data <- scan(what = list(type=0,hrskey=0,hrspain=0), multi.line=F)
            1       60                 85
            1       72                 95
            1       61                 69
            1       50                 58
            2       54                 41
            2       68                 74
            2       66                 71
            2       59                 52
            3       56                 41
            3       56                 34
            3       55                 50
            3       51                 40

data
     
keyboard <- data.frame(data)
rm(data)

keyboard$type  <- as.factor(keyboard$type)

 # looking only at the response and factor

plot(keyboard$type,keyboard$hrspain,ylab="Pain",xlab="Type",main="Keyboarding by type")


 # look at the data

plot(keyboard$hrskey,keyboard$hrspain,pch=as.numeric(keyboard$type),
      col=as.numeric(keyboard$type),ylab="Pain",xlab="Keyboarding hours")
legend("bottomright",legend=c("Type 1","Type 2","Type 3"),levels(keyboard$type),pch=1:3,col=1:3)
title("Keyboarding Study")


 # standard ANCOVA assuming equal slopes

keyboard.lm1 <- lm(hrspain ~ hrskey +type , data=keyboard)
anova(keyboard.lm1)
summary(keyboard.lm1)


 # another plot of the data with fitted parallel lines

plot(keyboard$hrskey,keyboard$hrspain,pch=as.numeric(keyboard$type),
      col=as.numeric(keyboard$type),ylab="Pain",xlab="Keyboarding hours")
legend("bottomright",legend=c("Type 1","Type 2","Type 3"),levels(keyboard$type),pch=1:3,col=1:3)
title("Keyboarding Study with fitted parallel lines")

abline(-33.81, 1.82, lty=1, lwd=2, col=1)
abline(-33.81 -19.07, 1.82, lty=1, lwd=2, col=2)
abline(-33.81 -24.13, 1.82, lty=1, lwd=2, col=3)


 # calculate adjustedMeans and standard errors from the effects package

library(effects)
adjustedMeans <- effect("type",keyboard.lm1) 
adjustedMeans
adjustedMeans$se


 # multiple comparisons using the multcomp package

library(multcomp)
summary(glht(keyboard.lm1,linfct=mcp(type="Tukey")))


 # check assumptions

windows()
par(mfrow=c(2,2))  
plot(keyboard.lm1)
par(mfrow=c(1,1))  


 # check equal slopes assumption

keyboard.lm2 <- lm(hrspain ~ hrskey +type +hrskey:type , data=keyboard)
anova(keyboard.lm2)


 # ------------------------------------------------------------------------------
 # one way to think about ANCOVA - not for routine use

keyboard.lm0 <- lm(hrspain ~ hrskey , data=keyboard)

windows()

plot(keyboard$type,keyboard.lm0$residuals,ylab="Residuals",xlab="Type",main="Res plot")

anova(lm(keyboard.lm0$residuals ~ keyboard$type))
 # ------------------------------------------------------------------------------