genSpline {simstudy}

Generate spline curves

Description

Generate spline curves

Usage

genSpline(
  dt,
  newvar,
  predictor,
  theta,
  knots = c(0.25, 0.5, 0.75),
  degree = 3,
  newrange = NULL,
  noise.var = 0
)

Arguments

dt

data.table that will be modified

newvar

Name of new variable to be created

predictor

Name of field in old data.table that is predicting new value

theta

A vector or matrix of values between 0 and 1. Each column of the matrix represents the weights/coefficients that will be applied to the basis functions determined by the knots and degree. Each column of theta represents a separate spline curve.

knots

A vector of values between 0 and 1, specifying quantile cut-points for splines. Defaults to c(0.25, 0.50, 0.75).

degree

Integer specifying polynomial degree of curvature.

newrange

Range of the spline function , specified as a string with two values separated by a semi-colon. The first value represents the minimum, and the second value represents the maximum. Defaults to NULL, which sets the range to be between 0 and 1.

noise.var

Add to normally distributed noise to observation - where mean is value of spline curve.

Value

A modified data.table with an added column named newvar.

Examples

ddef <- defData(varname = "age", formula = "0;1", dist = "uniform")

theta1 <- c(0.1, 0.8, 0.6, 0.4, 0.6, 0.9, 0.9)
knots <- c(0.25, 0.5, 0.75)

viewSplines(knots = knots, theta = theta1, degree = 3)

set.seed(234)
dt <- genData(1000, ddef)

dt <- genSpline(
  dt = dt, newvar = "weight",
  predictor = "age", theta = theta1,
  knots = knots, degree = 3,
  noise.var = .025
)

dt