*Check them out.*

Here are thirty **homoscedastic** ones:

`> homo.wieners <- array(0, c(100, 30))`

> for (j in 1:30) {

for (i in 2:length(homo.wieners)) **{**

homo.wieners[i,j] <- homo.wieners[ i - 1, j] + rnorm(1)** }**}

`> for (j in 1:30) {`

` plot`

**(** homo.wieners[,j],

type = "l", col = rgb(.1,.1,.1,.6),

ylab="", xlab="", ylim=c(-25,25)** )**;

par(new=TRUE) }

Here’s just the meat of that wiener, in case the `for`

loops or window dressing were confusing.

`homo.wiener[i] <- homo.wiener[ i - 1] + rnorm(1)`

I also made you some **heteroskedastic** wieners.

`# same for-loop encasing. ∀ j make wieners; ∀j plot wieners`

> hetero.wiener[i] <- hetero.wiener[ i-1 ] + rnorm(1, sd=rpois(1,1) )

It wasn’t even that hard — here are some **autoregressive(1)** wieners as well.

`# same for-loop encasing. ∀j make wieners; ∀j plot wieners`

> ar.wiener[i] <- ar.wiener[i-1]*.9 + rnorm(1)

Other types of wieners:

`a.wiener[i-1] + rnorm(1) * a.wiener[i-1] + rnorm(1)`

`central.limit.wiener[i-1] + sum( runif(17, min=-1) )`

`cauchy.wiener[i-1] + rcauchy(1) #leaping lizards!`

`random.eruption.wiener[i-1] + rnorm(1) * random.eruption.wiener[i-1] + rnorm(1)`

`non.markov.wiener[i-1] + non.markov.wiener[i-2] + rnorm(1)`

`the.wiener.that.never.forgets[i] <- cumsum( the.wiener.that.never.forgets) + rnorm(1)`

`non.wiener[i] <- rnorm(1)`

`moving.average.3.wiener[i] <- .6 * rnorm(n=1,sd=1) + .1 * rnorm(n=1,sd=50) + .3 * rnorm(n=1, mean=-3,sd=17)`

`2d.wiener <- array(0, c`

**(**2, 100**)**);

ifelse( runif(1) > .5**,**2d.wiener[1,i] <- 2d.wiener[1,i-1] + rnorm(1)

&& 2d.wiener[2,i] <- 2d.wiener[2,i-1]**,**2d.wiener[2,i] <- 2d.wiener[2,i-1] + rnorm(1)

&& 2d.wiener[ 1,i] <- 2d.wiener[1,i-1]

`131d.wiener <- array(0, c`

**(**131, 100**)**); ....`cross.pollinated.wiener`

- contrasting
`sd=1,2,3`

of`homo.wieners`

What really stands out in writing about these wieners after playing around with them, is that **logically interesting** wieners don’t always make for **visually interesting** wieners.

There are lots of games you can play with these wieners. Some of my favourites are:

- trying to make the wieners look like stock prices

(I thought`sqrt(rcauchy(1))`

errors with a little autocorrelation looked pretty good) - trying to make them look like heart monitors

(actually really hard, as they’re composed of PQRST waves)

Also it’s pretty hard to tell which wieners are interesting just from looking at the codes above. I guess you will just have to go mess around with some wieners yourself. Some of them will surprise you and not do anything; that’s instructive as well.

**VOICE OF GOD: WHAT’S UP. I AM THAT I AM. I DECLARE THAT THE WORD ‘WIENER’ IS OBJECTIVELY FUNNY. THAT’S ALL FOR NOW. SEE YOU WEDNESDAY THE 17TH.**