Generate spline curves

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
#> id age weight #> 1: 1 0.74562000 0.5520232 #> 2: 2 0.78171242 1.0489039 #> 3: 3 0.02003711 0.3503075 #> 4: 4 0.77608539 0.3605262 #> 5: 5 0.06691009 0.3618022 #> --- #> 996: 996 0.65790837 0.5771583 #> 997: 997 0.32153055 0.1728941 #> 998: 998 0.07071976 0.4556826 #> 999: 999 0.36119713 0.7547905 #> 1000: 1000 0.13465554 0.6626064