spruce.df = read.csv("SPRUCE.csv")
head(spruce.df)
# Scatterplot Height~BHDiameter
windows()
with(spruce.df, {
layout(matrix(1:2,nr=2))
plot(
Height~BHDiameter,
bg="Blue",
pch=21,
cex=1.2,
xlim=c(0, 1.1*max(BHDiameter)),
ylim=c(0, 1.1*max(Height))
)
})
# Lowess smoother scatter plot using layout and trendscatter()
library(s20x)
layout(matrix(1:3,nr=3,nc=1,byrow=TRUE))
layout.show(3)
trendscatter(Height~BHDiameter,f=0.5, data=spruce.df)
trendscatter(Height~BHDiameter,f=0.6, data=spruce.df)
trendscatter(Height~BHDiameter,f=0.7, data=spruce.df)
### Use lm() or linear model to create obj and scatter plot with abline()
spruce.lm <- lm(Height~BHDiameter, data = spruce.df)
summary(spruce.lm)
windows()
plot(Height~BHDiameter, data = spruce.df)
abline(spruce.lm)
## Task 4
layout(matrix(1:4,nr=2,nc=2,byrow=TRUE))
### First graph
with(spruce.df,
plot(Height~BHDiameter,bg="Blue",pch=21,ylim=c(0,1.1*max(Height)),xlim=c(0,1.1*max(BHDiameter)))
)
abline(spruce.lm)
### Second graph
with(spruce.df,
plot(Height~BHDiameter,bg="Blue",pch=21,ylim=c(0,1.1*max(Height)),xlim=c(0,1.1*max(BHDiameter)))
)
abline(spruce.lm)
### Third graph
with(spruce.df,{
segments(BHDiameter,Height,BHDiameter,yhat)
})
yhat=fitted(spruce.lm)
with(spruce.df,
plot(Height~BHDiameter,bg="Blue",pch=21,ylim=c(0,1.1*max(Height)),xlim=c(0,1.1*max(BHDiameter)))
)
#make nieve model
with(spruce.df, abline(h=mean(Height)))
abline(spruce.lm)
#make the explained deviations (explained by the model)
with(spruce.df, segments(BHDiameter,mean(Height),BHDiameter,yhat,col="Red"))
### Fourth graph
with(spruce.df,
plot(Height~BHDiameter,bg="Blue",pch=21,ylim=c(0,1.1*max(Height)),xlim=c(0,1.1*max(BHDiameter)))
)
with(spruce.df,abline(h=mean(Height)))
with(spruce.df, segments(BHDiameter,Height,BHDiameter,mean(Height),col="Green"))
# right?
with(spruce.df,
plot(Height~BHDiameter,bg="Blue")
)
abline(spruce.lm)
# wrong
obj = lm(Height~BHDiameter, data = spruce.df)
plot(Height~BHDiameter, data = spruce.df)
abline(spruce.lm)
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