Kartheek Palepu - 1 year ago 60

R Question

I have written the code below for minimization of error by changing the value of alpha (using iteration method).

`set.seed(16)`

npoints = 10000

Y = round(runif(npoints), 3)

OY = sample(c(0, 1, 0.5), npoints, replace = T)

minimizeAlpha = function(Y, OY, alpha) {

PY = alpha*Y

error = OY - PY

squaredError = sapply(error, function(x) x*x)

sse = sum(squaredError)

return(sse)

}

# # Iterate for 10000 values

alphas = seq(0.0001, 1, 0.0001)

sse = sapply(alphas, function(x) minimizeAlpha(Y, OY, x))

print(alphas[sse == min(sse)])

I have used

`sapply`

`Bisection`

Any help is appreciated.

Answer Source

Replacing `sapply`

instead of `for`

isn’t more efficient, that’s a misconception. It’s merely often simpler code.

However, you can actually take advantage of vectorisation in your code — and that *would* be faster.

For instance, `sapply(error, function(x) x*x)`

can simply be replaced by `x * x`

. The sum of squared errors of numbers in R is thus simply `sum((OY - PY) ** 2)`

.

Your whole function thus boils down to:

```
minimizeAlpha = function(Y, OY, alpha)
sum((OY - alpha * Y) ** 2)
```

This should be more efficient — but first and foremost it’s better code and more readable.