Luttinen HW 9
Rebecca Luttinen
4/14/2022
#splines
#load data and packages
library(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionlibrary(haven)
library(srvyr)##
## Attaching package: 'srvyr'## The following object is masked from 'package:stats':
##
## filterlibrary(survey)## Loading required package: grid## Loading required package: Matrix## Loading required package: survival##
## Attaching package: 'survey'## The following object is masked from 'package:graphics':
##
## dotchartuganda16 <- 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$v614, 1, 0)
uganda16$idealnumchil<-car::recode(uganda16$v614, recodes= "1='1'; 2='2'; 3='3'; 4='4'; 5='5'; 6='6 or more'; 7='NA'", as.factor=T)
uganda16$fertdis<-uganda16$idealnumchil-uganda16$currentchildren## Warning: Unknown or uninitialised column: `currentchildren`.## Warning in Ops.factor(uganda16$idealnumchil, uganda16$currentchildren): '-' not
## meaningful for factors#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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## [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
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## [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
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## [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
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## [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
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## [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
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## [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
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## [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$v024, recodes= "0='Kampala'; 1= 'South Buganda'; 2='North Buganda';3='Busoga';4='Bukedi'; 5='Bugisu'; 6='Teso';7='Karamoja'; 8='Lango'; 9='Acholi'; 10='West Nile'; 11='Bunyoro';12='Tooro'; 13='Ankole';14='Kigezi'; else='NA'", as.factor=T)
# survey design variables
uganda16$psu <- uganda16$v021
uganda16$strata <- uganda16$v022
uganda16$pwt <- uganda16$v005/1000000
desi<-svydesign(ids = ~ psu, strata = ~ strata, weights =~ pwt, data=uganda16)#check distribution
plot(uganda16$v013, uganda16$currentchildren)
#v013 is age in increments coded as numeric
plot(uganda16$v012, uganda16$currentchildren)
#v013 is age as a continuous variable#Using a data set of your choice, do the following:
#1) define an outcome, this can be of any form, but use an appropriate distribution for your outcome. Also define at least 1 continuous predictor.
#Outcome of interest- current number of children #continuous predictor- age
#2) Using the gam() function, estimate a model with only linear terms in your model
library(mgcv)## Loading required package: nlme##
## Attaching package: 'nlme'## The following object is masked from 'package:dplyr':
##
## collapse## This is mgcv 1.8-38. For overview type 'help("mgcv-package")'.gamfit<-gam(currentchildren ~ v012+ factor(educationlevel), data=uganda16)
summary(gamfit)##
## Family: gaussian
## Link function: identity
##
## Formula:
## currentchildren ~ v012 + factor(educationlevel)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.757019 0.057619 -13.138 < 2e-16
## v012 0.112483 0.001306 86.102 < 2e-16
## factor(educationlevel)primary -0.282743 0.039592 -7.141 9.58e-13
## factor(educationlevel)secondary and above -0.979961 0.042977 -22.802 < 2e-16
##
## (Intercept) ***
## v012 ***
## factor(educationlevel)primary ***
## factor(educationlevel)secondary and above ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## R-sq.(adj) = 0.35 Deviance explained = 35%
## GCV = 2.5745 Scale est. = 2.574 n = 18506plot(uganda16$v012, uganda16$currentchildren)
#3) Repeat Step 2, but include a smooth term for at least one continuous variable
gamfits<-gam(currentchildren ~ s(v012) +educationlevel, data=uganda16)
summary(gamfits)##
## Family: gaussian
## Link function: identity
##
## Formula:
## currentchildren ~ s(v012) + educationlevel
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.39165 0.03393 70.498 < 2e-16 ***
## educationlevelprimary -0.26317 0.03687 -7.138 9.84e-13 ***
## educationlevelsecondary and above -1.03793 0.04024 -25.795 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(v012) 7.511 8.433 1355 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.437 Deviance explained = 43.8%
## GCV = 2.23 Scale est. = 2.2288 n = 18506plot(gamfits)
#4) Test if the model in step 3 is fitting the data better than the purely linear model.
anova(gamfit, gamfits, test="Chisq")## Analysis of Deviance Table
##
## Model 1: currentchildren ~ v012 + factor(educationlevel)
## Model 2: currentchildren ~ s(v012) + educationlevel
## Resid. Df Resid. Dev Df Deviance Pr(>Chi)
## 1 18502 47624
## 2 18495 41222 7.4333 6401.5 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1#The results show the smoothed model did a better job than the solely linear model based on the chi test.#5) Produce a plot of the smooth effect from the model in step 3 #shown in step 3 already