletme_kisi_egitim_Lisansustu 0.07609 0.23648 0.322 0.74833
## isletme_sektor_Gıda... -0.18188 0.60580 -0.300 0.76465
## isletme_sektor_Makine.Metal -0.21864 0.47350 -0.462 0.64532
## isletme_sektor_Plastik.Kimya -0.28133 0.49343 -0.570 0.56992
## isletme_sektor_Ulasim.Lojistik -0.73653 0.48891 -1.506 0.13526
## isletme_sektor_Yapi.Malzemeleri -0.56950 0.52097 -1.093 0.27709
## isletme_yabanciOrtak_Hayir 0.07674 0.21647 0.355 0.72375
## isletme_yas_11...30.yil 1.03792 0.36303 2.859 0.00522 **
## isletme_yas_30.yildan.fazla 0.65105 0.38075 1.710 0.09055 .
## isletme_cal_say_50...249.kisi -0.08651 0.38192 -0.227 0.82129
## isletme_cal_say_250.kisi.ve.uzeri -0.14563 0.41301 -0.353 0.72517
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.064 on 95 degrees of freedom
## Multiple R-squared: 0.1298, Adjusted R-squared: 0.02906
## F-statistic: 1.288 on 11 and 95 DF, p-value: 0.243