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ScatterExamplePlots.R
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### Example scatterplots
### Parameters
fig.ht <- 5.5
fig.wt <- 5
set.seed(2300)
x <- runif(50,
min = 0,
max = 10)
y <- 2*x + 5 + rnorm( length(x), 0, 1)
par( mar = c(5, 4, 4, 4) + 0.1) # DEFAULT: c(5, 4, 4, 2) + 0.1.
plot( y ~ x,
pch = 19,
las = 1,
xlab = "Explanatory var",
ylab = "Response",
main = "Scatterplot A"
)
abline( coef(lm( y ~ x )),
lwd = 2,
col = 2)
text(0, 25,
pos = 4,
"Form: linear\nDirection: positive\nVariation: small")
dev.print(png,
filename = "ScatterExampleA.png",
width = fig.wt,
height = fig.ht,
units = "in",
res = 100)
#########################################
set.seed(2300)
x <- runif(50,
min = 0,
max = 10)
y <- -2*x + 5 + rnorm( length(x), 0, 5) + 25
par( mar = c(5, 4, 2, 2) + 0.1) # DEFAULT: c(5, 4, 4, 2) + 0.1.
plot( y ~ x,
pch = 19,
las = 1,
xlab = "Explanatory var",
ylab = "Response",
main = "Scatterplot B"
)
abline( coef(lm( y ~ x )),
lwd = 2,
col = 2)
text(0, 10,
pos = 4,
"Form: linear\nDirection: negative\nVariation: fairly large")
dev.print(png,
filename = "ScatterExampleB.png",
width = fig.wt,
height = fig.ht,
units = "in",
res = 100)
#########################################
set.seed(2300)
x <- runif(50,
min = 0,
max = 10)
y <- -2*(x-4)^2 + 5 + rnorm( length(x), 0, 5) + 80
par( mar = c(5, 4, 2, 2) + 0.1) # DEFAULT: c(5, 4, 4, 2) + 0.1.
plot( y ~ x,
pch = 19,
las = 1,
xlab = "Explanatory var",
ylab = "Response",
main = "Scatterplot C"
)
mod.curve <- lm( y ~ poly(x,2) )
xnew <- seq(0, 10,
length = 25)
newdata <- data.frame(x = xnew)
ynew <- predict( mod.curve,
newdata = newdata)
lines( ynew ~ xnew,
lwd = 2,
col = 2)
text(0.25, 40,
pos = 4,
"Form: non-linear (curved)\nDirection: (not relevant)\nVariation: small")
dev.print(png,
filename = "ScatterExampleC.png",
width = fig.wt,
height = fig.ht,
units = "in",
res = 100)
#########################################
set.seed(2300)
x <- runif(50,
min = 0,
max = 10)
y <- 2*(x-4)^2 + 5 + rnorm( length(x), 0, 15) + 25
par( mar = c(5, 4, 2, 2) + 0.1) # DEFAULT: c(5, 4, 4, 2) + 0.1.
plot( y ~ x,
pch = 19,
las = 1,
xlab = "Explanatory var",
ylab = "Response",
main = "Scatterplot D"
)
mod.curve <- lm( y ~ poly(x,2) )
xnew <- seq(0, 10,
length = 25)
newdata <- data.frame(x = xnew)
ynew <- predict( mod.curve,
newdata = newdata)
lines( ynew ~ xnew,
lwd = 2,
col = 2)
text(0, 125,
pos = 4,
"Form: non-linear (curved)\nDirection: (not relevant)\nVariation: moderate")
dev.print(png,
filename = "ScatterExampleD.png",
width = fig.wt,
height = fig.ht,
units = "in",
res = 100)
#########################################
set.seed(2300)
set.seed(2300)
x <- runif(50,
min = 0,
max = 10)
y <- 2*(x-5)^3 + 5 + rnorm( length(x), 0, 15) + 250
par( mar = c(5, 4, 2, 2) + 0.1) # DEFAULT: c(5, 4, 4, 2) + 0.1.
plot( y ~ x,
pch = 19,
las = 1,
xlab = "Explanatory var",
ylab = "Response",
main = "Scatterplot E"
)
mod.curve <- lm( y ~ poly(x,3) )
xnew <- seq(0, 10,
length = 25)
newdata <- data.frame(x = xnew)
ynew <- predict( mod.curve,
newdata = newdata)
lines( ynew ~ xnew,
lwd = 2,
col = 2)
text(0, 450,
pos = 4,
"Form: non-linear (curved)\nDirection: (not relevant)\nVariation: small/moderate")
dev.print(png,
filename = "ScatterExampleE.png",
width = fig.wt,
height = fig.ht,
units = "in",
res = 100)
#########################################
set.seed(2300)
x <- runif(50,
min = 0,
max = 10)
y <- runif(50,
min = 0,
max = 5)
par( mar=c(5, 4, 2, 2) + 0.1) # DEFAULT: c(5, 4, 4, 2) + 0.1.
plot( y ~ x,
pch = 19,
las = 1,
ylim = c(0, 6),
xlab = "Explanatory var",
ylab = "Response",
main = "Scatterplot F"
)
mod.curve <- lm( y ~ poly(x, 1) )
xnew <- seq(0, 10,
length = 25)
newdata <- data.frame(x = xnew)
ynew <- predict( mod.curve,
newdata = newdata)
lines( ynew ~ xnew,
lwd = 2,
col = 2)
text(0, 5.75,
"No obvious relationship",
pos = 4)
dev.print(png,
filename = "ScatterExampleF.png",
width = fig.wt,
height = fig.ht,
units = "in",
res = 100)
#########################################
set.seed(2300)
x <- runif(50,
min = 0,
max = 10)
y <- 2*x + 5 + rnorm( length(x), 0, 1*x/2)
par( mar = c(5, 4, 2, 2) + 0.1) # DEFAULT: c(5, 4, 4, 2) + 0.1.
plot( y ~ x,
pch = 19,
las = 1,
ylim = c(5, 25),
xlab = "Explanatory var",
ylab = "Response",
main = "Scatterplot G"
)
mod.curve <- lm( y ~ poly(x, 1) )
xnew <- seq(0, 10,
length = 25)
newdata <- data.frame(x = xnew)
ynew <- predict( mod.curve,
newdata = newdata)
lines( ynew ~ xnew,
lwd = 2,
col = 2)
text(0, 22,
pos = 4,
"Form: linear\nDirection: positive\nVariation: increase as x increases")
dev.print(png,
filename = "ScatterExampleG.png",
width = fig.wt,
height = fig.ht,
units = "in",
res = 100)
#########################################
set.seed(2300)
library(GLMsData)
data(nminer)
par( mar=c(5, 4, 2, 2) + 0.1) # DEFAULT: c(5, 4, 4, 2) + 0.1.
plot( Minerab ~ Eucs,
data = nminer,
pch = 19,
ylim = c(0, 20),
las = 1,
xlab = "Explanatory var",
ylab = "Response",
main = "Scatterplot H"
)
mod.curve <- glm( Minerab ~ Eucs,
data = nminer,
family = poisson(link = "log"))
xnew <- seq(0, 35,
length = 25)
newdata <- data.frame(Eucs = xnew)
ynew <- predict( mod.curve,
newdata = newdata,
type = "response")
lines( ynew ~ xnew,
lwd = 2,
col = 2)
text(0, 18,
pos = 4,
"Form: non-linear (curved)\nDirection: (not relevant)\nVariation: increasing")
dev.print(png,
filename = "ScatterExampleH.png",
width = fig.wt,
height = fig.ht,
units = "in",
res = 100)