Writing and Using Functions

Go back to the declaration and look at the parts:

# "Robust" loss function, for outlier-resistant regression
# Inputs: vector of numbers (x)
# Outputs: vector with x^2 for small entries, |x| for large ones
psi.1 <- function(x) {
  psi <- ifelse(x^2 > 1, 2*abs(x)-1, x^2)
  return(psi)
}

Interfaces: the inputs or arguments; the outputs or return value

Calls other functions ifelse(), abs(), operators ^ and >
could also call other functions we've written

return() says what the output is
alternately, return the last evaluation; I like explicit returns better

Comments: Not required by R, but a Very Good Idea
One-line description of purpose; listing of arguments; listing of outputs

Default values get used if names are missing:

identical(psi.2(z,c=1), psi.2(z))

[1] TRUE

Named arguments can go in any order when explicitly tagged:

identical(psi.2(x=z,c=2), psi.2(c=2,x=z))

[1] TRUE

Solution: Put little sanity checks into the code

# "Robust" loss function, for outlier-resistant regression
# Inputs: vector of numbers (x), scale for crossover (c)
# Outputs: vector with x^2 for small entries, 2c|x|-c^2 for large ones
psi.3 <- function(x,c=1) {
  # Scale should be a single positive number
  stopifnot(length(c) == 1,c>0)
  psi <- ifelse(x^2 > c^2, 2*c*abs(x)-c^2, x^2)
  return(psi)
}

Arguments to stopifnot() are a series of expressions which should all be TRUE; execution halts, with error message, at first FALSE (try it!)

Want to fit the model \[ Y = y_0 N^{a} + \mathrm{noise} \]

Take \( y_0=6611 \) for today

Approximate the derivative of error w.r.t \( a \) and move against it \[ \begin{eqnarray*} MSE(a) & \equiv & \frac{1}{n}\sum_{i=1}^{n}{(Y_i - y_0 N_i^a)^2}\\ MSE^{\prime}(a) & \approx & \frac{MSE(a+h) - MSE(a)}{h}\\ a_{t+1} - a_{t} & \propto & -MSE^{\prime}(a) \end{eqnarray*} \]

An actual first attempt at code:

maximum.iterations <- 100
deriv.step <- 1/1000
step.scale <- 1e-12
stopping.deriv <- 1/100
iteration <- 0
deriv <- Inf
a <- 0.15
while ((iteration < maximum.iterations) && (deriv > stopping.deriv)) {
  iteration <- iteration + 1
  mse.1 <- mean((gmp$pcgmp - 6611*gmp$pop^a)^2)
  mse.2 <- mean((gmp$pcgmp - 6611*gmp$pop^(a+deriv.step))^2)
  deriv <- (mse.2 - mse.1)/deriv.step
  a <- a - step.scale*deriv
}
list(a=a,iterations=iteration,converged=(iteration < maximum.iterations))

$a
[1] 0.1258

$iterations
[1] 58

$converged
[1] TRUE

First attempt, with logic fix:

estimate.scaling.exponent.1 <- function(a) {
  maximum.iterations <- 100
  deriv.step <- 1/1000
  step.scale <- 1e-12
  stopping.deriv <- 1/100
  iteration <- 0
  deriv <- Inf
  while ((iteration < maximum.iterations) && (abs(deriv) > stopping.deriv)) {
    iteration <- iteration + 1
    mse.1 <- mean((gmp$pcgmp - 6611*gmp$pop^a)^2)
    mse.2 <- mean((gmp$pcgmp - 6611*gmp$pop^(a+deriv.step))^2)
    deriv <- (mse.2 - mse.1)/deriv.step
    a <- a - step.scale*deriv
  }
  fit <- list(a=a,iterations=iteration,
    converged=(iteration < maximum.iterations))
  return(fit)
}

Problem: All those magic numbers!

Solution: Make them defaults

estimate.scaling.exponent.2 <- function(a, y0=6611,
  maximum.iterations=100, deriv.step = .001,
  step.scale = 1e-12, stopping.deriv = .01) {
  iteration <- 0
  deriv <- Inf
  while ((iteration < maximum.iterations) && (abs(deriv) > stopping.deriv)) {
    iteration <- iteration + 1
    mse.1 <- mean((gmp$pcgmp - y0*gmp$pop^a)^2)
    mse.2 <- mean((gmp$pcgmp - y0*gmp$pop^(a+deriv.step))^2)
    deriv <- (mse.2 - mse.1)/deriv.step
    a <- a - step.scale*deriv
  }
  fit <- list(a=a,iterations=iteration,
    converged=(iteration < maximum.iterations))
  return(fit)
}

Problem: Why type out the same calculation of the MSE twice?
Solution: Declare a function

estimate.scaling.exponent.3 <- function(a, y0=6611,
  maximum.iterations=100, deriv.step = .001,
  step.scale = 1e-12, stopping.deriv = .01) {
  iteration <- 0
  deriv <- Inf
  mse <- function(a) { mean((gmp$pcgmp - y0*gmp$pop^a)^2) }
  while ((iteration < maximum.iterations) && (abs(deriv) > stopping.deriv)) {
    iteration <- iteration + 1
    deriv <- (mse(a+deriv.step) - mse(a))/deriv.step
    a <- a - step.scale*deriv
  }
  fit <- list(a=a,iterations=iteration,
    converged=(iteration < maximum.iterations))
  return(fit)
}

mse() declared inside the function, so it can see y0, but it's not added to the global environment

Problem: Locked in to using specific columns of gmp; shouldn't have to re-write just to compare two data sets
Solution: More arguments, with defaults

estimate.scaling.exponent.4 <- function(a, y0=6611,
  response=gmp$pcgmp, predictor = gmp$pop,
  maximum.iterations=100, deriv.step = .001,
  step.scale = 1e-12, stopping.deriv = .01) {
  iteration <- 0
  deriv <- Inf
  mse <- function(a) { mean((response - y0*predictor^a)^2) }
  while ((iteration < maximum.iterations) && (abs(deriv) > stopping.deriv)) {
    iteration <- iteration + 1
    deriv <- (mse(a+deriv.step) - mse(a))/deriv.step
    a <- a - step.scale*deriv
  }
  fit <- list(a=a,iterations=iteration,
    converged=(iteration < maximum.iterations))
  return(fit)
}

Respecting the interfaces: We could turn the while() loop into a for() loop, and nothing outside the function would care

estimate.scaling.exponent.5 <- function(a, y0=6611,
  response=gmp$pcgmp, predictor = gmp$pop,
  maximum.iterations=100, deriv.step = .001,
  step.scale = 1e-12, stopping.deriv = .01) {
  mse <- function(a) { mean((response - y0*predictor^a)^2) }
  for (iteration in 1:maximum.iterations) {
    deriv <- (mse(a+deriv.step) - mse(a))/deriv.step
    a <- a - step.scale*deriv
    if (abs(deriv) <= stopping.deriv) { break() }
  }
  fit <- list(a=a,iterations=iteration,
    converged=(iteration < maximum.iterations))
  return(fit)
}