survPen (version 2.0.1)

smooth.cons.integral: Design matrix of penalized splines in a smooth.spec object for Gauss-Legendre quadrature

Description

Almost identical to smooth.cons. This version is dedicated to Gauss-Legendre quadrature. Here, the sum-to-zero constraints must be specified so that they correspond to the ones that were calculated with the initial dataset.

Usage

smooth.cons.integral(
  term,
  knots,
  df,
  by = NULL,
  option,
  data.spec,
  Z.smf,
  Z.tensor,
  Z.tint,
  name
)

Value

design matrix

Arguments

term

Vector of strings that generally comes from the value "term" of a smooth.spec object

knots

List of numeric vectors that specifies the knots of the splines (including boundaries).

df

Degrees of freedom : numeric vector that indicates the number of knots desired for each covariate.

by

numeric or factor variable in order to define a varying coefficient smooth; default is NULL.

option

"smf", "tensor" or "tint".

data.spec

data frame that represents the environment from which the covariate values and knots are to be calculated; default is NULL.

Z.smf

List of matrices that represents the sum-to-zero constraint to apply for smf splines.

Z.tensor

List of matrices that represents the sum-to-zero constraint to apply for tensor splines.

Z.tint

List of matrices that represents the sum-to-zero constraint to apply for tint splines.

name

simplified name of the smooth.spec call.

Examples

library(survPen)

# standard spline of time with 4 knots (so we get a design matrix with 3 columns 
# because of centering constraint)

data <- data.frame(time=seq(0,5,length=100))

# retrieving sum-to-zero constraint matrices
Z.smf <- smooth.cons("time",knots=list(c(0,1,3,5)),df=4,option="smf",
data.spec=data,name="smf(time)")$Z.smf

# constructing the design matrices for Gauss-Legendre quadrature
smooth.c.int <- smooth.cons.integral("time",knots=list(c(0,1,3,5)),df=4,option="smf",data.spec=data,
name="smf(time)",Z.smf=Z.smf,Z.tensor=NULL,Z.tint=NULL)