Galton example
Jeff Leek
Dependencies
This document depends on the following packages:
library(devtools)
library(Biobase)To install these packages you can use the code (or if you are compiling the document, remove the eval=FALSE from the chunk.)
install.packages(c("devtools"))
source("http://www.bioconductor.org/biocLite.R")
biocLite(c("Biobase"))WHO childhood hunger data
hunger = read.csv("http://apps.who.int/gho/athena/data/GHO/WHOSIS_000008.csv?profile=text&filter=COUNTRY:*;SEX:*")
hunger <- hunger[hunger$Sex!="Both sexes",]
head(hunger)## Indicator Data.Source PUBLISH.STATES Year
## 4 Children aged <5 years underweight (%) NLIS_312819 Published 2004
## 5 Children aged <5 years underweight (%) NLIS_311928 Published 1998
## 8 Children aged <5 years underweight (%) NLIS_312965 Published 2005
## 9 Children aged <5 years underweight (%) NLIS_312244 Published 2000
## 12 Children aged <5 years underweight (%) NLIS_312852 Published 2006
## 15 Children aged <5 years underweight (%) NLIS_313106 Published 2004
## WHO.region Country Sex Display.Value Numeric Low
## 4 Eastern Mediterranean Afghanistan Female 33.0 33.0 NA
## 5 Africa Cameroon Male 20.1 20.1 NA
## 8 Africa Algeria Male 3.7 3.7 NA
## 9 Europe Azerbaijan Male 13.2 13.2 NA
## 12 Eastern Mediterranean Somalia Female 31.3 31.3 NA
## 15 Africa South Africa Male 13.6 13.6 NA
## High Comments
## 4 NA NA
## 5 NA NA
## 8 NA NA
## 9 NA NA
## 12 NA NA
## 15 NA NAPlot percent hungry versus time
lm1 <- lm(hunger$Numeric ~ hunger$Year)
plot(hunger$Year,hunger$Numeric,pch=19,col="blue")
Add the linear model
lm1 <- lm(hunger$Numeric ~ hunger$Year)
plot(hunger$Year,hunger$Numeric,pch=19,col="blue")
lines(hunger$Year,lm1$fitted,lwd=3,col="darkgrey")
Color by male/female
plot(hunger$Year,hunger$Numeric,pch=19)
points(hunger$Year,hunger$Numeric,pch=19,col=((hunger$Sex=="Male")*1+1))
Color by male/female
lmM <- lm(hunger$Numeric[hunger$Sex=="Male"] ~ hunger$Year[hunger$Sex=="Male"])
lmF <- lm(hunger$Numeric[hunger$Sex=="Female"] ~ hunger$Year[hunger$Sex=="Female"])
plot(hunger$Year,hunger$Numeric,pch=19)
points(hunger$Year,hunger$Numeric,pch=19,col=((hunger$Sex=="Male")*1+1))
lines(hunger$Year[hunger$Sex=="Male"],lmM$fitted,col="black",lwd=3)
lines(hunger$Year[hunger$Sex=="Female"],lmF$fitted,col="red",lwd=3)
Two lines, same slope in R
lmBoth <- lm(hunger$Numeric ~ hunger$Year + hunger$Sex)
plot(hunger$Year,hunger$Numeric,pch=19)
points(hunger$Year,hunger$Numeric,pch=19,col=((hunger$Sex=="Male")*1+1))
abline(c(lmBoth$coeff[1],lmBoth$coeff[2]),col="red",lwd=3)
abline(c(lmBoth$coeff[1] + lmBoth$coeff[3],lmBoth$coeff[2] ),col="black",lwd=3)
Two lines, different slopes in R
lmBoth <- lm(hunger$Numeric ~ hunger$Year + hunger$Sex + hunger$Sex*hunger$Year)
plot(hunger$Year,hunger$Numeric,pch=19)
points(hunger$Year,hunger$Numeric,pch=19,col=((hunger$Sex=="Male")*1+1))
abline(c(lmBoth$coeff[1],lmBoth$coeff[2]),col="red",lwd=3)
abline(c(lmBoth$coeff[1] + lmBoth$coeff[3],lmBoth$coeff[2] +lmBoth$coeff[4]),col="black",lwd=3)
Two lines, different slopes in R
summary(lmBoth)##
## Call:
## lm(formula = hunger$Numeric ~ hunger$Year + hunger$Sex + hunger$Sex *
## hunger$Year)
##
## Residuals:
## Min 1Q Median 3Q Max
## -25.801 -11.147 -1.753 7.213 46.225
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 595.83544 153.02128 3.894 0.000105 ***
## hunger$Year -0.28958 0.07642 -3.790 0.000159 ***
## hunger$SexMale 64.74249 216.40478 0.299 0.764867
## hunger$Year:hunger$SexMale -0.03140 0.10807 -0.291 0.771454
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 13.05 on 1060 degrees of freedom
## Multiple R-squared: 0.03413, Adjusted R-squared: 0.03139
## F-statistic: 12.48 on 3 and 1060 DF, p-value: 5.018e-08Session information
Here is the session information
devtools::session_info()## setting value
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## language (EN)
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