tests/testthat/test.create.profile.R

In GabelHub/ProfileIroning: Calculates Likelihood Profiles And Smooths Them

context("Test the optimising function point.profile")

system("rm -r tests/testthat/Profile-Results")
system("rm -r Profile-Results")

library(ProfileIroning)

set.seed(1)

inits <- c(p1 = 3, p2 = 4, p3 = -2, p4 = 2, p5 = 0)

#create data with standard deviation of 1
x.values <- 1:7
y.values <-  9 * x.values^2 - exp(2 * x.values)
sd.y.values <- rep(1,7)

#define initial parameter values
inits <- c(p1 = 3, p2 = 4, p3 = -2, p4 = 2, p5 = 0)

#define cost function that returns the negative log-likelihood
cost_function <- function(parms, x.vals, y.vals, sd.y){
  # restrict the search range to -5 to +5
  if(max(abs(parms)) > 5){
    return(NA)
  }
  with(as.list(c(parms)), {
    res <- p1*4 + p2*x.vals + p3^2*x.vals^2 + p4*sin(x.vals)  - exp(p5*x.vals)
    diff <- sum((res - y.vals)^2/sd.y)
  })
}

#perform model selection
res <- create.profile(which.par = "p1",
                      par.names = inits,
                      range = list(seq(0, 2, 0.2)),
                      fit.fn = cost_function,
                      homedir = getwd(),
                      optim.runs = 2,
                      future.off = TRUE,
                      x.vals = x.values,
                      y.vals = y.values,
                      sd.y = sd.y.values)

test_that("Gives the correct output", {
  expect_equal(as.numeric(round(res[11,2])), 2)

})