swissLMs.R

makelms <- function(){
  # Store the coefficient of linear models with different independent variables
  cf <- c(coef(lm(Fertility ~ Agriculture, swiss))[2], 
          coef(lm(Fertility ~ Agriculture + Catholic,swiss))[2],
          coef(lm(Fertility ~ Agriculture + Catholic + Education,swiss))[2],
          coef(lm(Fertility ~ Agriculture + Catholic + Education + Examination,swiss))[2],
          coef(lm(Fertility ~ Agriculture + Catholic + Education + Examination +Infant.Mortality, swiss))[2])
  print(cf)
}

# Regressor generation process 1.
rgp1 <- function(){
  print("Processing. Please wait.")
  # number of samples per simulation
  n <- 100
  # number of simulations
  nosim <- 1000
  # set seed for reproducability
  set.seed(4321)
  # Point A:
  x1 <- rnorm(n)
  x2 <- rnorm(n)
  x3 <- rnorm(n)
  # Point B:
  betas <- sapply(1 : nosim, function(i)makelms(x1, x2, x3))
  round(apply(betas, 1, var), 5)
}

# Regressor generation process 2.
rgp2 <- function(){
  print("Processing. Please wait.")
  # number of samples per simulation
  n <- 100
  # number of simulations
  nosim <- 1000
  # set seed for reproducability
  set.seed(4321)
  # Point C:
  x1 <- rnorm(n)
  x2 <- x1/sqrt(2) + rnorm(n) /sqrt(2)
  x3 <- x1 * 0.95 + rnorm(n) * sqrt(1 - 0.95^2)
  # Point D:
  betas <- sapply(1 : nosim, function(i)makelms(x1, x2, x3))
  round(apply(betas, 1, var), 5)
}