simulatedpoissondata {CalibrationCurves} | R Documentation |
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
[Package CalibrationCurves version 2.0.3 Index]