# Cumulative distributions - Day 2

## Cumulative distributions and ranks

• We want to make a plot of the cumulative distribution of a function $P(x)$ the frequency of words in a text.

• The cumulative distribution of the frequency is defined such that $P(x)$ is the fraction of words with frequency greater than or equal to $x$.

• If $x$ is the frequency of the most frequent word, usually “the”, then there is exactly one word with frequency greater than or equal to $x$.

Similarly for the second most frequent word, usually “of”, there are two words with frequency greater than or equal: “of” and “the”.

## Cumulative distributions and ranks 1

• In general if we rank the words in descending order then by definition there are n words with frequency greater than or equal than that of the nth most commond word.

Thus the cumulative distribution $P(x)$ is proportional to the rank n of a word.

Then to plot $P(x)$ we only need to plot the ranks as a function of the frequency.

## Cumulative distributions and fractal dimension

• We can analyze the data from Metzler (1992)
		rm(list=ls())

ps$r <- rank(-ps$pSize)

plot(r ~ pSize,data=ps)

plot(log(r)~log(pSize),data=ps)

lm0 <- lm(log(r)~log(pSize),data=ps)

summary(lm0)

abline(lm0)


## Cumulative distributions and fractal dimension 1

• If $B$ is the exponent then $H = 2 - 2B$
		slope0 <- coef(lm0)[2]

2+slope0*2

The patches are persistent because H=1.18 > 0.5

• We need to install the package “car” to test for autocorrelation with the Durbin-Watson statistic. We can do this using the RStudio menu Tools/Install Packages.
		require(car)

dwt(lm0)

we can draw a grid to determine the break point.

		grid()


## Exercise 1

• Split the data in two to obtain two fractal dimensions without correlation

• There is a shorcut for doing this: the package “segmented” fits a broken line and finds the break point.

		require(segmented)

ps$logr <- log(ps$r)
ps$logpSize <- log(ps$pSize)
lm0 <- lm(logr~logpSize,data=ps)
seg <- segmented(lm0, seg.Z = ~logpSize, psi=4)
summary(seg)
slope(seg)


## Exercise 1 (Cont.)

• Let’s do a function to calculate H

• small patches are persistent

		calcH <- function(B) { 2-2*abs(B)}

calcH(.1550) # H = 1.69

Big patches are anti-persistent

		calcH(.9036) # H = 0.19

• What is the breakpoint value in ha? Let’s do another function.

		calcBreak <- function(B) { 0.1*exp(B)*0.65 }

calcBreak(3.35) # 1.85 ha


## Conclusion

• small patches: if they are growing they keep growing, if they are reducing they vanish.

• big patches: if they are growing they will reduce, if they are reducing they will grow.

• Thus big patches are more stable, small patches appear and disappear.

## Exercise 2

• Let’s do the same thing using segmented with the 1985 data: “patch1985.dat”

• We can do a plot with the segmented object

		plot(seg,col="green",xlab="Log Patch Size"
,ylab="Acum Freq")

points(log(r)~log(pSize),data=ps,pch=2,cex=.5)

• We can use the functions:
		summary(seg)
slope(seg)

calcH(0.2676)
calcH(1.26)

calcBreak(2.708) # 0.97 ha


## A different graphic analysis

• Using graphics package “ggplot2”. We need to add both datasets in one data frame
		ps <- read.table("patch1985.dat",header=T)
ps$r <- rank(-ps$pSize)
ps$Year <- "1985" ps1 <- read.table("patch1968.dat",header=T) ps1$r <- rank(-ps1$pSize) ps1$Year <- "1968"

ps <- rbind(ps,ps1)


## Gramar of graphics ggplot2

		require(ggplot2)
ggplot(data=ps,aes(x=pSize,y=r,color=Year))
+geom_point()

p <- ggplot(data=ps,aes(x=log(pSize),y=log(r),
color=Year))+geom_point(aes(shape=Year))


## More questions

• This seems a fragmentation process the frequency of small patches increases but the scaling of big patches seems similar, have big patches different scalings?
		ps$logpSize <- log(ps$pSize)

ps1 <- ps[ps\$logpSize>3.35,]

p + geom_smooth(data=ps1,method="lm")

ggsave("patch_Breaks.png",width=2)

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