Final Transport_cleaned
Amra Fetahovic
2024-11-25
Ovaj dokument se sADRZI SAMO POBJEDNICKI MODEL P2.AGE.NEW. OVAJ SHEET SADRZI PRAAAVE KOEFF I IZADJE DA U PROSJEKU ZAPOSLENI TROSI 60 NA COMMUTING.
- koeficijenti i monetarna konverzija ja uradjena u drugom excel filu
- koeficijanti za mapiranje su uradjeni u drugom fileu takodjer I MAPIRANJE je u posebnom file.u
According to email from ismir 06.01.2024. I will include transport costs only fro hh where someone works so variable Activ_01,….Acitiv_09 ==1 or 2
Importing data
keep necessary vars 329
Analiza transporta
##subset df
library (dplyr)
hbs.transport <- hbs_shorter %>% select(contains(c("SifraDom","Entity","Canton","naziv_Opcine","RurUrb","ncomp","finweight", "poor","Relat","Sex","Age", "Educ","Activ", "HH_Monthly","S6", "COICOP_07", "_DA", "E07_a", "E07_b", "E07_c", "E07_d","E07_e", "E07_f")))Label unlabeled
Varijable koje cu koristi u transport cost
“mj_Q02_01_AM_S6” = Mj.Godisnja registracija vozila (obavezno osiguranje, tehnički pregled i ostali izdaci vezani za registraciju vozila) “mj_Q02_02_AM_S6” = Mj. Kasko osiguranje godisnje “mj_Q02_03_AM_S6” = Gume, sve vrste “mj_Q02_04_AM_S6” = Ostali rezervni dijelovi i pribor (svjećice, akumulatori itd.) “mj_Q02_05_AM_S6” = Pribor za osobna vozila (prva pomoć, trokut, uže za vuču, signalni prsluci i sl.) “mj_Q02_06_AM_S6” = Ulja, maziva, antifriz, tečnost za brisače itd “mj_Q02_07_AM_S6” = Popravak i pranje vozila “mj_Q02_08_AM_S6” = Zakup privatne garaže ili parking mjesta “mj_Q02_09_AM_S6” = Unajmljivanje vozila bez vozača (rent-a-car) “mj_Q02_10_AM_S6” = Putarina “mj_Q04_02_AM_S6” = Koliki je bio iznos_Skolski autobus “mj_Q04_03_AM_S6” = Koliki je bio iznos_Mjesecne ili sezonske karte za gradski prevoz “mj_Q04_04_AM_S6” = Koliki je bio iznos_Karte za prevoz vozom “mj_DA_S_04” = Pojedinačne karte za gradski prevoz tramvajem “mj_DA_S_05” = Pojedinačne karte za gradski prevoz autobusom, trolejbusom i minibusom “mj_DA_S_06” = Taksi “mj_DA_S_07” = Parking na dnevnoj osnovi
Formiraj varijablu broj clanova koji rade n.employ
Napravi varijablu broj clanova u domacinstvu koji rade tj. Active = 1 (puno.r.v)i/ili 2 (pola r.v.) !
#sta Active sadrzi
str(hbs.transport$Activ_1)## num [1:7702] 8 8 8 8 2 7 8 3 8 8 ...
## - attr(*, "label")= chr "Status tekuce aktivnost (1-puno r.v.; 2-pola r.v.; 3-neyaposlen; 4-trazi prvo zaposlenje; 5-Domacica; 6-Student"| __truncated__
## - attr(*, "format.spss")= chr "F1.0"
## - attr(*, "display_width")= int 9Izbaci one u kojima niko ne radi
hbs.transport <- hbs.transport %>% dplyr::filter(amr.br.cl.radi == 1 |amr.br.cl.radi == 2 | amr.br.cl.radi == 3 | amr.br.cl.radi == 4 | amr.br.cl.radi == 5 | amr.br.cl.radi == 6 )
table (hbs.transport$amr.br.cl.radi) # nema vise domacinstava u kojima niko ne radi##
## 1 2 3 4 5 6
## 2572 1410 261 54 8 2Formiraj varijablu od hbs.transport$amr.br.cl.radi da bude 1,2,3 i vise i nazovi je n.employ
library(dplyr)
hbs.transport <- hbs.transport %>% mutate(n.employ = case_when(amr.br.cl.radi == 1 ~"one",amr.br.cl.radi ==2 ~ "two", amr.br.cl.radi %in% c(3,4,5,6) ~ "three and more"))
table(hbs.transport$n.employ)##
## one three and more two
## 2572 325 1410hbs.transport$n.employ <- factor (hbs.transport$n.employ)
round(prop.table(table(hbs.transport$n.employ)),3)*100##
## one three and more two
## 59.7 7.5 32.7Relevel n.employ
This will be one of the independent
Labelled whats unlabelled
library(labelled)
var_label(hbs.transport$Q01_03_NW_OT_YN_S6) <- labelled_spss("Koju vrstu vozila vase domacinstvo posjeduje? Iskljucujuci vozila koja se koriste u poslovne svrhe")
var_label(hbs.transport$Q01_01_NW_DS_YN_S6) <- labelled_spss("Koju vrstu vozila vase domacinstvo posjeduje? Iskljucujuci vozila koja se koriste u poslovne svrhe")
var_label(hbs.transport$Q01_02_NW_BZ_YN_S6) <- labelled_spss("Koju vrstu vozila vase domacinstvo posjeduje? Iskljucujuci vozila koja se koriste u poslovne svrhe")Mutate Educ_1
Steceno obrazovanje: 1 - bez skole; 2- nepotpuna osnovna 8.g.skola 3 - nepotpuna osnovna 9.g. skola; 4-osnovna skola, 5-srednja skola, 6-specijalizacia poslije s.s; 7 - Visa skola; 8 - Fakultet; 9 - Master; 10 - Doktorat
prop.table(table(hbs.transport$Educ_1))##
## 1 2 3 4 5 6
## 0.0253076387 0.0390062689 0.0006965405 0.1827257952 0.6073833295 0.0085906664
## 7 8 9 10
## 0.0394706292 0.0870675644 0.0069654052 0.0027861621Mislim da je bolje napraviti, radi strukture slijedecu podjelu: 1 - bez skole, 2 i 3 nepotpuna osnovna, 4 osnovna, 5 i 6 srednja , 7 visa i 8,9 10 visoka, master i doktorat. ovo se razlikuje od prethodnog koji je koristen u slucaju wage i koji je bio slijdeci: bez osnovne skole (1,2,3), osnovna skola (4), srednja skola (5,6), fakultet (7,8,9,10).
#Relevel Educ
hbs.transport$Educ.1 <- forcats::fct_relevel (hbs.transport$Educ.1, "no education","no elementary", "elementary", "high schl","college", "university")
table(hbs.transport$Educ.1)##
## no education no elementary elementary high schl college
## 109 171 787 2653 170
## university
## 417str(hbs.transport$Educ.1)## Factor w/ 6 levels "no education",..: 4 4 4 4 4 3 3 4 4 5 ...Formiraj Activ.1 na bazi transport
Ovo vec nema smisla zato sto ne uzimam Activity of hh vec imam da su oni koji rade. Probala sam sa varijabntom da uzmem Activ.1 za prva dva clana ali mislim da to nije u redu jer se citavo vrijeme fokusirma na prvog clana domacinstva i po obrazovanju i po spolu i po godinama. ne uyimam druge radi visoke korelacije. recimo Age_1 i Age_2 imaju korelaciju 65 a spol 90
table (hbs.transport$Activ_1) #mislim da je ovdje bolje staviti dummy prvog clana##
## 1 2 3 4 5 6 7 8 9
## 2707 374 250 29 179 1 60 685 22hbs.transport <- hbs.transport %>% mutate(Activ.1 = case_when(Activ_1 == 1 ~"1", Activ_1!=1 ~
"0"))
table (hbs.transport$Activ.1) #Mislim da mi je ova varijabla puno bolja, dakle imam oni koji su full time bilo prvi ili drugi clan je 1 ostali su 0##
## 0 1
## 1600 2707###Relevel Activ.1
hbs.transport$Activ.1 <- forcats::fct_relevel (hbs.transport$Activ.1, "1", "0")
table(hbs.transport$Activ.1)##
## 1 0
## 2707 1600Mutate ncomp
11 + clanova staviti u jednu varijabli
table (hbs.transport$ncomp) #mislim da je ovdje bolje staviti dummy prvog clana##
## 1 2 3 4 5 6 7 8 9 10 11 13
## 223 707 1020 1315 620 283 88 31 10 4 5 1hbs.transport <- hbs.transport %>% mutate(ncomp1 = case_when(ncomp %in% c(11,13) ~"11+", ncomp == 1 ~ "one", ncomp == 2 ~ "two", ncomp == 3 ~ "three", ncomp==4 ~ "four", ncomp ==5 ~ "five", ncomp==6 ~ "six", ncomp == 7 ~ "seven", ncomp == 8 ~ "eight", ncomp == 9 ~ "nine", ncomp == 10 ~ "ten"))
table (hbs.transport$ncomp1) #Mislim da mi je ova varijabla puno bolja, dakle imam oni koji su full time bilo prvi ili drugi clan je 1 ostali su 0##
## 11+ eight five four nine one seven six ten three two
## 6 31 620 1315 10 223 88 283 4 1020 707#Relevel ncomp1
hbs.transport$ncomp1 <- forcats::fct_relevel (hbs.transport$ncomp1, "one", "two", "three", "four", "five", "six", "seven", "eight", "nine", "ten", "11+")
table(hbs.transport$ncomp1)##
## one two three four five six seven eight nine ten 11+
## 223 707 1020 1315 620 283 88 31 10 4 6Kad sam uporedila regresije sa ncomp1 i ncomp dobila sam da je ipak bolje da imaju ncomp bude vise tih varijabli sign kao i opstinskih varijabli pa cu uraditi relevel za to
#Relevel ncomp
hbs.transport$ncomp <- forcats::fct_relevel (as.factor(hbs.transport$ncomp), "1", "2", "3", "4", "5", "6", "7", "8", "9","10","11","13")
table(hbs.transport$ncomp)##
## 1 2 3 4 5 6 7 8 9 10 11 13
## 223 707 1020 1315 620 283 88 31 10 4 5 1Drop upper and low
summary(hbs.transport$mj.trans.AM)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00 31.67 47.98 102.05 113.27 2509.05summary (hbs.transport$mj.trans.AM/hbs.transport$amr.br.cl.radi) #ispada da ovaj koji trosi 2,509 KM mjesecno ima samo 1 clana zaposlenog tako da cu brisati samo upper percentiles.## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00 18.75 37.50 74.97 81.67 2509.05table(hbs.transport$mj.trans.AM==0) #imamo 581 domacinstva koja nisu trosila na transport a imaju zaposlenog clana familije. 590??(24.11.2024)##
## FALSE TRUE
## 3717 590plot(density(hbs.transport$mj.trans.AM))
# Top 1% is removed
hbs.transport.drop <- hbs.transport %>% dplyr::filter(mj.trans.AM < quantile (hbs.transport$mj.trans.AM, 0.99, na.rm = T) )
summary(hbs.transport.drop$mj.trans.AM)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00 31.67 46.67 91.19 110.24 745.50summary(hbs.transport.drop$mj.trans.AM/hbs.transport.drop$amr.br.cl.radi) #i ovdje ispada da domacinstva u koijma 1 clan rad potrosi se na transport 745KM.. ali ovo je vec realnije.. pa cu ostaviti## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00 18.75 37.50 67.91 79.17 745.50plot(density(hbs.transport.drop$mj.trans.AM)) #realnije je uzeti median
#7551Check again low frequency municipality after deleting percentils
low.freq.1 <- hbs.transport.drop %>%
select(c(naziv_Opcine, mj.trans.AM)) %>%
group_by(naziv_Opcine) %>%
summarise (n = n()) %>%
arrange (desc(n))%>%
dplyr::filter(n<10)
low.freq.1$naziv_Opcine #21 opstina je low.frequency## [1] "Cajnice" "Hadzici" "Kostajnica" "Kupres" "Mrkonjic"
## [6] "Bileca" "Srebrenica" "D-Samac" "Doboj-Jug" "Milici"
## [11] "Odzak" "Teocak" "Pelagicevo" "Vares" "B.Grahovo"
## [16] "Han Pijesak" "Vukosavlje" "Jablanica" "Olovo" "Ostra Luka"
## [21] "Sekovici" "Trnovo" "Usora" "Berkovici" "Donji Zabar"
## [26] "Kalinovik" "Osmaci" "Petrovo" "Sapna" "Sipovo"
## [31] "Jezero" "Krupa na Uni" "Neum" "Ravno"
## attr(,"label")
## [1] "Naziv opcine"
## attr(,"format.spss")
## [1] "A12"dim(hbs.transport.drop) #4234 obs i ## [1] 4263 208unique (hbs.transport.drop$naziv_Opcine) #131 opstina dakle sa brisanjem trebalo bi biti 97## [1] "B.Grahovo" "B.Krupa" "B.Petrovac" "Banovici" "Banja Luka"
## [6] "Berkovici" "Bihac" "Bijeljina" "Bileca" "Bratunac"
## [11] "Brcko" "Breza" "Brod" "Bugojno" "Busovaca"
## [16] "Buzim" "C.Sarajevo" "Cajnice" "Capljina" "Cazin"
## [21] "Celic" "Celinac" "Citluk" "D-Samac" "D.Vakuf"
## [26] "Derventa" "Doboj" "Doboj-Istok" "Doboj-Jug" "Donji Zabar"
## [31] "Drvar" "Foca - RS" "Fojnica" "G.Vakuf" "Gacko"
## [36] "Glamoc" "Gorazde" "Gracanica" "Grad Mostar" "Gradacac"
## [41] "Gradiska" "Grude" "Hadzici" "Han Pijesak" "I.Ilidza"
## [46] "Ilidza" "Ilijas" "Jablanica" "Jajce" "Jezero"
## [51] "K.Dubica" "Kakanj" "Kalesija" "Kalinovik" "Kiseljak"
## [56] "Kladanj" "Kljuc" "Knezevo" "Konjic" "Kostajnica"
## [61] "Kotor Varos" "Kresevo" "Krupa na Uni" "Kupres" "Laktasi"
## [66] "Livno" "Lopare" "Lukavac" "Ljubuski" "Maglaj"
## [71] "Milici" "Modrica" "Mrkonjic" "N.G.Sarajevo" "N.Gorazde"
## [76] "N.Sarajevo" "N.Travnik" "Neum" "Nevesinje" "Novi Grad"
## [81] "Odzak" "Olovo" "Orasje" "Osmaci" "Ostra Luka"
## [86] "Pale" "Pelagicevo" "Petrovo" "Posusje" "Prijedor"
## [91] "Prnjavor" "Prozor" "Ravno" "Ribnik" "Rogatica"
## [96] "Rudo" "S.Brijeg" "S.G.Sarajevo" "Samac" "Sanski Most"
## [101] "Sapna" "Sekovici" "Sipovo" "Sokolac" "Srbac"
## [106] "Srebrenica" "Srebrenik" "Stolac" "Teocak" "Tesanj"
## [111] "Teslic" "Tomislavgrad" "Travnik" "Trebinje" "Trnovo"
## [116] "Tuzla" "Ugljevik" "Usora" "V.Kladusa" "Vares"
## [121] "Visegrad" "Visoko" "Vitez" "Vlasenica" "Vogosca"
## [126] "Vukosavlje" "Zavidovici" "Zenica" "Zepce" "Zivinice"
## [131] "Zvornik"Delete low frequency obs <10 for hbs.transport.drop
Analiza mj.trans.AM
library(ggplot2)
dim(hbs.transport.drop.1) #4096## [1] 4096 208table (hbs.transport.drop.1$naziv_Opcine)##
## B.Krupa B.Petrovac Banovici Banja Luka Bihac Bijeljina
## 28 11 23 215 69 180
## Bratunac Brcko Breza Brod Bugojno Busovaca
## 11 246 13 22 56 25
## Buzim C.Sarajevo Capljina Cazin Celic Celinac
## 11 55 21 56 11 11
## Citluk D.Vakuf Derventa Doboj Doboj-Istok Drvar
## 23 34 29 55 12 13
## Foca - RS Fojnica G.Vakuf Gacko Glamoc Gorazde
## 21 16 25 19 10 31
## Gracanica Grad Mostar Gradacac Gradiska Grude I.Ilidza
## 77 168 35 30 20 13
## Ilidza Ilijas Jajce K.Dubica Kakanj Kalesija
## 49 15 21 37 72 28
## Kiseljak Kladanj Kljuc Knezevo Konjic Kotor Varos
## 36 13 21 14 47 16
## Kresevo Laktasi Livno Lopare Lukavac Ljubuski
## 15 42 22 27 44 26
## Maglaj Modrica N.G.Sarajevo N.Gorazde N.Sarajevo N.Travnik
## 40 34 160 10 49 49
## Nevesinje Novi Grad Orasje Pale Posusje Prijedor
## 11 47 25 20 13 82
## Prnjavor Prozor Ribnik Rogatica Rudo S.Brijeg
## 37 11 14 14 16 37
## S.G.Sarajevo Samac Sanski Most Sokolac Srbac Srebrenik
## 36 51 73 11 19 68
## Stolac Tesanj Teslic Tomislavgrad Travnik Trebinje
## 11 59 30 19 97 33
## Tuzla Ugljevik V.Kladusa Visegrad Visoko Vitez
## 112 17 47 12 50 46
## Vlasenica Vogosca Zavidovici Zenica Zepce Zivinice
## 16 20 55 163 10 78
## Zvornik
## 54summary (hbs.transport.drop.1$mj.trans.AM)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00 31.67 47.50 91.24 110.00 745.50sd (hbs.transport.drop.1$mj.trans.AM)## [1] 113.6885ggplot(data=hbs.transport.drop.1, mapping = aes(mj.trans.AM)) + geom_density() + xlab("Monthly household expenditure on transport (BAM)")
dim(hbs.transport.drop.1)## [1] 4096 208round(prop.table(table(hbs.transport.drop.1$ncomp)),3)*100##
## 1 2 3 4 5 6 7 8 9 10 11 13
## 5.2 16.5 23.9 30.4 14.4 6.5 2.0 0.7 0.2 0.1 0.1 0.0round(prop.table(table(hbs.transport.drop.1$Educ_1)),3)*100##
## 1 2 3 4 5 6 7 8 9 10
## 2.5 3.9 0.1 18.2 60.9 0.9 3.8 8.8 0.7 0.3Sada winning model ali sa hbs.transport.drop.1
table(hbs.transport.drop.1$ncomp1) #no Nas##
## one two three four five six seven eight nine ten 11+
## 212 674 979 1247 588 267 82 29 9 4 5prop.table(table(hbs.transport.drop.1$ncomp1))#no Nas##
## one two three four five six
## 0.0517578125 0.1645507812 0.2390136719 0.3044433594 0.1435546875 0.0651855469
## seven eight nine ten 11+
## 0.0200195312 0.0070800781 0.0021972656 0.0009765625 0.0012207031summary (hbs.transport.drop.1$n.employ) #no Nas.## one two three and more
## 2448 1341 307str(hbs.transport.drop.1$n.employ) ## Factor w/ 3 levels "one","two","three and more": 1 1 1 1 1 2 2 2 2 1 ...prop.table(table(hbs.transport.drop.1$n.employ))#no Nas##
## one two three and more
## 0.59765625 0.32739258 0.07495117table (hbs.transport.drop.1$Activ.1) #probacu bez ovoga mozda mi se sudara sa drugim..##
## 1 0
## 2575 1521prop.table(table(hbs.transport.drop.1$Activ.1))#no Nas##
## 1 0
## 0.6286621 0.3713379summary (hbs.transport.drop.1$Age_1)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 21.00 43.75 52.00 51.62 59.00 95.00sd(hbs.transport.drop.1$Age_1)## [1] 11.90181prop.table (table (hbs.transport.drop.1$Sex_1))##
## 1 2
## 0.8479004 0.1520996Correlation of indipendant
library(polycor)
polychor (hbs.transport.drop.1$ncomp, hbs.transport.drop.1$n.employ)## [1] 0.3791869#0.379 nije visoka i stavicu ih u model
polychor (hbs.transport.drop.1$n.employ, hbs.transport.drop.1$Activ.1) #negativna korelacija i niska## [1] -0.2362859polychor (hbs.transport.drop.1$n.employ, hbs.transport.drop.1$Activ.1) #negativna i -0.286 #n.employ.1 je samo u transport1 bazi## [1] -0.2362859Bila sam budala i dodavala + 0.001 umjesto + 1 jer je log(1)=0
#prethodni sa ncomp1 i normal Activ
p2.age.new <- lm (log(mj.trans.AM + 1) ~ factor (Sex_1)+ factor(Educ.1) + factor (Activ.1) + n.employ + factor (ncomp1) + as.numeric(Age_1) + as.numeric(Age_1^2) +factor (naziv_Opcine), data = hbs.transport.drop.1)
summary(p2.age.new)##
## Call:
## lm(formula = log(mj.trans.AM + 1) ~ factor(Sex_1) + factor(Educ.1) +
## factor(Activ.1) + n.employ + factor(ncomp1) + as.numeric(Age_1) +
## as.numeric(Age_1^2) + factor(naziv_Opcine), data = hbs.transport.drop.1)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.8254 -0.5045 0.2407 0.9233 4.1550
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.1505558 0.4797694 2.398 0.016524 *
## factor(Sex_1)2 -0.2260883 0.0741011 -3.051 0.002295 **
## factor(Educ.1)no elementary -0.0900952 0.2058774 -0.438 0.661689
## factor(Educ.1)elementary -0.0150239 0.1808393 -0.083 0.933793
## factor(Educ.1)high schl 0.3778419 0.1819172 2.077 0.037866 *
## factor(Educ.1)college 0.5880373 0.2171602 2.708 0.006801 **
## factor(Educ.1)university 0.6674755 0.1972142 3.385 0.000720 ***
## factor(Activ.1)0 -0.1899942 0.0613495 -3.097 0.001969 **
## n.employtwo 0.3142005 0.0563010 5.581 2.55e-08 ***
## n.employthree and more 0.3703289 0.1022819 3.621 0.000298 ***
## factor(ncomp1)two 0.6677931 0.1257312 5.311 1.15e-07 ***
## factor(ncomp1)three 1.0225817 0.1248022 8.194 3.38e-16 ***
## factor(ncomp1)four 1.3282649 0.1237333 10.735 < 2e-16 ***
## factor(ncomp1)five 1.3552052 0.1336170 10.142 < 2e-16 ***
## factor(ncomp1)six 1.5426339 0.1533921 10.057 < 2e-16 ***
## factor(ncomp1)seven 1.8124582 0.2117077 8.561 < 2e-16 ***
## factor(ncomp1)eight 0.9510854 0.3174765 2.996 0.002754 **
## factor(ncomp1)nine 1.7413764 0.5369215 3.243 0.001191 **
## factor(ncomp1)ten 2.6003379 0.7955753 3.269 0.001090 **
## factor(ncomp1)11+ 2.1920016 0.7115032 3.081 0.002079 **
## as.numeric(Age_1) 0.0154416 0.0140904 1.096 0.273191
## as.numeric(Age_1^2) -0.0001123 0.0001355 -0.828 0.407482
## factor(naziv_Opcine)B.Petrovac -0.3776748 0.5515335 -0.685 0.493528
## factor(naziv_Opcine)Banovici 1.0374490 0.4380510 2.368 0.017916 *
## factor(naziv_Opcine)Banja Luka 0.8319756 0.3165001 2.629 0.008605 **
## factor(naziv_Opcine)Bihac 0.7092533 0.3502148 2.025 0.042914 *
## factor(naziv_Opcine)Bijeljina 0.0995604 0.3194100 0.312 0.755284
## factor(naziv_Opcine)Bratunac 0.5256885 0.5513729 0.953 0.340437
## factor(naziv_Opcine)Brcko 0.5648749 0.3125690 1.807 0.070807 .
## factor(naziv_Opcine)Breza -0.0250029 0.5208487 -0.048 0.961715
## factor(naziv_Opcine)Brod 0.1702639 0.4443096 0.383 0.701584
## factor(naziv_Opcine)Bugojno 0.5092489 0.3617734 1.408 0.159314
## factor(naziv_Opcine)Busovaca 1.5139902 0.4274176 3.542 0.000401 ***
## factor(naziv_Opcine)Buzim 0.2525970 0.5510536 0.458 0.646698
## factor(naziv_Opcine)C.Sarajevo 0.5672652 0.3651561 1.553 0.120386
## factor(naziv_Opcine)Capljina 1.4569535 0.4468256 3.261 0.001121 **
## factor(naziv_Opcine)Cazin 0.4934372 0.3576542 1.380 0.167772
## factor(naziv_Opcine)Celic 0.2538266 0.5516448 0.460 0.645450
## factor(naziv_Opcine)Celinac 0.8521213 0.5514201 1.545 0.122348
## factor(naziv_Opcine)Citluk 1.6963959 0.4395818 3.859 0.000116 ***
## factor(naziv_Opcine)D.Vakuf 0.7265216 0.3982888 1.824 0.068211 .
## factor(naziv_Opcine)Derventa 0.3851238 0.4155416 0.927 0.354087
## factor(naziv_Opcine)Doboj 0.7510947 0.3628484 2.070 0.038517 *
## factor(naziv_Opcine)Doboj-Istok 1.0621164 0.5340125 1.989 0.046777 *
## factor(naziv_Opcine)Drvar -0.1032951 0.5216447 -0.198 0.843041
## factor(naziv_Opcine)Foca - RS 0.4941934 0.4494446 1.100 0.271588
## factor(naziv_Opcine)Fojnica 1.3066619 0.4869032 2.684 0.007313 **
## factor(naziv_Opcine)G.Vakuf 0.9023135 0.4277387 2.109 0.034964 *
## factor(naziv_Opcine)Gacko -0.1344266 0.4621885 -0.291 0.771183
## factor(naziv_Opcine)Glamoc -0.4467542 0.5729998 -0.780 0.435628
## factor(naziv_Opcine)Gorazde 0.2938519 0.4063192 0.723 0.469597
## factor(naziv_Opcine)Gracanica 0.8304466 0.3446174 2.410 0.016008 *
## factor(naziv_Opcine)Grad Mostar 0.8545848 0.3210955 2.661 0.007811 **
## factor(naziv_Opcine)Gradacac 1.1328375 0.3947147 2.870 0.004126 **
## factor(naziv_Opcine)Gradiska 1.2253599 0.4101429 2.988 0.002829 **
## factor(naziv_Opcine)Grude 0.7754123 0.4549501 1.704 0.088386 .
## factor(naziv_Opcine)I.Ilidza -0.1016630 0.5219792 -0.195 0.845587
## factor(naziv_Opcine)Ilidza 0.7202101 0.3698139 1.947 0.051546 .
## factor(naziv_Opcine)Ilijas 0.0054865 0.4964606 0.011 0.991183
## factor(naziv_Opcine)Jajce 0.7977866 0.4493668 1.775 0.075915 .
## factor(naziv_Opcine)K.Dubica 0.7056375 0.3902432 1.808 0.070651 .
## factor(naziv_Opcine)Kakanj 0.3589538 0.3475932 1.033 0.301815
## factor(naziv_Opcine)Kalesija 1.0474252 0.4171122 2.511 0.012074 *
## factor(naziv_Opcine)Kiseljak 1.1095210 0.3928253 2.824 0.004760 **
## factor(naziv_Opcine)Kladanj 0.7145529 0.5205399 1.373 0.169918
## factor(naziv_Opcine)Kljuc 0.3275010 0.4490867 0.729 0.465886
## factor(naziv_Opcine)Knezevo 0.3404024 0.5080746 0.670 0.502906
## factor(naziv_Opcine)Konjic 0.3591983 0.3729116 0.963 0.335492
## factor(naziv_Opcine)Kotor Varos 0.4801895 0.4859631 0.988 0.323154
## factor(naziv_Opcine)Kresevo 1.0856282 0.4987258 2.177 0.029554 *
## factor(naziv_Opcine)Laktasi 0.8648153 0.3806588 2.272 0.023146 *
## factor(naziv_Opcine)Livno 0.3526910 0.4424478 0.797 0.425420
## factor(naziv_Opcine)Lopare 0.7584298 0.4197502 1.807 0.070860 .
## factor(naziv_Opcine)Lukavac 1.0099877 0.3766167 2.682 0.007354 **
## factor(naziv_Opcine)Ljubuski 1.5881069 0.4261135 3.727 0.000196 ***
## factor(naziv_Opcine)Maglaj 0.0543514 0.3836179 0.142 0.887339
## factor(naziv_Opcine)Modrica 0.4306975 0.3971366 1.085 0.278206
## factor(naziv_Opcine)N.G.Sarajevo 0.8521916 0.3224844 2.643 0.008260 **
## factor(naziv_Opcine)N.Gorazde 1.4668142 0.5719795 2.564 0.010370 *
## factor(naziv_Opcine)N.Sarajevo 0.2987892 0.3716899 0.804 0.421522
## factor(naziv_Opcine)N.Travnik 0.5789122 0.3702454 1.564 0.117993
## factor(naziv_Opcine)Nevesinje 0.3347484 0.5534635 0.605 0.545330
## factor(naziv_Opcine)Novi Grad -0.0477252 0.3722468 -0.128 0.897990
## factor(naziv_Opcine)Orasje 1.1081282 0.4293639 2.581 0.009891 **
## factor(naziv_Opcine)Pale -0.3578067 0.4559481 -0.785 0.432645
## factor(naziv_Opcine)Posusje 0.7400731 0.5200333 1.423 0.154778
## factor(naziv_Opcine)Prijedor 0.6675458 0.3435768 1.943 0.052095 .
## factor(naziv_Opcine)Prnjavor 0.8499690 0.3914054 2.172 0.029946 *
## factor(naziv_Opcine)Prozor 0.5571052 0.5552704 1.003 0.315775
## factor(naziv_Opcine)Ribnik 0.2680127 0.5098592 0.526 0.599154
## factor(naziv_Opcine)Rogatica -0.3667612 0.5085011 -0.721 0.470792
## factor(naziv_Opcine)Rudo 0.5131765 0.4872562 1.053 0.292315
## factor(naziv_Opcine)S.Brijeg 1.2726689 0.3925248 3.242 0.001196 **
## factor(naziv_Opcine)S.G.Sarajevo 0.6968395 0.3935049 1.771 0.076662 .
## factor(naziv_Opcine)Samac 0.5303376 0.3676873 1.442 0.149279
## factor(naziv_Opcine)Sanski Most 0.6261049 0.3471940 1.803 0.071412 .
## factor(naziv_Opcine)Sokolac -0.3603452 0.5535479 -0.651 0.515101
## factor(naziv_Opcine)Srbac 0.6694379 0.4617654 1.450 0.147211
## factor(naziv_Opcine)Srebrenik 0.0478550 0.3507034 0.136 0.891469
## factor(naziv_Opcine)Stolac 1.3577009 0.5516832 2.461 0.013897 *
## factor(naziv_Opcine)Tesanj 0.3373002 0.3585180 0.941 0.346855
## factor(naziv_Opcine)Teslic 0.0425347 0.4097788 0.104 0.917334
## factor(naziv_Opcine)Tomislavgrad 1.3567829 0.4633199 2.928 0.003427 **
## factor(naziv_Opcine)Travnik 1.0287700 0.3349072 3.072 0.002142 **
## factor(naziv_Opcine)Trebinje 0.0978234 0.4014138 0.244 0.807478
## factor(naziv_Opcine)Tuzla 0.7319490 0.3318877 2.205 0.027482 *
## factor(naziv_Opcine)Ugljevik 0.7206744 0.4765402 1.512 0.130536
## factor(naziv_Opcine)V.Kladusa 0.9439790 0.3710007 2.544 0.010984 *
## factor(naziv_Opcine)Visegrad -0.6144386 0.5364255 -1.145 0.252099
## factor(naziv_Opcine)Visoko 0.7017306 0.3683522 1.905 0.056845 .
## factor(naziv_Opcine)Vitez 1.0624150 0.3747041 2.835 0.004601 **
## factor(naziv_Opcine)Vlasenica 0.3591127 0.4865950 0.738 0.460551
## factor(naziv_Opcine)Vogosca 0.9660204 0.4554038 2.121 0.033963 *
## factor(naziv_Opcine)Zavidovici -0.1761700 0.3632730 -0.485 0.627737
## factor(naziv_Opcine)Zenica -0.0644010 0.3214624 -0.200 0.841227
## factor(naziv_Opcine)Zepce 0.3224940 0.5740909 0.562 0.574320
## factor(naziv_Opcine)Zivinice 1.0577272 0.3445152 3.070 0.002154 **
## factor(naziv_Opcine)Zvornik 0.8324091 0.3632602 2.291 0.021987 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.539 on 3978 degrees of freedom
## Multiple R-squared: 0.1784, Adjusted R-squared: 0.1542
## F-statistic: 7.382 on 117 and 3978 DF, p-value: < 2.2e-16#ovaj ima 35 sign munici ali 15.5 r2 for 90 Mail od Ismira 5.8: uzeti ovaj p2.age jer uzima i godine i treba ih ukljuciti.
NE uzimam nista od drugog clana, iako godine drugog clana su znacajne ali mi nekako se cini da nije dobro miksati prvog i drugog.. a mozda i je mogu provjeriti sa Ismirom.
Nakon razmisljanja shvatila sam da ipak nije bas smisleno oslanjati se malo na prvog pa malo na drugog clana domacinstva.. nekako nije bas smisleno a i kada okrenem u novac dobijem vema male iznose oko 3 km prosjecno po domacinstvu tako da cu ipak uzeti model gjde je sve oko 1 clana domacinstva A to je rekao i Ismir
Extracting the coefficients from the WINNING model on drop.1 database
model.coef.empl <- summary(p2.age.new)$coefficients
model.coef.empl # vidi koliko redova su koef.za druge varijable (22 redova su drugi koeficijenti)## Estimate Std. Error t value
## (Intercept) 1.1505558085 0.4797694252 2.39814325
## factor(Sex_1)2 -0.2260883477 0.0741010945 -3.05107973
## factor(Educ.1)no elementary -0.0900952185 0.2058774006 -0.43761587
## factor(Educ.1)elementary -0.0150238957 0.1808393053 -0.08307871
## factor(Educ.1)high schl 0.3778419261 0.1819171797 2.07699969
## factor(Educ.1)college 0.5880372575 0.2171602232 2.70784976
## factor(Educ.1)university 0.6674755019 0.1972141973 3.38452054
## factor(Activ.1)0 -0.1899942460 0.0613494889 -3.09691653
## n.employtwo 0.3142004870 0.0563010367 5.58072294
## n.employthree and more 0.3703288824 0.1022819201 3.62066807
## factor(ncomp1)two 0.6677930837 0.1257311860 5.31127642
## factor(ncomp1)three 1.0225816751 0.1248021574 8.19362178
## factor(ncomp1)four 1.3282648552 0.1237332954 10.73490244
## factor(ncomp1)five 1.3552052197 0.1336170423 10.14245785
## factor(ncomp1)six 1.5426338865 0.1533920704 10.05680335
## factor(ncomp1)seven 1.8124581905 0.2117076842 8.56113559
## factor(ncomp1)eight 0.9510853967 0.3174764887 2.99576640
## factor(ncomp1)nine 1.7413763913 0.5369214711 3.24326086
## factor(ncomp1)ten 2.6003379273 0.7955752869 3.26850013
## factor(ncomp1)11+ 2.1920015695 0.7115031854 3.08080359
## as.numeric(Age_1) 0.0154416088 0.0140904182 1.09589429
## as.numeric(Age_1^2) -0.0001122846 0.0001355406 -0.82842073
## factor(naziv_Opcine)B.Petrovac -0.3776747521 0.5515335086 -0.68477209
## factor(naziv_Opcine)Banovici 1.0374490107 0.4380510255 2.36832914
## factor(naziv_Opcine)Banja Luka 0.8319755743 0.3165000527 2.62867436
## factor(naziv_Opcine)Bihac 0.7092532952 0.3502147714 2.02519526
## factor(naziv_Opcine)Bijeljina 0.0995604141 0.3194100386 0.31170096
## factor(naziv_Opcine)Bratunac 0.5256885481 0.5513728956 0.95341746
## factor(naziv_Opcine)Brcko 0.5648748698 0.3125690207 1.80720043
## factor(naziv_Opcine)Breza -0.0250029347 0.5208487445 -0.04800421
## factor(naziv_Opcine)Brod 0.1702639176 0.4443095699 0.38321011
## factor(naziv_Opcine)Bugojno 0.5092488538 0.3617734496 1.40764574
## factor(naziv_Opcine)Busovaca 1.5139901829 0.4274176416 3.54217991
## factor(naziv_Opcine)Buzim 0.2525970322 0.5510536094 0.45838922
## factor(naziv_Opcine)C.Sarajevo 0.5672651907 0.3651561035 1.55348681
## factor(naziv_Opcine)Capljina 1.4569534717 0.4468256121 3.26067583
## factor(naziv_Opcine)Cazin 0.4934371883 0.3576541856 1.37964886
## factor(naziv_Opcine)Celic 0.2538265923 0.5516447521 0.46012690
## factor(naziv_Opcine)Celinac 0.8521213104 0.5514200728 1.54532153
## factor(naziv_Opcine)Citluk 1.6963959089 0.4395818344 3.85911286
## factor(naziv_Opcine)D.Vakuf 0.7265215510 0.3982888421 1.82410722
## factor(naziv_Opcine)Derventa 0.3851237853 0.4155416262 0.92679953
## factor(naziv_Opcine)Doboj 0.7510946953 0.3628484125 2.06999581
## factor(naziv_Opcine)Doboj-Istok 1.0621163751 0.5340125484 1.98893524
## factor(naziv_Opcine)Drvar -0.1032950543 0.5216447472 -0.19801801
## factor(naziv_Opcine)Foca - RS 0.4941934067 0.4494445671 1.09956476
## factor(naziv_Opcine)Fojnica 1.3066618686 0.4869032229 2.68361721
## factor(naziv_Opcine)G.Vakuf 0.9023134502 0.4277386535 2.10949710
## factor(naziv_Opcine)Gacko -0.1344265832 0.4621885348 -0.29084794
## factor(naziv_Opcine)Glamoc -0.4467542383 0.5729997858 -0.77967610
## factor(naziv_Opcine)Gorazde 0.2938518665 0.4063191813 0.72320452
## factor(naziv_Opcine)Gracanica 0.8304466325 0.3446174155 2.40976397
## factor(naziv_Opcine)Grad Mostar 0.8545848334 0.3210954805 2.66146640
## factor(naziv_Opcine)Gradacac 1.1328374547 0.3947146916 2.87001594
## factor(naziv_Opcine)Gradiska 1.2253599075 0.4101428745 2.98764158
## factor(naziv_Opcine)Grude 0.7754122927 0.4549501344 1.70438963
## factor(naziv_Opcine)I.Ilidza -0.1016629699 0.5219791572 -0.19476442
## factor(naziv_Opcine)Ilidza 0.7202100851 0.3698139285 1.94749313
## factor(naziv_Opcine)Ilijas 0.0054865413 0.4964606115 0.01105131
## factor(naziv_Opcine)Jajce 0.7977865588 0.4493667621 1.77535729
## factor(naziv_Opcine)K.Dubica 0.7056374870 0.3902431884 1.80819937
## factor(naziv_Opcine)Kakanj 0.3589537739 0.3475932146 1.03268349
## factor(naziv_Opcine)Kalesija 1.0474252387 0.4171121978 2.51113548
## factor(naziv_Opcine)Kiseljak 1.1095209841 0.3928253252 2.82446399
## factor(naziv_Opcine)Kladanj 0.7145528726 0.5205398938 1.37271491
## factor(naziv_Opcine)Kljuc 0.3275010481 0.4490867433 0.72926011
## factor(naziv_Opcine)Knezevo 0.3404023897 0.5080745995 0.66998506
## factor(naziv_Opcine)Konjic 0.3591983253 0.3729116288 0.96322640
## factor(naziv_Opcine)Kotor Varos 0.4801894529 0.4859631313 0.98811910
## factor(naziv_Opcine)Kresevo 1.0856281566 0.4987257789 2.17680377
## factor(naziv_Opcine)Laktasi 0.8648152611 0.3806588468 2.27189061
## factor(naziv_Opcine)Livno 0.3526909779 0.4424477868 0.79713582
## factor(naziv_Opcine)Lopare 0.7584297919 0.4197502233 1.80685977
## factor(naziv_Opcine)Lukavac 1.0099876921 0.3766167363 2.68173874
## factor(naziv_Opcine)Ljubuski 1.5881068685 0.4261134513 3.72695784
## factor(naziv_Opcine)Maglaj 0.0543513736 0.3836179484 0.14168100
## factor(naziv_Opcine)Modrica 0.4306975289 0.3971365770 1.08450733
## factor(naziv_Opcine)N.G.Sarajevo 0.8521916113 0.3224844007 2.64258243
## factor(naziv_Opcine)N.Gorazde 1.4668141927 0.5719794668 2.56445253
## factor(naziv_Opcine)N.Sarajevo 0.2987891980 0.3716898933 0.80386689
## factor(naziv_Opcine)N.Travnik 0.5789122314 0.3702453920 1.56359065
## factor(naziv_Opcine)Nevesinje 0.3347483635 0.5534635475 0.60482459
## factor(naziv_Opcine)Novi Grad -0.0477252432 0.3722467863 -0.12820861
## factor(naziv_Opcine)Orasje 1.1081282124 0.4293638663 2.58086043
## factor(naziv_Opcine)Pale -0.3578066802 0.4559481025 -0.78475309
## factor(naziv_Opcine)Posusje 0.7400731235 0.5200333058 1.42312639
## factor(naziv_Opcine)Prijedor 0.6675457752 0.3435768383 1.94293008
## factor(naziv_Opcine)Prnjavor 0.8499689929 0.3914053696 2.17158235
## factor(naziv_Opcine)Prozor 0.5571051515 0.5552704132 1.00330422
## factor(naziv_Opcine)Ribnik 0.2680127055 0.5098591770 0.52566026
## factor(naziv_Opcine)Rogatica -0.3667611975 0.5085011443 -0.72125934
## factor(naziv_Opcine)Rudo 0.5131764943 0.4872561919 1.05319646
## factor(naziv_Opcine)S.Brijeg 1.2726688650 0.3925248174 3.24226344
## factor(naziv_Opcine)S.G.Sarajevo 0.6968394580 0.3935049404 1.77085314
## factor(naziv_Opcine)Samac 0.5303376394 0.3676873259 1.44236040
## factor(naziv_Opcine)Sanski Most 0.6261048988 0.3471940049 1.80332866
## factor(naziv_Opcine)Sokolac -0.3603451732 0.5535478714 -0.65097382
## factor(naziv_Opcine)Srbac 0.6694379268 0.4617654401 1.44973588
## factor(naziv_Opcine)Srebrenik 0.0478549684 0.3507034179 0.13645424
## factor(naziv_Opcine)Stolac 1.3577008610 0.5516831763 2.46101552
## factor(naziv_Opcine)Tesanj 0.3373002234 0.3585179994 0.94081810
## factor(naziv_Opcine)Teslic 0.0425346864 0.4097787616 0.10379915
## factor(naziv_Opcine)Tomislavgrad 1.3567828594 0.4633198948 2.92839326
## factor(naziv_Opcine)Travnik 1.0287699728 0.3349071631 3.07180642
## factor(naziv_Opcine)Trebinje 0.0978233607 0.4014138408 0.24369703
## factor(naziv_Opcine)Tuzla 0.7319490237 0.3318877155 2.20541162
## factor(naziv_Opcine)Ugljevik 0.7206744182 0.4765402200 1.51230555
## factor(naziv_Opcine)V.Kladusa 0.9439790403 0.3710007186 2.54441297
## factor(naziv_Opcine)Visegrad -0.6144386362 0.5364254662 -1.14543152
## factor(naziv_Opcine)Visoko 0.7017305574 0.3683522453 1.90505302
## factor(naziv_Opcine)Vitez 1.0624149911 0.3747040935 2.83534397
## factor(naziv_Opcine)Vlasenica 0.3591126544 0.4865949550 0.73801146
## factor(naziv_Opcine)Vogosca 0.9660204371 0.4554037597 2.12123949
## factor(naziv_Opcine)Zavidovici -0.1761700104 0.3632730408 -0.48495206
## factor(naziv_Opcine)Zenica -0.0644010467 0.3214623901 -0.20033773
## factor(naziv_Opcine)Zepce 0.3224940400 0.5740909090 0.56174734
## factor(naziv_Opcine)Zivinice 1.0577272398 0.3445152114 3.07019024
## factor(naziv_Opcine)Zvornik 0.8324091214 0.3632602227 2.29149538
## Pr(>|t|)
## (Intercept) 1.652422e-02
## factor(Sex_1)2 2.295239e-03
## factor(Educ.1)no elementary 6.616885e-01
## factor(Educ.1)elementary 9.337931e-01
## factor(Educ.1)high schl 3.786562e-02
## factor(Educ.1)college 6.801028e-03
## factor(Educ.1)university 7.199281e-04
## factor(Activ.1)0 1.969072e-03
## n.employtwo 2.554771e-08
## n.employthree and more 2.975046e-04
## factor(ncomp1)two 1.148152e-07
## factor(ncomp1)three 3.382240e-16
## factor(ncomp1)four 1.605415e-26
## factor(ncomp1)five 6.971287e-24
## factor(ncomp1)six 1.633584e-23
## factor(ncomp1)seven 1.574242e-17
## factor(ncomp1)eight 2.754446e-03
## factor(ncomp1)nine 1.191462e-03
## factor(ncomp1)ten 1.090383e-03
## factor(ncomp1)11+ 2.078537e-03
## as.numeric(Age_1) 2.731914e-01
## as.numeric(Age_1^2) 4.074820e-01
## factor(naziv_Opcine)B.Petrovac 4.935276e-01
## factor(naziv_Opcine)Banovici 1.791617e-02
## factor(naziv_Opcine)Banja Luka 8.604801e-03
## factor(naziv_Opcine)Bihac 4.291397e-02
## factor(naziv_Opcine)Bijeljina 7.552841e-01
## factor(naziv_Opcine)Bratunac 3.404365e-01
## factor(naziv_Opcine)Brcko 7.080655e-02
## factor(naziv_Opcine)Breza 9.617153e-01
## factor(naziv_Opcine)Brod 7.015845e-01
## factor(naziv_Opcine)Bugojno 1.593141e-01
## factor(naziv_Opcine)Busovaca 4.013885e-04
## factor(naziv_Opcine)Buzim 6.466979e-01
## factor(naziv_Opcine)C.Sarajevo 1.203864e-01
## factor(naziv_Opcine)Capljina 1.120836e-03
## factor(naziv_Opcine)Cazin 1.677723e-01
## factor(naziv_Opcine)Celic 6.454503e-01
## factor(naziv_Opcine)Celinac 1.223481e-01
## factor(naziv_Opcine)Citluk 1.156038e-04
## factor(naziv_Opcine)D.Vakuf 6.821085e-02
## factor(naziv_Opcine)Derventa 3.540869e-01
## factor(naziv_Opcine)Doboj 3.851713e-02
## factor(naziv_Opcine)Doboj-Istok 4.677674e-02
## factor(naziv_Opcine)Drvar 8.430411e-01
## factor(naziv_Opcine)Foca - RS 2.715883e-01
## factor(naziv_Opcine)Fojnica 7.313201e-03
## factor(naziv_Opcine)G.Vakuf 3.496401e-02
## factor(naziv_Opcine)Gacko 7.711828e-01
## factor(naziv_Opcine)Glamoc 4.356279e-01
## factor(naziv_Opcine)Gorazde 4.695968e-01
## factor(naziv_Opcine)Gracanica 1.600797e-02
## factor(naziv_Opcine)Grad Mostar 7.811383e-03
## factor(naziv_Opcine)Gradacac 4.126166e-03
## factor(naziv_Opcine)Gradiska 2.828560e-03
## factor(naziv_Opcine)Grude 8.838641e-02
## factor(naziv_Opcine)I.Ilidza 8.455873e-01
## factor(naziv_Opcine)Ilidza 5.154591e-02
## factor(naziv_Opcine)Ilijas 9.911831e-01
## factor(naziv_Opcine)Jajce 7.591535e-02
## factor(naziv_Opcine)K.Dubica 7.065096e-02
## factor(naziv_Opcine)Kakanj 3.018148e-01
## factor(naziv_Opcine)Kalesija 1.207368e-02
## factor(naziv_Opcine)Kiseljak 4.759556e-03
## factor(naziv_Opcine)Kladanj 1.699184e-01
## factor(naziv_Opcine)Kljuc 4.658855e-01
## factor(naziv_Opcine)Knezevo 5.029062e-01
## factor(naziv_Opcine)Konjic 3.354925e-01
## factor(naziv_Opcine)Kotor Varos 3.231544e-01
## factor(naziv_Opcine)Kresevo 2.955382e-02
## factor(naziv_Opcine)Laktasi 2.314628e-02
## factor(naziv_Opcine)Livno 4.254197e-01
## factor(naziv_Opcine)Lopare 7.085968e-02
## factor(naziv_Opcine)Lukavac 7.354314e-03
## factor(naziv_Opcine)Ljubuski 1.964981e-04
## factor(naziv_Opcine)Maglaj 8.873392e-01
## factor(naziv_Opcine)Modrica 2.782056e-01
## factor(naziv_Opcine)N.G.Sarajevo 8.259884e-03
## factor(naziv_Opcine)N.Gorazde 1.037026e-02
## factor(naziv_Opcine)N.Sarajevo 4.215219e-01
## factor(naziv_Opcine)N.Travnik 1.179933e-01
## factor(naziv_Opcine)Nevesinje 5.453300e-01
## factor(naziv_Opcine)Novi Grad 8.979904e-01
## factor(naziv_Opcine)Orasje 9.890939e-03
## factor(naziv_Opcine)Pale 4.326451e-01
## factor(naziv_Opcine)Posusje 1.547779e-01
## factor(naziv_Opcine)Prijedor 5.209507e-02
## factor(naziv_Opcine)Prnjavor 2.994608e-02
## factor(naziv_Opcine)Prozor 3.157751e-01
## factor(naziv_Opcine)Ribnik 5.991536e-01
## factor(naziv_Opcine)Rogatica 4.707924e-01
## factor(naziv_Opcine)Rudo 2.923149e-01
## factor(naziv_Opcine)S.Brijeg 1.195629e-03
## factor(naziv_Opcine)S.G.Sarajevo 7.666169e-02
## factor(naziv_Opcine)Samac 1.492795e-01
## factor(naziv_Opcine)Sanski Most 7.141225e-02
## factor(naziv_Opcine)Sokolac 5.151010e-01
## factor(naziv_Opcine)Srbac 1.472110e-01
## factor(naziv_Opcine)Srebrenik 8.914691e-01
## factor(naziv_Opcine)Stolac 1.389661e-02
## factor(naziv_Opcine)Tesanj 3.468552e-01
## factor(naziv_Opcine)Teslic 9.173340e-01
## factor(naziv_Opcine)Tomislavgrad 3.426530e-03
## factor(naziv_Opcine)Travnik 2.142063e-03
## factor(naziv_Opcine)Trebinje 8.074780e-01
## factor(naziv_Opcine)Tuzla 2.748222e-02
## factor(naziv_Opcine)Ugljevik 1.305356e-01
## factor(naziv_Opcine)V.Kladusa 1.098364e-02
## factor(naziv_Opcine)Visegrad 2.520994e-01
## factor(naziv_Opcine)Visoko 5.684522e-02
## factor(naziv_Opcine)Vitez 4.600747e-03
## factor(naziv_Opcine)Vlasenica 4.605510e-01
## factor(naziv_Opcine)Vogosca 3.396333e-02
## factor(naziv_Opcine)Zavidovici 6.277371e-01
## factor(naziv_Opcine)Zenica 8.412267e-01
## factor(naziv_Opcine)Zepce 5.743198e-01
## factor(naziv_Opcine)Zivinice 2.153661e-03
## factor(naziv_Opcine)Zvornik 2.198680e-02#Ovdje je B.Krupa referentna opstina, B.Grahovo sam brisala, provjeri koja ostina je abecedno po redusort(unique(hbs.transport.drop.1$naziv_Opcine)) #B.Krupa je referentna## [1] "B.Krupa" "B.Petrovac" "Banovici" "Banja Luka" "Bihac"
## [6] "Bijeljina" "Bratunac" "Brcko" "Breza" "Brod"
## [11] "Bugojno" "Busovaca" "Buzim" "C.Sarajevo" "Capljina"
## [16] "Cazin" "Celic" "Celinac" "Citluk" "D.Vakuf"
## [21] "Derventa" "Doboj" "Doboj-Istok" "Drvar" "Foca - RS"
## [26] "Fojnica" "G.Vakuf" "Gacko" "Glamoc" "Gorazde"
## [31] "Gracanica" "Grad Mostar" "Gradacac" "Gradiska" "Grude"
## [36] "I.Ilidza" "Ilidza" "Ilijas" "Jajce" "K.Dubica"
## [41] "Kakanj" "Kalesija" "Kiseljak" "Kladanj" "Kljuc"
## [46] "Knezevo" "Konjic" "Kotor Varos" "Kresevo" "Laktasi"
## [51] "Livno" "Lopare" "Lukavac" "Ljubuski" "Maglaj"
## [56] "Modrica" "N.G.Sarajevo" "N.Gorazde" "N.Sarajevo" "N.Travnik"
## [61] "Nevesinje" "Novi Grad" "Orasje" "Pale" "Posusje"
## [66] "Prijedor" "Prnjavor" "Prozor" "Ribnik" "Rogatica"
## [71] "Rudo" "S.Brijeg" "S.G.Sarajevo" "Samac" "Sanski Most"
## [76] "Sokolac" "Srbac" "Srebrenik" "Stolac" "Tesanj"
## [81] "Teslic" "Tomislavgrad" "Travnik" "Trebinje" "Tuzla"
## [86] "Ugljevik" "V.Kladusa" "Visegrad" "Visoko" "Vitez"
## [91] "Vlasenica" "Vogosca" "Zavidovici" "Zenica" "Zepce"
## [96] "Zivinice" "Zvornik"model.coef.empl[,c("Estimate","Pr(>|t|)")]## Estimate Pr(>|t|)
## (Intercept) 1.1505558085 1.652422e-02
## factor(Sex_1)2 -0.2260883477 2.295239e-03
## factor(Educ.1)no elementary -0.0900952185 6.616885e-01
## factor(Educ.1)elementary -0.0150238957 9.337931e-01
## factor(Educ.1)high schl 0.3778419261 3.786562e-02
## factor(Educ.1)college 0.5880372575 6.801028e-03
## factor(Educ.1)university 0.6674755019 7.199281e-04
## factor(Activ.1)0 -0.1899942460 1.969072e-03
## n.employtwo 0.3142004870 2.554771e-08
## n.employthree and more 0.3703288824 2.975046e-04
## factor(ncomp1)two 0.6677930837 1.148152e-07
## factor(ncomp1)three 1.0225816751 3.382240e-16
## factor(ncomp1)four 1.3282648552 1.605415e-26
## factor(ncomp1)five 1.3552052197 6.971287e-24
## factor(ncomp1)six 1.5426338865 1.633584e-23
## factor(ncomp1)seven 1.8124581905 1.574242e-17
## factor(ncomp1)eight 0.9510853967 2.754446e-03
## factor(ncomp1)nine 1.7413763913 1.191462e-03
## factor(ncomp1)ten 2.6003379273 1.090383e-03
## factor(ncomp1)11+ 2.1920015695 2.078537e-03
## as.numeric(Age_1) 0.0154416088 2.731914e-01
## as.numeric(Age_1^2) -0.0001122846 4.074820e-01
## factor(naziv_Opcine)B.Petrovac -0.3776747521 4.935276e-01
## factor(naziv_Opcine)Banovici 1.0374490107 1.791617e-02
## factor(naziv_Opcine)Banja Luka 0.8319755743 8.604801e-03
## factor(naziv_Opcine)Bihac 0.7092532952 4.291397e-02
## factor(naziv_Opcine)Bijeljina 0.0995604141 7.552841e-01
## factor(naziv_Opcine)Bratunac 0.5256885481 3.404365e-01
## factor(naziv_Opcine)Brcko 0.5648748698 7.080655e-02
## factor(naziv_Opcine)Breza -0.0250029347 9.617153e-01
## factor(naziv_Opcine)Brod 0.1702639176 7.015845e-01
## factor(naziv_Opcine)Bugojno 0.5092488538 1.593141e-01
## factor(naziv_Opcine)Busovaca 1.5139901829 4.013885e-04
## factor(naziv_Opcine)Buzim 0.2525970322 6.466979e-01
## factor(naziv_Opcine)C.Sarajevo 0.5672651907 1.203864e-01
## factor(naziv_Opcine)Capljina 1.4569534717 1.120836e-03
## factor(naziv_Opcine)Cazin 0.4934371883 1.677723e-01
## factor(naziv_Opcine)Celic 0.2538265923 6.454503e-01
## factor(naziv_Opcine)Celinac 0.8521213104 1.223481e-01
## factor(naziv_Opcine)Citluk 1.6963959089 1.156038e-04
## factor(naziv_Opcine)D.Vakuf 0.7265215510 6.821085e-02
## factor(naziv_Opcine)Derventa 0.3851237853 3.540869e-01
## factor(naziv_Opcine)Doboj 0.7510946953 3.851713e-02
## factor(naziv_Opcine)Doboj-Istok 1.0621163751 4.677674e-02
## factor(naziv_Opcine)Drvar -0.1032950543 8.430411e-01
## factor(naziv_Opcine)Foca - RS 0.4941934067 2.715883e-01
## factor(naziv_Opcine)Fojnica 1.3066618686 7.313201e-03
## factor(naziv_Opcine)G.Vakuf 0.9023134502 3.496401e-02
## factor(naziv_Opcine)Gacko -0.1344265832 7.711828e-01
## factor(naziv_Opcine)Glamoc -0.4467542383 4.356279e-01
## factor(naziv_Opcine)Gorazde 0.2938518665 4.695968e-01
## factor(naziv_Opcine)Gracanica 0.8304466325 1.600797e-02
## factor(naziv_Opcine)Grad Mostar 0.8545848334 7.811383e-03
## factor(naziv_Opcine)Gradacac 1.1328374547 4.126166e-03
## factor(naziv_Opcine)Gradiska 1.2253599075 2.828560e-03
## factor(naziv_Opcine)Grude 0.7754122927 8.838641e-02
## factor(naziv_Opcine)I.Ilidza -0.1016629699 8.455873e-01
## factor(naziv_Opcine)Ilidza 0.7202100851 5.154591e-02
## factor(naziv_Opcine)Ilijas 0.0054865413 9.911831e-01
## factor(naziv_Opcine)Jajce 0.7977865588 7.591535e-02
## factor(naziv_Opcine)K.Dubica 0.7056374870 7.065096e-02
## factor(naziv_Opcine)Kakanj 0.3589537739 3.018148e-01
## factor(naziv_Opcine)Kalesija 1.0474252387 1.207368e-02
## factor(naziv_Opcine)Kiseljak 1.1095209841 4.759556e-03
## factor(naziv_Opcine)Kladanj 0.7145528726 1.699184e-01
## factor(naziv_Opcine)Kljuc 0.3275010481 4.658855e-01
## factor(naziv_Opcine)Knezevo 0.3404023897 5.029062e-01
## factor(naziv_Opcine)Konjic 0.3591983253 3.354925e-01
## factor(naziv_Opcine)Kotor Varos 0.4801894529 3.231544e-01
## factor(naziv_Opcine)Kresevo 1.0856281566 2.955382e-02
## factor(naziv_Opcine)Laktasi 0.8648152611 2.314628e-02
## factor(naziv_Opcine)Livno 0.3526909779 4.254197e-01
## factor(naziv_Opcine)Lopare 0.7584297919 7.085968e-02
## factor(naziv_Opcine)Lukavac 1.0099876921 7.354314e-03
## factor(naziv_Opcine)Ljubuski 1.5881068685 1.964981e-04
## factor(naziv_Opcine)Maglaj 0.0543513736 8.873392e-01
## factor(naziv_Opcine)Modrica 0.4306975289 2.782056e-01
## factor(naziv_Opcine)N.G.Sarajevo 0.8521916113 8.259884e-03
## factor(naziv_Opcine)N.Gorazde 1.4668141927 1.037026e-02
## factor(naziv_Opcine)N.Sarajevo 0.2987891980 4.215219e-01
## factor(naziv_Opcine)N.Travnik 0.5789122314 1.179933e-01
## factor(naziv_Opcine)Nevesinje 0.3347483635 5.453300e-01
## factor(naziv_Opcine)Novi Grad -0.0477252432 8.979904e-01
## factor(naziv_Opcine)Orasje 1.1081282124 9.890939e-03
## factor(naziv_Opcine)Pale -0.3578066802 4.326451e-01
## factor(naziv_Opcine)Posusje 0.7400731235 1.547779e-01
## factor(naziv_Opcine)Prijedor 0.6675457752 5.209507e-02
## factor(naziv_Opcine)Prnjavor 0.8499689929 2.994608e-02
## factor(naziv_Opcine)Prozor 0.5571051515 3.157751e-01
## factor(naziv_Opcine)Ribnik 0.2680127055 5.991536e-01
## factor(naziv_Opcine)Rogatica -0.3667611975 4.707924e-01
## factor(naziv_Opcine)Rudo 0.5131764943 2.923149e-01
## factor(naziv_Opcine)S.Brijeg 1.2726688650 1.195629e-03
## factor(naziv_Opcine)S.G.Sarajevo 0.6968394580 7.666169e-02
## factor(naziv_Opcine)Samac 0.5303376394 1.492795e-01
## factor(naziv_Opcine)Sanski Most 0.6261048988 7.141225e-02
## factor(naziv_Opcine)Sokolac -0.3603451732 5.151010e-01
## factor(naziv_Opcine)Srbac 0.6694379268 1.472110e-01
## factor(naziv_Opcine)Srebrenik 0.0478549684 8.914691e-01
## factor(naziv_Opcine)Stolac 1.3577008610 1.389661e-02
## factor(naziv_Opcine)Tesanj 0.3373002234 3.468552e-01
## factor(naziv_Opcine)Teslic 0.0425346864 9.173340e-01
## factor(naziv_Opcine)Tomislavgrad 1.3567828594 3.426530e-03
## factor(naziv_Opcine)Travnik 1.0287699728 2.142063e-03
## factor(naziv_Opcine)Trebinje 0.0978233607 8.074780e-01
## factor(naziv_Opcine)Tuzla 0.7319490237 2.748222e-02
## factor(naziv_Opcine)Ugljevik 0.7206744182 1.305356e-01
## factor(naziv_Opcine)V.Kladusa 0.9439790403 1.098364e-02
## factor(naziv_Opcine)Visegrad -0.6144386362 2.520994e-01
## factor(naziv_Opcine)Visoko 0.7017305574 5.684522e-02
## factor(naziv_Opcine)Vitez 1.0624149911 4.600747e-03
## factor(naziv_Opcine)Vlasenica 0.3591126544 4.605510e-01
## factor(naziv_Opcine)Vogosca 0.9660204371 3.396333e-02
## factor(naziv_Opcine)Zavidovici -0.1761700104 6.277371e-01
## factor(naziv_Opcine)Zenica -0.0644010467 8.412267e-01
## factor(naziv_Opcine)Zepce 0.3224940400 5.743198e-01
## factor(naziv_Opcine)Zivinice 1.0577272398 2.153661e-03
## factor(naziv_Opcine)Zvornik 0.8324091214 2.198680e-02model.coef.empl <- model.coef.empl #provjeri koliko je drugih unosa
model.coef.empl <- as.data.frame.array(model.coef.empl[-c(1:22),]) # da bude dataframe a ne matrix i da mi brise prvih 20 redova koji su drugi koeficijenti.24.11.2024. OVO SU TRAZENI KOEFICIJENTI
Munic <- rownames(model.coef.empl)
model.coef.empl$Munic <- Municdim(model.coef.empl) #da vidim koliko redova ## [1] 96 5rownames(model.coef.empl) <- 1:96 #nedostaje B.Krupa koje je 0
head(model.coef.empl)## Estimate Std. Error t value Pr(>|t|) Munic
## 1 -0.37767475 0.5515335 -0.6847721 0.493527646 factor(naziv_Opcine)B.Petrovac
## 2 1.03744901 0.4380510 2.3683291 0.017916166 factor(naziv_Opcine)Banovici
## 3 0.83197557 0.3165001 2.6286744 0.008604801 factor(naziv_Opcine)Banja Luka
## 4 0.70925330 0.3502148 2.0251953 0.042913971 factor(naziv_Opcine)Bihac
## 5 0.09956041 0.3194100 0.3117010 0.755284137 factor(naziv_Opcine)Bijeljina
## 6 0.52568855 0.5513729 0.9534175 0.340436524 factor(naziv_Opcine)BratunacMake a new variable with municipal names without
factor(Munic)
Munic1 <- sapply(strsplit(Munic, split=')', fixed=TRUE), function(x) (x[2]))
Munic1## [1] "B.Petrovac" "Banovici" "Banja Luka" "Bihac" "Bijeljina"
## [6] "Bratunac" "Brcko" "Breza" "Brod" "Bugojno"
## [11] "Busovaca" "Buzim" "C.Sarajevo" "Capljina" "Cazin"
## [16] "Celic" "Celinac" "Citluk" "D.Vakuf" "Derventa"
## [21] "Doboj" "Doboj-Istok" "Drvar" "Foca - RS" "Fojnica"
## [26] "G.Vakuf" "Gacko" "Glamoc" "Gorazde" "Gracanica"
## [31] "Grad Mostar" "Gradacac" "Gradiska" "Grude" "I.Ilidza"
## [36] "Ilidza" "Ilijas" "Jajce" "K.Dubica" "Kakanj"
## [41] "Kalesija" "Kiseljak" "Kladanj" "Kljuc" "Knezevo"
## [46] "Konjic" "Kotor Varos" "Kresevo" "Laktasi" "Livno"
## [51] "Lopare" "Lukavac" "Ljubuski" "Maglaj" "Modrica"
## [56] "N.G.Sarajevo" "N.Gorazde" "N.Sarajevo" "N.Travnik" "Nevesinje"
## [61] "Novi Grad" "Orasje" "Pale" "Posusje" "Prijedor"
## [66] "Prnjavor" "Prozor" "Ribnik" "Rogatica" "Rudo"
## [71] "S.Brijeg" "S.G.Sarajevo" "Samac" "Sanski Most" "Sokolac"
## [76] "Srbac" "Srebrenik" "Stolac" "Tesanj" "Teslic"
## [81] "Tomislavgrad" "Travnik" "Trebinje" "Tuzla" "Ugljevik"
## [86] "V.Kladusa" "Visegrad" "Visoko" "Vitez" "Vlasenica"
## [91] "Vogosca" "Zavidovici" "Zenica" "Zepce" "Zivinice"
## [96] "Zvornik"model.coef.empl$Munic1 <- Munic1add B.Krupa to dataframe
model.coef.empl[nrow(model.coef.empl) + 1,] = c(0, NA, NA, NA,"B.Krupa","B.Krupa" )
model.coef.empl$Estimate <- as.numeric(model.coef.empl$Estimate)top10.m.empl <- model.coef.empl %>%
dplyr::select(Estimate, Munic1) %>%
arrange (desc(Estimate))%>%
head(10) #top ten municipalities
top10.m.empl## Estimate Munic1
## 1 1.696396 Citluk
## 2 1.588107 Ljubuski
## 3 1.513990 Busovaca
## 4 1.466814 N.Gorazde
## 5 1.456953 Capljina
## 6 1.357701 Stolac
## 7 1.356783 Tomislavgrad
## 8 1.306662 Fojnica
## 9 1.272669 S.Brijeg
## 10 1.225360 GradiskaExtracting the coefficients from the second best model p2 the compareble model
Spasiti u excelu koef za winning model
library(openxlsx)
wb = createWorkbook()
addWorksheet(wb, "estimates.WINNING.MODEL_p2.age") #ovo je naziv sheeta
writeData(wb,"estimates.WINNING.MODEL_p2.age", model.coef.empl)
saveWorkbook(wb, "estimates.transport.hbs.cost.empl.24.11.2024.xlsx", overwrite = T)## Warning in file.create(to[okay]): cannot create file
## 'estimates.transport.hbs.cost.empl.24.11.2024.xlsx', reason 'Permission denied'Dummy variables.. summary and sd
# Load necessary libraries
library(fastDummies)
library(dplyr)#ncomp1
# Step 1: Create dummy variables for ncomp1
hbs.transport.drop.2 <- hbs.transport.drop.1 %>%
mutate(ncomp1 = as.factor(ncomp1)) %>%
dummy_cols(select_columns = "ncomp1", remove_selected_columns = TRUE)
# Step 2: Get the names of the dummy variables
dummy_cols <- grep("^ncomp1_", names(hbs.transport.drop.2), value = TRUE)
# Step 3: Calculate descriptive statistics for each dummy variable
stats_list <- lapply(dummy_cols, function(dummy_col) {
data.frame(
Dummy = dummy_col,
Min = min(hbs.transport.drop.2[[dummy_col]], na.rm = TRUE),
Max = max(hbs.transport.drop.2[[dummy_col]], na.rm = TRUE),
Mean = mean(hbs.transport.drop.2[[dummy_col]], na.rm = TRUE),
SD = sd(hbs.transport.drop.2[[dummy_col]], na.rm = TRUE)
)
})
# Step 4: Combine results into a single data frame
stats_df <- bind_rows(stats_list)
# View the results
head(stats_df,11)## Dummy Min Max Mean SD
## 1 ncomp1_one 0 1 0.0517578125 0.22156472
## 2 ncomp1_two 0 1 0.1645507812 0.37081989
## 3 ncomp1_three 0 1 0.2390136719 0.42653318
## 4 ncomp1_four 0 1 0.3044433594 0.46022746
## 5 ncomp1_five 0 1 0.1435546875 0.35068043
## 6 ncomp1_six 0 1 0.0651855469 0.24688311
## 7 ncomp1_seven 0 1 0.0200195312 0.14008405
## 8 ncomp1_eight 0 1 0.0070800781 0.08385504
## 9 ncomp1_nine 0 1 0.0021972656 0.04682919
## 10 ncomp1_ten 0 1 0.0009765625 0.03123855
## 11 ncomp1_11+ 0 1 0.0012207031 0.03492149level of edu
library(Hmisc)
describe(hbs.transport.drop.1$Educ.1)## hbs.transport.drop.1$Educ.1
## n missing distinct
## 4096 0 6
##
## Value no education no elementary elementary high schl
## Frequency 103 163 744 2531
## Proportion 0.025 0.040 0.182 0.618
##
## Value college university
## Frequency 157 398
## Proportion 0.038 0.097# Step 1: Create dummy variables for Educ.1
hbs.transport.drop.2 <- hbs.transport.drop.1 %>%
mutate(Educ.1 = as.factor(Educ.1)) %>%
dummy_cols(select_columns = "Educ.1", remove_selected_columns = TRUE)
# Step 2: Get the names of the dummy variables for Educ.1
educ_dummy_cols <- grep("^Educ.1_", names(hbs.transport.drop.2), value = TRUE)
# Step 3: Calculate descriptive statistics for each dummy variable
educ_stats_list <- lapply(educ_dummy_cols, function(dummy_col) {
data.frame(
Dummy = dummy_col,
Min = min(hbs.transport.drop.2[[dummy_col]], na.rm = TRUE),
Max = max(hbs.transport.drop.2[[dummy_col]], na.rm = TRUE),
Mean = mean(hbs.transport.drop.2[[dummy_col]], na.rm = TRUE),
SD = sd(hbs.transport.drop.2[[dummy_col]], na.rm = TRUE)
)
})
# Step 4: Combine results into a single data frame
educ_stats_df <- bind_rows(educ_stats_list)
# View the results
print(educ_stats_df)## Dummy Min Max Mean SD
## 1 Educ.1_no education 0 1 0.02514648 0.1565890
## 2 Educ.1_no elementary 0 1 0.03979492 0.1955009
## 3 Educ.1_elementary 0 1 0.18164062 0.3855951
## 4 Educ.1_high schl 0 1 0.61791992 0.4859553
## 5 Educ.1_college 0 1 0.03833008 0.1920153
## 6 Educ.1_university 0 1 0.09716797 0.2962225n.employ
describe(hbs.transport.drop.1$n.employ)## hbs.transport.drop.1$n.employ
## n missing distinct
## 4096 0 3
##
## Value one two three and more
## Frequency 2448 1341 307
## Proportion 0.598 0.327 0.075# Step 1: Create dummy variables for n.employ
hbs.transport.drop.1 <- hbs.transport.drop.1 %>%
mutate(n.employ = as.factor(n.employ)) %>%
dummy_cols(select_columns = "n.employ", remove_selected_columns = TRUE)
# Step 2: Get the names of the dummy variables for n.employ
n_employ_dummy_cols <- grep("^n.employ_", names(hbs.transport.drop.1), value = TRUE)
# Step 3: Calculate descriptive statistics for each dummy variable
n_employ_stats_list <- lapply(n_employ_dummy_cols, function(dummy_col) {
data.frame(
Dummy = dummy_col,
Min = min(hbs.transport.drop.1[[dummy_col]], na.rm = TRUE),
Max = max(hbs.transport.drop.1[[dummy_col]], na.rm = TRUE),
Mean = mean(hbs.transport.drop.1[[dummy_col]], na.rm = TRUE),
SD = sd(hbs.transport.drop.1[[dummy_col]], na.rm = TRUE)
)
})
# Step 4: Combine results into a single data frame
n_employ_stats_df <- bind_rows(n_employ_stats_list)
# View the results
print(n_employ_stats_df)## Dummy Min Max Mean SD
## 1 n.employ_one 0 1 0.59765625 0.4904304
## 2 n.employ_two 0 1 0.32739258 0.4693191
## 3 n.employ_three and more 0 1 0.07495117 0.2633447Activ.1
describe(hbs.transport.drop.1$Activ.1)## hbs.transport.drop.1$Activ.1
## n missing distinct
## 4096 0 2
##
## Value 1 0
## Frequency 2575 1521
## Proportion 0.629 0.371# Step 1: Create dummy variables for Activ.1
hbs.transport.drop.1 <- hbs.transport.drop.1 %>%
mutate(Activ.1 = as.factor(Activ.1)) %>%
dummy_cols(select_columns = "Activ.1", remove_selected_columns = TRUE)
# Step 2: Get the names of the dummy variables for Activ.1
activ_dummy_cols <- grep("^Activ.1_", names(hbs.transport.drop.1), value = TRUE)
# Step 3: Calculate descriptive statistics for each dummy variable
activ_stats_list <- lapply(activ_dummy_cols, function(dummy_col) {
data.frame(
Dummy = dummy_col,
Min = min(hbs.transport.drop.1[[dummy_col]], na.rm = TRUE),
Max = max(hbs.transport.drop.1[[dummy_col]], na.rm = TRUE),
Mean = mean(hbs.transport.drop.1[[dummy_col]], na.rm = TRUE),
SD = sd(hbs.transport.drop.1[[dummy_col]], na.rm = TRUE)
)
})
# Step 4: Combine results into a single data frame
activ_stats_df <- bind_rows(activ_stats_list)
# View the results
print(activ_stats_df)## Dummy Min Max Mean SD
## 1 Activ.1_1 0 1 0.6286621 0.4832216
## 2 Activ.1_0 0 1 0.3713379 0.4832216Sex_1
describe(hbs.transport.drop.1$Sex_1)## hbs.transport.drop.1$Sex_1 : Sex_1 Format:F1.0
## n missing distinct Info Mean
## 4096 0 2 0.387 1.152
##
## Value 1 2
## Frequency 3473 623
## Proportion 0.848 0.152# Step 1: Create dummy variables for Sex_1
hbs.transport.drop.1 <- hbs.transport.drop.1 %>%
mutate(Sex_1 = as.factor(Sex_1)) %>%
dummy_cols(select_columns = "Sex_1", remove_selected_columns = TRUE)
# Step 2: Get the names of the dummy variables for Sex_1
sex_dummy_cols <- grep("^Sex_1_", names(hbs.transport.drop.1), value = TRUE)
# Step 3: Calculate descriptive statistics for each dummy variable
sex_stats_list <- lapply(sex_dummy_cols, function(dummy_col) {
data.frame(
Dummy = dummy_col,
Min = min(hbs.transport.drop.1[[dummy_col]], na.rm = TRUE),
Max = max(hbs.transport.drop.1[[dummy_col]], na.rm = TRUE),
Mean = mean(hbs.transport.drop.1[[dummy_col]], na.rm = TRUE),
SD = sd(hbs.transport.drop.1[[dummy_col]], na.rm = TRUE)
)
})
# Step 4: Combine results into a single data frame
sex_stats_df <- bind_rows(sex_stats_list)
# View the results
print(sex_stats_df)## Dummy Min Max Mean SD
## 1 Sex_1_1 0 1 0.8479004 0.3591613
## 2 Sex_1_2 0 1 0.1520996 0.3591613summary (hbs.transport.drop.1$Age_1)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 21.00 43.75 52.00 51.62 59.00 95.00The age variables in the regression (as.numeric(Age_1)
and as.numeric(Age_1^2)) represent a quadratic relationship
between the age of the head of household and transport expenditure. In a
quadratic model, the relationship can be described as a parabola, with
the coefficients determining whether the parabola opens upwards
(minimum) or downwards (maximum).
Step-by-Step Interpretation:
Coefficients of Age Variables:
as.numeric(Age_1)(linear term): \(\beta_1 = 0.0154\)
as.numeric(Age_1^2)(quadratic term): \(\beta_2 = -0.0001123\)
The negative sign of the quadratic term (\(\beta_2\)) indicates that the parabola opens downwards, meaning the relationship has a peak (maximum transport expenditure) at a certain age.
Finding the Age at Maximum Expenditure: The age at which transport expenditure is highest can be calculated using the formula for the vertex of a parabola:
\[ \text{Age at maximum} = -\frac{\beta_1}{2 \cdot \beta_2} \]Substituting the values:
\[ \text{Age at maximum} = -\frac{0.0154}{2 \cdot (-0.0001123)} = \frac{0.0154}{0.0002246} \approx 68.6 \]
Interpretation:
The transport expenditure is highest when the head of household is approximately 69 years old.
Additional Note:
- While the regression includes age, neither
as.numeric(Age_1)noras.numeric(Age_1^2)is statistically significant (p-values: 0.273 and 0.407, respectively). This means the effect of age on transport expenditure may not be reliable in this model. However, the calculated peak age provides a descriptive insight.