MIC.splines.basis: Splines basis functions

Description

Creation splines basis for M_splines, I_splines and C_splines

Usage

MIC.splines.basis(x, df = NULL, knots = NULL, boundary.knots = NULL, 
type = "Ms", degree = 3, delta = 0.01, eq.alloc = F)

Value

list(mat=mat,x=x, df=df, knots=knots,boundary.knots=boundary.knots, type=type,degree=degree,delta=delta)

mat: it is a matrix of splines basis
x: the input of covariate
df: the input of number of splines needed
knots: the input of number of knots needed not including the boundary
boundary.knots: the values of boundary knots
type: type of splines function which can be MS IS or Cs
degree: degree of the splines functions
delta: the length of the spans to split the range of covariate

Arguments

x: values of covariate
df: number of splines needed which is equal to knots+degree
knots: number of knots needed which does not include the number of knots at the beginning
boundary.knots: the values of boundary knots, which are usually the minimum and maximum of covariate
type: types of splines basis needed, which can be Ms Is or Cs
degree: degree of the splines function
delta: the length of the spans to split the range of covariate
eq.alloc: indicators of whether the knots are equally allocated

Author

Yili Hong

References

Hong. Y., Y. Duan, W. Q. Meeker, D. L. Stanley, and X. Gu (2014), Statistical Methods for Degradation Data with Dynamic Covariates Information and an Application to Outdoor Weathering Data, Technometrics, DOI: 10.1080/00401706.2014.915891.

Examples

Run this code

MIC.splines.basis(x=runif(20), df = NULL, knots = 3, boundary.knots = NULL, 
    type = "Ms", degree = 4, delta = 0.01, eq.alloc = FALSE)

Run the code above in your browser using DataLab