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