CalibrationCurves (version 2.0.6)

simulatedpoissondata: Simulated data sets to illustrate the package functionality

Description

Both the traindata and testdata dataframe are synthetically generated data sets to illustrate the functionality of the package. The traindata has 5000 observations and the testdata has 1000 observations. The same settings were used to generate both data sets.

Usage

data(poissontraindata)
  data(poissontestdata)

Format

y

the poisson distributed outcome variable

x1

covariate 1

x2

covariate 2

x3

covariate 3

x4

covariate 4

x5

covariate 5

Details

See the examples for how the data sets were generated.

Examples

  # The data sets were generated as follows
  library(MASS)
  library(magrittr)
  ScaleRange <- function(x, xmin = -1, xmax = 1) {
  xRange = range(x)
  (x - xRange[1]) / diff(xRange) * (xmax - xmin) + xmin
  }

  set.seed(144)
  p    = 5
  N    = 1e6
  n    = 5e3
  nOOS = 1e3
  S    = matrix(NA, 5, 5)
  rho  = c(0.025, 0, 0, 0.05, 0.075, 0, 0, 0.025, 0, 0)
  S[upper.tri(S)] = rho
  S[lower.tri(S)] = t(S)[lower.tri(S)]
  diag(S) = 1
  Matrix::isSymmetric(S)


  X  = mvrnorm(N, rep(0, p), Sigma = S, empirical = TRUE)
  X  = apply(X, 2, ScaleRange)
  B  = c(-2.3, 1.5, 2, -1, -2, -1.5)
  mu = poisson()$linkinv(cbind(1, X) %*% B)
  Y  = rpois(N, mu)

  Df = data.frame(Y, X)
  colnames(Df)[-1] %<>% tolower()

  set.seed(2)
  DfS   = Df[sample(1:nrow(Df), n, FALSE), ]
  DfOOS = Df[sample(1:nrow(Df), nOOS, FALSE), ]

  poissontraindata = DfS
  poissontestdata  = DfOOS