aridneptune - 1 year ago 69

R Question

I am rather new to R and could use some basic help. I'd like to generate sums of two normal random variables (variance = 1 for each) as their means move apart and plot the results. The basic idea: if the means are sufficiently far apart, the distribution will be bimodal. Here's the code I'm trying:

`x <- seq(-3, 3, length=500)`

for(i in seq(0, 3, 0.25)) {

y <- dnorm(x, mean=0-i, sd=1)

z <- dnorm(x, mean=0+i, sd=1)

plot(x,y+z, type="l", xlim=c(-3,3))

}

Several questions:

- Are there better ways to do this?
- I'm only getting one PDF on my plot. How can I put multiple PDFs on the same plot?

Thank you in advance!

Recommended for you: Get network issues from **WhatsUp Gold**. **Not end users.**

Answer Source

It is not difficult to do this using basic R features. We first define a function `f`

to compute the density of mixture normal:

```
## `x` is an evaluation grid
## `dev` is deviation of mean from 0
f <- function (x, dev) {
(dnorm(x, -dev) + dnorm(x, dev)) / 2
}
```

Then we use `sapply`

to loop through various `dev`

to get corresponding density:

```
## `dev` sequence to test
dev <- seq(0, 3, 0.25)
## evaluation grid; extending `c(-1, 1) * max(dev)` by 4 standard deviation
x <- seq(-max(dev) -4, max(dev) + 4, by = 0.1)
## density matrix
X <- sapply (dev, f, x = x)
```

Finally we use `matplot`

for plotting:

```
matplot(x, X, type = "l", lty = 1)
```

**More explanation**

During `sapply`

, `x`

is not changed, while we pick up and try one element of `dev`

each iteration. It is like

```
X <- matrix(0, nrow = length(x), ncol = length(dev))
for (i in 1:length(dev)) X[, i] <- f(x, dev[i])
```

`matplot(x, X)`

will plot columns of `X`

one by one, against `x`

.

Recommended from our users: **Dynamic Network Monitoring from WhatsUp Gold from IPSwitch**. ** Free Download**