Uganda Project Modeling
2022-04-04
#load data & packages
library(haven)
library(gtsummary)
library(MASS)## Warning: package 'MASS' was built under R version 4.1.3##
## Attaching package: 'MASS'## The following object is masked from 'package:gtsummary':
##
## selectlibrary(survey)## Loading required package: grid## Loading required package: Matrix## Loading required package: survival##
## Attaching package: 'survey'## The following object is masked from 'package:graphics':
##
## dotchartlibrary(dplyr)##
## Attaching package: 'dplyr'## The following object is masked from 'package:MASS':
##
## select## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionlibrary(survey)
library(dplyr)
library(svyVGAM)## Warning: package 'svyVGAM' was built under R version 4.1.3## Loading required package: VGAM## Loading required package: stats4## Loading required package: splines##
## Attaching package: 'VGAM'## The following object is masked from 'package:survey':
##
## calibrateuganda16 <- read_dta("C:/Users/rlutt/Downloads/UGIR7BFL.DTA")
uganda16<-zap_labels(uganda16)#recodes
#bank account
uganda16$bank<-car::Recode(uganda16$v170, recodes= "0= 'No'; 1= 'Yes'")
#financial decision-making
uganda16$decide_money <- ifelse(uganda16$v739 %in% c(1, 2), 1, 0)
#age groups
uganda16$agegroup <- car::Recode(uganda16$v013, recodes = "1='15-19'; 2='20-24';3='25-29'; 4 = '30-34';5='35-39';6='40-44';7='45+' ", as.factor=T)
#fertility preferences
uganda16$wantanotherchild<-ifelse(uganda16$v602!=9&uganda16$v602==1,1,0)
uganda16$chilborn<-ifelse(uganda16$v201>6, 6, uganda16$v201)
uganda16$extra_kid<- ifelse(uganda16$chilborn > uganda16$v614, 1,0)
#education level
uganda16$educationlevel <- car::recode(uganda16$v106,
recodes = "0 = 'none'; 1 = 'primary'; 2:3='secondary and above'; else='NA' ",
as.factor=T)
#internet
uganda16$internet<-as.factor(uganda16$v171a)
uganda16$internet<-car::Recode(uganda16$v171a, recodes= "0='never'; 1= 'in the past year'; 2='over a year ago'; 3= 'yes, but unsure when'; else='NA'", as.factor=T)
#contraception
uganda16$contraception<-as.factor(uganda16$v313)
uganda16$contraception<-car::Recode(uganda16$v313, recodes= "0='none'; 1='folkloric method'; 2='traditional method' ;3= 'modern method'; else='NA'", as.factor=T)
#usingmoderncontraception
uganda16$moderncon<-car::Recode(uganda16$v313, recodes= "0='0'; 1='1'; 2='1'; 3='2'; else='NA'", as.factor=F)
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## [14509] 0 2 2 2 1 0 2 2 0 2 0 0 0 2 2 2 2 0 0 0 0 0 0 0 2 2 2 0 2 0 2 0 2 0 2 2
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## [14977] 2 2 0 2 2 2 2 0 0 0 0 2 1 0 0 2 0 2 0 2 0 2 0 2 2 2 2 0 2 2 0 0 0 0 0 0
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## [15121] 2 0 2 2 2 0 2 2 1 0 0 2 2 2 2 2 0 0 0 0 2 2 0 2 2 0 2 1 0 0 0 2 0 2 0 0
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## [15445] 1 2 0 2 2 2 2 2 0 0 0 0 0 2 0 2 2 2 2 0 0 0 0 0 2 2 2 0 0 0 2 0 0 2 2 1
## [15481] 0 2 0 0 0 0 2 0 0 0 2 0 1 0 1 0 2 2 0 0 0 0 0 0 0 0 0 0 0 0 2 0 0 0 0 0
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## [15697] 0 2 2 2 0 0 2 0 0 0 1 0 0 0 0 0 0 0 0 2 0 0 2 2 0 2 0 0 2 0 0 0 0 2 2 0
## [15733] 2 0 0 2 0 2 0 0 0 0 2 0 0 0 2 2 0 0 2 0 0 0 0 2 2 2 0 0 0 2 1 0 0 0 0 0
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## [15805] 0 0 0 2 0 2 0 0 0 0 0 0 1 2 0 2 2 0 0 2 0 2 0 0 0 0 2 0 0 2 0 0 2 2 0 2
## [15841] 2 0 2 0 2 2 0 2 0 0 2 0 0 0 2 0 0 2 2 0 0 0 2 2 0 0 0 2 0 1 0 0 0 2 0 0
## [15877] 0 0 0 2 0 0 2 0 0 0 0 0 1 0 2 0 0 0 0 0 0 0 2 2 0 0 2 0 2 0 1 0 2 0 2 2
## [15913] 2 0 2 2 2 2 0 0 0 0 0 0 0 1 0 2 2 0 2 0 0 2 2 0 2 0 2 2 0 0 0 0 2 0 2 2
## [15949] 0 0 1 0 0 2 2 0 0 0 0 0 2 0 0 0 0 0 2 2 2 0 2 1 1 0 0 2 0 0 0 0 0 2 0 0
## [15985] 0 0 0 2 0 2 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 2 2 0 0 0 0 0 0 2 2 0
## [16021] 0 0 0 2 0 0 0 0 2 2 0 2 2 2 0 2 0 0 0 1 0 2 2 0 0 0 2 0 2 2 2 0 0 0 2 0
## [16057] 0 0 0 0 0 0 0 0 0 0 2 0 0 0 2 0 2 0 2 0 0 0 2 0 0 2 1 0 0 0 0 0 1 0 0 0
## [16093] 0 0 2 0 0 2 0 0 0 0 2 0 2 0 0 0 0 0 0 0 0 2 0 0 2 0 0 0 0 0 0 0 0 0 0 2
## [16129] 0 2 0 0 0 2 2 0 1 0 1 0 2 0 2 0 0 1 0 0 2 0 0 2 0 1 0 0 0 2 0 0 0 0 2 0
## [16165] 0 0 0 0 0 0 0 0 0 0 0 2 0 2 2 0 2 0 2 1 2 0 2 0 0 2 0 2 0 0 0 0 0 1 2 2
## [16201] 0 0 0 2 0 0 2 2 0 0 0 0 2 1 0 0 0 2 2 2 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0
## [16237] 2 1 0 0 2 0 2 2 2 0 2 2 0 2 0 0 2 2 2 0 1 0 2 2 2 0 0 2 2 0 0 2 0 1 2 0
## [16273] 0 0 0 0 0 0 1 2 0 0 2 2 0 0 2 0 0 0 0 2 0 0 0 2 2 2 0 0 2 0 1 2 0 0 0 0
## [16309] 2 2 0 0 0 0 0 0 0 0 2 2 0 0 0 0 0 0 2 0 0 0 0 0 0 2 0 0 2 2 0 2 1 0 0 2
## [16345] 0 2 0 0 0 2 0 0 1 0 2 0 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0 2 0 0 0 1 0
## [16381] 0 0 2 0 0 2 2 2 0 2 0 0 2 0 0 0 2 0 0 0 2 0 0 0 2 2 1 2 0 0 1 0 0 0 2 0
## [16417] 0 2 0 1 2 0 0 0 0 0 0 0 2 0 0 0 0 0 0 2 0 0 0 0 0 0 2 0 0 2 0 1 0 0 0 0
## [16453] 2 0 0 1 2 0 2 2 0 0 0 2 0 1 0 0 0 0 2 2 0 0 0 0 0 2 0 0 0 0 0 0 2 2 0 2
## [16489] 2 0 0 0 0 0 2 1 2 0 2 0 2 0 0 0 2 0 0 1 0 2 0 0 2 0 0 0 0 0 0 0 0 2 1 0
## [16525] 0 1 0 0 2 0 0 0 2 2 0 2 0 2 0 0 2 0 2 0 2 0 2 0 0 0 0 0 0 2 2 0 0 2 0 0
## [16561] 0 0 2 2 2 0 0 0 2 0 0 2 0 2 0 2 0 2 0 0 0 0 2 2 2 0 0 2 0 0 2 0 0 2 0 0
## [16597] 0 0 0 2 0 0 2 0 2 2 0 0 0 0 0 1 0 2 0 2 2 0 2 0 0 0 2 0 0 0 1 2 0 2 0 0
## [16633] 2 0 0 0 0 1 2 0 2 0 0 0 0 0 2 0 2 0 0 0 0 2 1 0 0 2 2 0 0 0 0 2 2 0 2 2
## [16669] 0 0 0 2 0 0 0 0 0 0 0 0 2 2 0 0 0 0 0 0 0 2 2 0 0 0 2 2 2 0 0 0 0 0 2 2
## [16705] 0 0 0 2 2 0 0 0 0 0 0 2 2 2 2 0 0 2 2 2 0 0 2 0 0 2 0 0 0 0 2 2 2 0 2 2
## [16741] 1 2 2 0 0 0 2 0 0 0 0 0 0 0 0 0 0 2 0 2 2 0 0 0 0 2 0 0 0 2 0 0 0 2 0 0
## [16777] 0 2 0 2 0 0 2 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 2 2 2 0 2 1 0 0 0 0 2 0 0 0
## [16813] 0 2 0 2 0 0 0 0 0 0 2 0 2 0 0 2 0 1 0 0 2 0 0 0 0 2 2 2 0 2 0 2 2 0 0 0
## [16849] 0 2 2 0 0 0 2 2 0 0 2 0 2 0 2 2 0 0 0 2 0 2 2 2 0 0 2 2 2 0 0 2 2 2 0 0
## [16885] 0 2 0 0 0 0 0 0 2 2 0 2 0 0 0 2 0 0 0 0 0 0 0 0 0 2 0 0 2 0 0 0 0 0 0 0
## [16921] 0 2 2 0 0 0 0 2 2 0 2 0 2 2 2 0 2 0 0 2 2 2 0 0 2 0 0 2 0 0 0 2 0 0 0 0
## [16957] 2 2 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 2 2 0 1 2 0 0 0 0 0 0 0 0 0 0
## [16993] 2 0 0 2 2 0 0 0 0 2 0 0 2 0 2 0 0 2 0 1 0 0 0 2 2 2 0 0 0 0 2 0 0 2 2 0
## [17029] 0 0 2 0 2 0 2 2 0 0 2 0 0 0 2 0 0 2 1 0 2 0 0 0 0 0 2 0 0 0 0 2 2 2 0 0
## [17065] 0 0 2 0 2 0 2 0 0 0 0 0 2 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 2 0 2 0 1 1 2
## [17101] 0 2 0 0 2 0 2 0 2 2 0 0 0 0 0 0 2 0 0 2 0 2 0 0 2 0 0 1 1 0 0 2 0 0 0 0
## [17137] 2 0 2 0 0 0 2 2 2 0 0 2 0 0 2 0 0 0 2 0 0 0 0 0 0 2 2 2 0 0 0 0 0 0 2 1
## [17173] 0 2 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 2 0 0 0 2 2 0 0 0 0 0 0 0 0 0 0 0
## [17209] 0 0 0 0 0 0 0 0 0 0 0 0 2 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 0
## [17245] 0 0 0 0 0 0 0 2 2 0 0 0 2 0 0 0 0 0 0 2 0 0 0 0 0 2 2 0 2 0 0 0 0 0 0 2
## [17281] 0 2 0 0 2 2 2 2 2 0 0 2 0 0 0 0 0 2 0 2 2 0 2 0 0 0 2 0 0 0 0 0 0 2 0 0
## [17317] 0 2 0 0 2 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 2 0 0 0 0 2 0 0 0 2 0 0 0 0 0 0
## [17353] 0 0 0 0 0 2 0 2 0 2 0 2 2 2 0 0 0 0 2 0 0 1 0 0 0 0 2 0 0 2 2 0 0 0 0 1
## [17389] 0 0 2 0 0 2 2 0 0 2 0 0 2 0 0 0 0 0 0 0 0 0 2 2 0 2 0 2 2 1 0 2 2 2 0 0
## [17425] 2 0 0 2 2 2 2 2 0 2 0 2 1 2 0 0 0 2 0 2 0 1 0 2 2 2 2 0 2 2 1 1 2 0 0 0
## [17461] 2 2 0 0 2 2 0 2 0 2 0 0 0 0 0 0 2 2 0 0 0 0 0 2 0 2 2 0 0 2 0 0 0 0 1 2
## [17497] 0 0 0 0 2 2 0 0 2 2 2 0 0 2 2 0 2 0 0 0 0 0 0 0 2 0 0 0 2 2 0 0 2 2 0 0
## [17533] 0 0 2 0 0 2 2 0 0 2 0 0 0 0 0 0 0 0 0 0 2 2 0 2 2 0 0 2 0 0 0 0 2 2 2 0
## [17569] 0 0 2 2 2 0 2 2 0 2 0 2 0 1 2 0 0 0 0 2 0 2 0 2 2 2 2 0 2 2 2 0 2 0 2 2
## [17605] 0 0 0 1 0 0 0 0 2 0 2 0 1 2 0 2 0 0 0 0 0 0 2 0 0 2 2 2 1 0 0 2 0 0 0 0
## [17641] 0 0 0 0 2 2 1 0 2 2 0 2 0 1 2 2 0 0 0 2 2 0 0 2 0 0 1 0 0 0 2 0 0 0 0 0
## [17677] 0 0 0 2 0 0 2 0 2 2 0 0 2 1 0 0 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 2 0 1 0
## [17713] 0 2 0 2 0 1 2 0 2 0 0 0 0 2 0 0 0 2 0 0 0 0 0 0 0 2 0 0 0 0 2 0 2 0 2 0
## [17749] 0 0 0 0 0 1 2 0 2 2 0 0 0 2 0 0 0 0 0 2 2 0 0 2 0 1 0 0 2 0 0 0 0 0 2 2
## [17785] 0 0 2 2 0 0 0 2 0 2 2 0 2 1 2 0 0 0 0 0 2 0 0 2 0 2 1 0 0 0 0 2 0 0 0 2
## [17821] 2 0 0 0 2 0 0 2 0 2 0 0 0 0 0 0 0 0 0 0 0 2 0 0 2 0 0 0 0 0 0 0 0 0 0 0
## [17857] 1 0 0 0 0 0 0 0 2 0 2 0 2 0 0 2 0 2 0 2 2 0 2 2 0 0 0 2 0 0 2 2 2 0 0 0
## [17893] 2 2 0 2 0 0 2 0 0 2 0 0 0 0 2 2 2 2 0 0 0 2 0 2 2 2 2 0 0 0 0 0 0 0 0 2
## [17929] 0 0 2 2 0 0 0 0 2 0 0 0 0 2 0 2 2 0 0 0 2 0 2 2 0 0 2 0 0 0 0 0 2 0 2 0
## [17965] 2 0 0 0 2 2 2 0 2 2 0 2 0 0 0 0 2 0 0 0 0 2 0 0 2 2 0 0 0 0 0 0 0 0 0 0
## [18001] 0 0 0 0 0 0 2 2 0 0 0 0 0 2 1 0 0 0 2 0 0 0 0 2 0 2 0 0 0 2 0 0 0 0 0 0
## [18037] 0 2 2 2 2 2 0 0 1 2 0 0 0 0 0 0 0 0 2 0 0 2 0 0 1 2 2 0 0 2 0 1 2 0 0 0
## [18073] 2 0 0 0 0 2 0 2 1 2 2 0 0 0 0 0 2 0 2 0 2 0 0 0 0 0 2 0 2 0 0 2 0 0 0 0
## [18109] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 2 1 0 0 0 0 0 2
## [18145] 0 0 0 0 0 0 0 0 0 0 0 2 2 2 0 2 0 0 0 0 0 2 1 0 0 0 0 0 0 0 0 0 2 2 0 2
## [18181] 0 0 0 2 0 0 0 0 0 0 0 0 0 0 2 0 0 0 0 0 2 2 0 0 0 0 0 0 0 0 0 0 2 0 0 0
## [18217] 0 0 2 2 2 0 0 2 0 2 0 0 0 0 0 2 0 0 0 0 0 0 0 0 2 2 0 0 0 0 0 0 0 0 0 0
## [18253] 0 2 0 0 0 0 2 2 2 0 0 0 0 0 0 0 0 0 0 0 0 2 2 0 0 0 0 0 0 0 0 2 0 0 0 0
## [18289] 1 1 2 0 0 0 2 0 2 0 2 0 0 0 0 0 0 2 0 2 0 0 0 0 2 0 0 0 0 0 0 0 0 2 0 0
## [18325] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 2 0 0 0 0 0 0 2 2 0 0 2 0 2 0 0 0 0 0 2
## [18361] 2 0 0 0 2 0 2 2 0 0 0 0 0 0 2 2 0 1 2 0 0 0 0 0 0 2 2 0 0 2 0 0 2 0 0 0
## [18397] 0 0 2 0 0 0 2 2 0 2 0 0 0 0 2 2 2 2 2 0 2 0 0 0 0 0 2 0 0 2 0 0 0 2 0 0
## [18433] 0 0 2 0 0 0 2 0 0 0 0 0 2 2 2 1 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0
## [18469] 0 2 0 2 0 0 0 2 0 0 0 0 0 2 0 2 2 2 0 0 0 0 2 0 0 2 0 0 0 2 0 0 0 0 0 2
## [18505] 0 2#has another kid
uganda16$currentchildren<-uganda16$v202+uganda16$v203
#regions
uganda16$region<-as.factor(uganda16$v024)
uganda16$regions<-car::Recode(uganda16$v024, recodes= "0='Kampala'; 1= 'South Buganda'; 2='North Buganda';3='Busoga';4='Bukedi'; 5='Bugisu'; 6='Teso';7='Karamoja'; 8='Lango'; 9='Acholi'; 10='West Nile'; 11='Bunyoro';12='Tooro'; 13='Ankole';14='Kigezi'; else='NA'", as.factor=T)
library(dplyr)
ug<-filter(uganda16,uganda16$v613<50)
ug$fertdis<-(ug$v613-ug$currentchildren)
ug2<-filter(ug, ug$fertdis<12, ug$fertdis>-12)
ug2$fertdisgroup<-car::recode(ug2$fertdis, recodes= "0='no discrepancy'; 1:12= 'have more kids than want'; -1= 'have less kids than want'; -2= 'have less kids than want'; -3= 'have less kids than want'; -4= 'have less kids than want'; -5= 'have less kids than want'; -6= 'have less kids than want'; -7= 'have less kids than want'; -8= 'have less kids than want'; -9= 'have less kids than want'; -10= 'have less kids than want'", as.factor = T)
uganda16$idealnumchil<-car::recode(uganda16$v613, recodes= "1='1'; 2='2'; 3='3'; 4='4'; 5='5'; 6='6 or more'; 7='NA'", as.factor=T)
uganda16$fertdis<-uganda16$idealnumchil-uganda16$currentchildren## Warning in Ops.factor(uganda16$idealnumchil, uganda16$currentchildren): '-' not
## meaningful for factors #need to code this into 3 categories: 1 ideal number of children= current number of children, 2= ideal number of children less than current number of children and 3= ideal number of children more than current number of children
# survey design variables
uganda16$psu <- uganda16$v021
uganda16$strata <- uganda16$v022
uganda16$pwt <- uganda16$v005/1000000
desi<-svydesign(ids = ~ psu, strata = ~ strata, weights =~ pwt, data=uganda16)
# survey design variables
ug$psu <- ug$v021
ug$strata <- ug$v022
ug$pwt <- ug$v005/1000000
des<-svydesign(ids = ~ psu, strata = ~ strata, weights =~ pwt, data=ug)
# survey design variables
ug2$psu <- ug2$v021
ug2$strata <- ug2$v022
ug2$pwt <- ug2$v005/1000000
design<-svydesign(ids = ~ psu, strata = ~ strata, weights =~ pwt, data=ug2)#model 1 (binomial logistic)
#empowerment & whether women want another child or not
library(gtsummary)
library(ggplot2)
library(stats)
library(srvyr)##
## Attaching package: 'srvyr'## The following object is masked from 'package:MASS':
##
## select## The following object is masked from 'package:stats':
##
## filterglm.fit <- svyglm(wantanotherchild ~ currentchildren + bank+ moderncon + educationlevel + agegroup +decide_money, family = binomial, design=desi)## Warning in eval(family$initialize): non-integer #successes in a binomial glm!glm.fit%>%
tbl_regression(exp=T)| Characteristic | OR1 | 95% CI1 | p-value |
|---|---|---|---|
| sons at home | 0.76 | 0.73, 0.78 | <0.001 |
| has an account in a bank or other financial institution | |||
| No | — | — | |
| Yes | 1.13 | 0.98, 1.32 | 0.10 |
| moderncon | 0.97 | 0.92, 1.03 | 0.3 |
| educationlevel | |||
| none | — | — | |
| primary | 1.02 | 0.85, 1.22 | 0.8 |
| secondary and above | 1.41 | 1.16, 1.72 | <0.001 |
| agegroup | |||
| 15-19 | — | — | |
| 20-24 | 1.02 | 0.85, 1.21 | 0.9 |
| 25-29 | 0.50 | 0.42, 0.61 | <0.001 |
| 30-34 | 0.21 | 0.18, 0.25 | <0.001 |
| 35-39 | 0.08 | 0.07, 0.10 | <0.001 |
| 40-44 | 0.03 | 0.02, 0.03 | <0.001 |
| 45+ | 0.01 | 0.01, 0.02 | <0.001 |
| decide_money | 1.53 | 1.39, 1.68 | <0.001 |
|
1
OR = Odds Ratio, CI = Confidence Interval
|
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#model 2
#empowerment & current children minus ideal family size
#need to fix dependent variable
#Use multinomial model
library(svyVGAM)
mfit<-svy_vglm(fertdisgroup~currentchildren+factor(bank)+moderncon+ factor(educationlevel) +factor(agegroup)+ decide_money, family=multinomial(refLevel = 1),
design = design)
mfit%>%
tbl_regression(exp=T)## ! `broom::tidy()` failed to tidy the model.## x No tidy method for objects of class svy_vglm## v `tidy_parameters()` used instead.## i Add `tidy_fun = broom.helpers::tidy_parameters` to quiet these messages.## x Unable to identify the list of variables.
##
## This is usually due to an error calling `stats::model.frame(x)`or `stats::model.matrix(x)`.
## It could be the case if that type of model does not implement these methods.
## Rarely, this error may occur if the model object was created within
## a functional programming framework (e.g. using `lappy()`, `purrr::map()`, etc.).| Characteristic | exp(Beta) | 95% CI1 | p-value |
|---|---|---|---|
| (Intercept):1 | 18,620 | 2,582, 134,262 | <0.001 |
| (Intercept):2 | 191 | 25.9, 1,399 | <0.001 |
| currentchildren:1 | 0.31 | 0.28, 0.33 | <0.001 |
| currentchildren:2 | 0.60 | 0.56, 0.65 | <0.001 |
| factor(bank)Yes:1 | 1.11 | 0.82, 1.50 | 0.5 |
| factor(bank)Yes:2 | 1.21 | 0.87, 1.69 | 0.3 |
| moderncon:1 | 0.86 | 0.79, 0.93 | <0.001 |
| moderncon:2 | 0.93 | 0.85, 1.03 | 0.2 |
| factor(educationlevel)primary:1 | 0.63 | 0.50, 0.80 | <0.001 |
| factor(educationlevel)primary:2 | 0.91 | 0.70, 1.18 | 0.5 |
| factor(educationlevel)secondary and above:1 | 0.46 | 0.33, 0.66 | <0.001 |
| factor(educationlevel)secondary and above:2 | 0.80 | 0.55, 1.17 | 0.3 |
| factor(agegroup)20-24:1 | 0.14 | 0.02, 1.05 | 0.056 |
| factor(agegroup)20-24:2 | 0.07 | 0.01, 0.56 | 0.012 |
| factor(agegroup)25-29:1 | 0.08 | 0.01, 0.56 | 0.012 |
| factor(agegroup)25-29:2 | 0.07 | 0.01, 0.51 | 0.009 |
| factor(agegroup)30-34:1 | 0.07 | 0.01, 0.51 | 0.009 |
| factor(agegroup)30-34:2 | 0.07 | 0.01, 0.55 | 0.011 |
| factor(agegroup)35-39:1 | 0.05 | 0.01, 0.40 | 0.004 |
| factor(agegroup)35-39:2 | 0.05 | 0.01, 0.35 | 0.003 |
| factor(agegroup)40-44:1 | 0.08 | 0.01, 0.60 | 0.014 |
| factor(agegroup)40-44:2 | 0.06 | 0.01, 0.48 | 0.008 |
| factor(agegroup)45+:1 | 0.08 | 0.01, 0.62 | 0.016 |
| factor(agegroup)45+:2 | 0.05 | 0.01, 0.41 | 0.005 |
| decide_money:1 | 1.25 | 1.04, 1.50 | 0.016 |
| decide_money:2 | 1.17 | 0.95, 1.44 | 0.13 |
|
1
CI = Confidence Interval
|
|||
summary(mfit)## svy_vglm.survey.design(fertdisgroup ~ currentchildren + factor(bank) +
## moderncon + factor(educationlevel) + factor(agegroup) + decide_money,
## family = multinomial(refLevel = 1), design = design)
## Stratified 1 - level Cluster Sampling design (with replacement)
## With (696) clusters.
## svydesign(ids = ~psu, strata = ~strata, weights = ~pwt, data = ug2)
## Coef SE z
## (Intercept):1 9.831991 1.007955 9.7544
## (Intercept):2 5.249876 1.017294 5.1606
## currentchildren:1 -1.177294 0.039986 -29.4429
## currentchildren:2 -0.504529 0.035674 -14.1428
## factor(bank)Yes:1 0.100647 0.154017 0.6535
## factor(bank)Yes:2 0.193103 0.170430 1.1330
## moderncon:1 -0.152744 0.042804 -3.5685
## moderncon:2 -0.068062 0.049070 -1.3870
## factor(educationlevel)primary:1 -0.460119 0.123604 -3.7225
## factor(educationlevel)primary:2 -0.093149 0.130886 -0.7117
## factor(educationlevel)secondary and above:1 -0.768617 0.178857 -4.2974
## factor(educationlevel)secondary and above:2 -0.220381 0.192946 -1.1422
## factor(agegroup)20-24:1 -1.967267 1.027680 -1.9143
## factor(agegroup)20-24:2 -2.614322 1.039843 -2.5142
## factor(agegroup)25-29:1 -2.558675 1.013798 -2.5239
## factor(agegroup)25-29:2 -2.677883 1.018151 -2.6301
## factor(agegroup)30-34:1 -2.669011 1.016994 -2.6244
## factor(agegroup)30-34:2 -2.600594 1.020704 -2.5478
## factor(agegroup)35-39:1 -2.923490 1.019983 -2.8662
## factor(agegroup)35-39:2 -3.073920 1.028026 -2.9901
## factor(agegroup)40-44:1 -2.520974 1.025706 -2.4578
## factor(agegroup)40-44:2 -2.741181 1.028913 -2.6642
## factor(agegroup)45+:1 -2.478723 1.024802 -2.4187
## factor(agegroup)45+:2 -2.913352 1.035858 -2.8125
## decide_money:1 0.222914 0.092729 2.4039
## decide_money:2 0.157532 0.104888 1.5019
## p
## (Intercept):1 < 2.2e-16
## (Intercept):2 2.461e-07
## currentchildren:1 < 2.2e-16
## currentchildren:2 < 2.2e-16
## factor(bank)Yes:1 0.5134487
## factor(bank)Yes:2 0.2571979
## moderncon:1 0.0003591
## moderncon:2 0.1654282
## factor(educationlevel)primary:1 0.0001972
## factor(educationlevel)primary:2 0.4766603
## factor(educationlevel)secondary and above:1 1.728e-05
## factor(educationlevel)secondary and above:2 0.2533744
## factor(agegroup)20-24:1 0.0555845
## factor(agegroup)20-24:2 0.0119319
## factor(agegroup)25-29:1 0.0116077
## factor(agegroup)25-29:2 0.0085349
## factor(agegroup)30-34:1 0.0086799
## factor(agegroup)30-34:2 0.0108391
## factor(agegroup)35-39:1 0.0041541
## factor(agegroup)35-39:2 0.0027887
## factor(agegroup)40-44:1 0.0139794
## factor(agegroup)40-44:2 0.0077183
## factor(agegroup)45+:1 0.0155746
## factor(agegroup)45+:2 0.0049158
## decide_money:1 0.0162197
## decide_money:2 0.1331226#model 4
#linear model of fertdis
hist(ug$fertdis)
fertdis.lm <- lm(fertdis ~ educationlevel + currentchildren + bank + moderncon + decide_money + agegroup, data = ug, design=des)## Warning: In lm.fit(x, y, offset = offset, singular.ok = singular.ok, ...) :
## extra argument 'design' will be disregardedsummary(fertdis.lm)##
## Call:
## lm(formula = fertdis ~ educationlevel + currentchildren + bank +
## moderncon + decide_money + agegroup, data = ug, design = des)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.963 -1.022 -0.267 0.733 24.424
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.232693 0.054906 95.303 < 2e-16 ***
## educationlevelprimary -0.965734 0.048380 -19.961 < 2e-16 ***
## educationlevelsecondary and above -1.449719 0.054659 -26.523 < 2e-16 ***
## currentchildren -0.826923 0.009567 -86.434 < 2e-16 ***
## bankYes -0.218357 0.046069 -4.740 2.16e-06 ***
## moderncon -0.124892 0.016758 -7.453 9.57e-14 ***
## decide_money 0.149932 0.031751 4.722 2.35e-06 ***
## agegroup20-24 0.036475 0.044622 0.817 0.413702
## agegroup25-29 0.187265 0.051140 3.662 0.000251 ***
## agegroup30-34 0.394394 0.056710 6.955 3.66e-12 ***
## agegroup35-39 0.604676 0.062982 9.601 < 2e-16 ***
## agegroup40-44 0.965395 0.065373 14.767 < 2e-16 ***
## agegroup45+ 1.308961 0.070400 18.593 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.89 on 18026 degrees of freedom
## Multiple R-squared: 0.3722, Adjusted R-squared: 0.3718
## F-statistic: 890.7 on 12 and 18026 DF, p-value: < 2.2e-16fertdis.lm%>%
tbl_regression(exp=F)| Characteristic | Beta | 95% CI1 | p-value |
|---|---|---|---|
| educationlevel | |||
| none | — | — | |
| primary | -1.0 | -1.1, -0.87 | <0.001 |
| secondary and above | -1.4 | -1.6, -1.3 | <0.001 |
| sons at home | -0.83 | -0.85, -0.81 | <0.001 |
| has an account in a bank or other financial institution | |||
| No | — | — | |
| Yes | -0.22 | -0.31, -0.13 | <0.001 |
| moderncon | -0.12 | -0.16, -0.09 | <0.001 |
| decide_money | 0.15 | 0.09, 0.21 | <0.001 |
| agegroup | |||
| 15-19 | — | — | |
| 20-24 | 0.04 | -0.05, 0.12 | 0.4 |
| 25-29 | 0.19 | 0.09, 0.29 | <0.001 |
| 30-34 | 0.39 | 0.28, 0.51 | <0.001 |
| 35-39 | 0.60 | 0.48, 0.73 | <0.001 |
| 40-44 | 1.0 | 0.84, 1.1 | <0.001 |
| 45+ | 1.3 | 1.2, 1.4 | <0.001 |
|
1
CI = Confidence Interval
|
|||
#compare AICs
-2*mfit$fit@criterion$loglikelihood+2*length(fertdis.lm$coef)## [1] 11268.44-2*mfit$fit@criterion$loglikelihood+2*length(mfit$coef)## [1] 11294.44