# load packages
library(tidyverse)## -- Attaching packages --------------------------------------- tidyverse 1.3.1 --## v ggplot2 3.3.5 v purrr 0.3.4
## v tibble 3.1.6 v dplyr 1.0.7
## v tidyr 1.1.4 v stringr 1.4.0
## v readr 2.1.1 v forcats 0.5.1## -- Conflicts ------------------------------------------ tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()library(survey)## Loading required package: grid## Loading required package: Matrix##
## Attaching package: 'Matrix'## The following objects are masked from 'package:tidyr':
##
## expand, pack, unpack## Loading required package: survival##
## Attaching package: 'survey'## The following object is masked from 'package:graphics':
##
## dotchartlibrary(ggplot2)
library(haven)
library(tableone)
library(gtsummary)## #BlackLivesMatteruganda16 <- 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'")
#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$v602==1,1,0)
uganda16$fertilitypreference<-car::Recode(uganda16$wantanotherchild, recodes= "0= 'Do Not Want another child'; 1= 'Do Want another child'")
#education level
uganda16$educationlevel <- car::recode(uganda16$v106,
recodes = "0 = 'none'; 1 = 'primary'; 2:3='secondary and above' ",
as.factor=T)
#internet
uganda16$internet<-as.factor(uganda16$v171a)
uganda16$internet<-car::Recode(uganda16$v171a, recodes= "0='never'; 1= 'in the past year'; 2='over a year ago'; 3= 'yes, but unsure when'", as.factor=T)
#contraception
uganda16$contraception<-as.factor(uganda16$v313)
uganda16$contraception<-car::Recode(uganda16$v313, recodes= "0='none'; 1='folkloric method'; 2='traditional method' ;3= 'modern method'", as.factor=T)
# survey design variables
uganda16$psu <- uganda16$v021
uganda16$strata <- uganda16$v022
uganda16$pwt <- uganda16$v005/1000000
design<-svydesign(ids = ~ psu, strata = ~ strata, weights =~ pwt, data=uganda16)#add variables to model one by one (controlling for age & education)
model1<-svyglm(wantanotherchild ~ agegroup + contraception + educationlevel,
family = binomial,
design = design)## Warning in eval(family$initialize): non-integer #successes in a binomial glm!summary(model1)##
## Call:
## svyglm(formula = wantanotherchild ~ agegroup + contraception +
## educationlevel, design = design, family = binomial)
##
## Survey design:
## svydesign(ids = ~psu, strata = ~strata, weights = ~pwt, data = uganda16)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.41768 0.49672 2.854 0.00445 **
## agegroup20-24 -0.19157 0.08856 -2.163 0.03088 *
## agegroup25-29 -1.10112 0.08870 -12.415 < 2e-16 ***
## agegroup30-34 -2.13288 0.08114 -26.287 < 2e-16 ***
## agegroup35-39 -3.12327 0.08693 -35.929 < 2e-16 ***
## agegroup40-44 -4.11390 0.11898 -34.578 < 2e-16 ***
## agegroup45+ -4.79567 0.15235 -31.477 < 2e-16 ***
## contraceptionmodern method 0.31642 0.47640 0.664 0.50681
## contraceptionnone 0.43767 0.47947 0.913 0.36167
## contraceptiontraditional method 0.85921 0.49697 1.729 0.08430 .
## educationlevelprimary 0.12149 0.08639 1.406 0.16012
## educationlevelsecondary and above 0.70304 0.09710 7.241 1.26e-12 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1.009267)
##
## Number of Fisher Scoring iterations: 5#add variables to model one by one (controlling for age & education)
model2<-svyglm(wantanotherchild ~ agegroup + contraception + internet,
family = binomial,
design = design)## Warning in eval(family$initialize): non-integer #successes in a binomial glm!summary(model2)##
## Call:
## svyglm(formula = wantanotherchild ~ agegroup + contraception +
## internet, design = design, family = binomial)
##
## Survey design:
## svydesign(ids = ~psu, strata = ~strata, weights = ~pwt, data = uganda16)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.48190 0.53712 4.621 4.61e-06 ***
## agegroup20-24 -0.18349 0.08710 -2.107 0.0355 *
## agegroup25-29 -1.11182 0.08862 -12.546 < 2e-16 ***
## agegroup30-34 -2.16385 0.08127 -26.626 < 2e-16 ***
## agegroup35-39 -3.19015 0.08602 -37.088 < 2e-16 ***
## agegroup40-44 -4.18566 0.11815 -35.426 < 2e-16 ***
## agegroup45+ -4.86831 0.15250 -31.923 < 2e-16 ***
## contraceptionmodern method 0.38298 0.51353 0.746 0.4561
## contraceptionnone 0.46845 0.51641 0.907 0.3647
## contraceptiontraditional method 0.92191 0.53217 1.732 0.0837 .
## internetnever -0.85964 0.13128 -6.548 1.18e-10 ***
## internetover a year ago -0.55186 0.26588 -2.076 0.0383 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1.007343)
##
## Number of Fisher Scoring iterations: 5#finalmodel
finalmodel<-svyglm(wantanotherchild ~ agegroup + contraception + bank + internet + educationlevel,
family = binomial,
design = design)## Warning in eval(family$initialize): non-integer #successes in a binomial glm!summary(finalmodel)##
## Call:
## svyglm(formula = wantanotherchild ~ agegroup + contraception +
## bank + internet + educationlevel, design = design, family = binomial)
##
## Survey design:
## svydesign(ids = ~psu, strata = ~strata, weights = ~pwt, data = uganda16)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.90405 0.52656 3.616 0.000322 ***
## agegroup20-24 -0.21141 0.08792 -2.404 0.016479 *
## agegroup25-29 -1.13566 0.08907 -12.750 < 2e-16 ***
## agegroup30-34 -2.16686 0.08165 -26.539 < 2e-16 ***
## agegroup35-39 -3.16834 0.08866 -35.737 < 2e-16 ***
## agegroup40-44 -4.15785 0.12088 -34.397 < 2e-16 ***
## agegroup45+ -4.83811 0.15306 -31.609 < 2e-16 ***
## contraceptionmodern method 0.31506 0.48775 0.646 0.518534
## contraceptionnone 0.43906 0.49105 0.894 0.371591
## contraceptiontraditional method 0.82570 0.50911 1.622 0.105324
## bankYes 0.19372 0.07471 2.593 0.009726 **
## internetnever -0.46131 0.13172 -3.502 0.000493 ***
## internetover a year ago -0.41643 0.25208 -1.652 0.099016 .
## educationlevelprimary 0.10683 0.08691 1.229 0.219429
## educationlevelsecondary and above 0.54156 0.09839 5.504 5.35e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1.009863)
##
## Number of Fisher Scoring iterations: 5## don't forget parameter estimates and odds ratios
library(gtsummary)
model1%>%
tbl_regression(exponentiate=TRUE)| Characteristic | OR1 | 95% CI1 | p-value |
|---|---|---|---|
| agegroup | |||
| 15-19 | — | — | |
| 20-24 | 0.83 | 0.69, 0.98 | 0.031 |
| 25-29 | 0.33 | 0.28, 0.40 | <0.001 |
| 30-34 | 0.12 | 0.10, 0.14 | <0.001 |
| 35-39 | 0.04 | 0.04, 0.05 | <0.001 |
| 40-44 | 0.02 | 0.01, 0.02 | <0.001 |
| 45+ | 0.01 | 0.01, 0.01 | <0.001 |
| contraception | |||
| folkloric method | — | — | |
| modern method | 1.37 | 0.54, 3.50 | 0.5 |
| none | 1.55 | 0.60, 3.97 | 0.4 |
| traditional method | 2.36 | 0.89, 6.27 | 0.084 |
| educationlevel | |||
| none | — | — | |
| primary | 1.13 | 0.95, 1.34 | 0.2 |
| secondary and above | 2.02 | 1.67, 2.44 | <0.001 |
|
1
OR = Odds Ratio, CI = Confidence Interval
|
|||
model2%>%
tbl_regression(exponentiate=TRUE)| Characteristic | OR1 | 95% CI1 | p-value |
|---|---|---|---|
| agegroup | |||
| 15-19 | — | — | |
| 20-24 | 0.83 | 0.70, 0.99 | 0.036 |
| 25-29 | 0.33 | 0.28, 0.39 | <0.001 |
| 30-34 | 0.11 | 0.10, 0.13 | <0.001 |
| 35-39 | 0.04 | 0.03, 0.05 | <0.001 |
| 40-44 | 0.02 | 0.01, 0.02 | <0.001 |
| 45+ | 0.01 | 0.01, 0.01 | <0.001 |
| contraception | |||
| folkloric method | — | — | |
| modern method | 1.47 | 0.54, 4.02 | 0.5 |
| none | 1.60 | 0.58, 4.40 | 0.4 |
| traditional method | 2.51 | 0.88, 7.15 | 0.084 |
| internet | |||
| in the past year | — | — | |
| never | 0.42 | 0.33, 0.55 | <0.001 |
| over a year ago | 0.58 | 0.34, 0.97 | 0.038 |
|
1
OR = Odds Ratio, CI = Confidence Interval
|
|||
finalmodel%>%
tbl_regression(exponentiate=TRUE)| Characteristic | OR1 | 95% CI1 | p-value |
|---|---|---|---|
| agegroup | |||
| 15-19 | — | — | |
| 20-24 | 0.81 | 0.68, 0.96 | 0.016 |
| 25-29 | 0.32 | 0.27, 0.38 | <0.001 |
| 30-34 | 0.11 | 0.10, 0.13 | <0.001 |
| 35-39 | 0.04 | 0.04, 0.05 | <0.001 |
| 40-44 | 0.02 | 0.01, 0.02 | <0.001 |
| 45+ | 0.01 | 0.01, 0.01 | <0.001 |
| contraception | |||
| folkloric method | — | — | |
| modern method | 1.37 | 0.53, 3.57 | 0.5 |
| none | 1.55 | 0.59, 4.07 | 0.4 |
| traditional method | 2.28 | 0.84, 6.21 | 0.11 |
| has an account in a bank or other financial institution | |||
| No | — | — | |
| Yes | 1.21 | 1.05, 1.41 | 0.010 |
| internet | |||
| in the past year | — | — | |
| never | 0.63 | 0.49, 0.82 | <0.001 |
| over a year ago | 0.66 | 0.40, 1.08 | 0.10 |
| educationlevel | |||
| none | — | — | |
| primary | 1.11 | 0.94, 1.32 | 0.2 |
| secondary and above | 1.72 | 1.42, 2.08 | <0.001 |
|
1
OR = Odds Ratio, CI = Confidence Interval
|
|||
#interesting cases
library(emmeans)
grid1<-ref_grid(finalmodel)
specialcases<-emmeans(object = grid1,
specs = c( "bank", "internet", "agegroup"),
type="response" )
knitr::kable(specialcases, digits = 4)| bank | internet | agegroup | prob | SE | df | asymp.LCL | asymp.UCL |
|---|---|---|---|---|---|---|---|
| No | in the past year | 15-19 | 0.9252 | 0.0134 | Inf | 0.8943 | 0.9476 |
| Yes | in the past year | 15-19 | 0.9375 | 0.0116 | Inf | 0.9105 | 0.9568 |
| No | never | 15-19 | 0.8863 | 0.0151 | Inf | 0.8531 | 0.9128 |
| Yes | never | 15-19 | 0.9044 | 0.0141 | Inf | 0.8730 | 0.9287 |
| No | over a year ago | 15-19 | 0.8908 | 0.0265 | Inf | 0.8270 | 0.9330 |
| Yes | over a year ago | 15-19 | 0.9082 | 0.0237 | Inf | 0.8501 | 0.9453 |
| No | in the past year | 20-24 | 0.9092 | 0.0159 | Inf | 0.8728 | 0.9359 |
| Yes | in the past year | 20-24 | 0.9240 | 0.0139 | Inf | 0.8918 | 0.9471 |
| No | never | 20-24 | 0.8632 | 0.0168 | Inf | 0.8269 | 0.8929 |
| Yes | never | 20-24 | 0.8845 | 0.0160 | Inf | 0.8493 | 0.9124 |
| No | over a year ago | 20-24 | 0.8684 | 0.0304 | Inf | 0.7967 | 0.9175 |
| Yes | over a year ago | 20-24 | 0.8890 | 0.0275 | Inf | 0.8228 | 0.9326 |
| No | in the past year | 25-29 | 0.7989 | 0.0295 | Inf | 0.7349 | 0.8506 |
| Yes | in the past year | 25-29 | 0.8282 | 0.0260 | Inf | 0.7711 | 0.8734 |
| No | never | 25-29 | 0.7147 | 0.0284 | Inf | 0.6559 | 0.7669 |
| Yes | never | 25-29 | 0.7525 | 0.0273 | Inf | 0.6952 | 0.8020 |
| No | over a year ago | 25-29 | 0.7237 | 0.0539 | Inf | 0.6069 | 0.8163 |
| Yes | over a year ago | 25-29 | 0.7607 | 0.0506 | Inf | 0.6484 | 0.8457 |
| No | in the past year | 30-34 | 0.5862 | 0.0434 | Inf | 0.4994 | 0.6680 |
| Yes | in the past year | 30-34 | 0.6323 | 0.0419 | Inf | 0.5470 | 0.7100 |
| No | never | 30-34 | 0.4718 | 0.0335 | Inf | 0.4070 | 0.5375 |
| Yes | never | 30-34 | 0.5202 | 0.0361 | Inf | 0.4495 | 0.5900 |
| No | over a year ago | 30-34 | 0.4830 | 0.0660 | Inf | 0.3576 | 0.6105 |
| Yes | over a year ago | 30-34 | 0.5314 | 0.0682 | Inf | 0.3986 | 0.6598 |
| No | in the past year | 35-39 | 0.3423 | 0.0414 | Inf | 0.2663 | 0.4273 |
| Yes | in the past year | 35-39 | 0.3871 | 0.0430 | Inf | 0.3068 | 0.4740 |
| No | never | 35-39 | 0.2470 | 0.0262 | Inf | 0.1994 | 0.3018 |
| Yes | never | 35-39 | 0.2848 | 0.0297 | Inf | 0.2303 | 0.3463 |
| No | over a year ago | 35-39 | 0.2555 | 0.0503 | Inf | 0.1697 | 0.3655 |
| Yes | over a year ago | 35-39 | 0.2940 | 0.0564 | Inf | 0.1965 | 0.4149 |
| No | in the past year | 40-44 | 0.1621 | 0.0267 | Inf | 0.1163 | 0.2214 |
| Yes | in the past year | 40-44 | 0.1902 | 0.0295 | Inf | 0.1389 | 0.2548 |
| No | never | 40-44 | 0.1087 | 0.0152 | Inf | 0.0824 | 0.1422 |
| Yes | never | 40-44 | 0.1289 | 0.0177 | Inf | 0.0980 | 0.1679 |
| No | over a year ago | 40-44 | 0.1131 | 0.0279 | Inf | 0.0688 | 0.1804 |
| Yes | over a year ago | 40-44 | 0.1341 | 0.0329 | Inf | 0.0816 | 0.2125 |
| No | in the past year | 45+ | 0.0892 | 0.0185 | Inf | 0.0590 | 0.1328 |
| Yes | in the past year | 45+ | 0.1063 | 0.0215 | Inf | 0.0710 | 0.1562 |
| No | never | 45+ | 0.0582 | 0.0103 | Inf | 0.0410 | 0.0819 |
| Yes | never | 45+ | 0.0697 | 0.0124 | Inf | 0.0490 | 0.0983 |
| No | over a year ago | 45+ | 0.0607 | 0.0172 | Inf | 0.0345 | 0.1045 |
| Yes | over a year ago | 45+ | 0.0727 | 0.0208 | Inf | 0.0411 | 0.1254 |
###Some key takeaways that I am deriving from this analysis are: 1) maybe I should take contraception out of the model as it isn’t significant & 2) Access to internet and a bank are interesting cases to look at together. Next, I should look for more variables that showcase empowerment to add to my analysis. I also need to add whether or not a woman already has children to my analysis and maybe religion, too.