smoothSurv (version 2.0.1)

eval.Gspline: Evaluate a G-spline in a grid of values

Description

This function computes values of $$f(x) = \sum_{j=1}^g c_j \varphi_{\mu_j, \sigma_j^2}(x)$$ in a grid of \(x\) values.

In above expression, \(\varphi_{\mu_j, \sigma_j^2}(x)\) denotes a density of \(N(\mu_j, \sigma_j^2)\).

Usage

eval.Gspline(Gspline, grid)

Arguments

Gspline

A data frame with at least three columns named ``Knot'', ``SD basis'' and ``c coef.'' which determine \(\mu_1, \dots,\mu_g\), \(\sigma_1, \dots, \sigma_g\) and \(c_1,\dots, c_g\). Data.frame with such properties can be found e.g. as spline component of the resulting object returned by functions smoothSurvReg and minPenalty.

grid

A numeric vector giving the grid of \(x\) values at which the G-spline is to be evaluated.

Value

A data.frame with columns named ``x'' (grid) and ``y'' (G-spline values).

Examples

# NOT RUN {
  spline <- minPenalty(knots=seq(-4.2, 4.2, by=0.3), sdspline=0.2, difforder=3)$spline
  values <- eval.Gspline(spline, seq(-4.5, 4.5, by=0.05))
  plot(values, type="l", bty="n", lwd=3)
# }