bsplines: Cubic B-Splines with all three order Laplacian diffentiation

In amouchot/nparamEstim: Nonparametric centile curves estimation

Description

Cubic B-Splines with all three order Laplacian diffentiation

Usage

bsplines(x, y, lambdas, cents = c(0.03, 0.25, 0.5, 0.75, 0.97))

Arguments

x	the explanatory variable - numeric
y	the response variable - numeric
lambdas	tunes the tradeoff between the goodness of fit and the regularity of the spline - numeric value or numeric vector
cents	A numeric vector that represents the centiles calculated. Default is set to cents=c(0.03,0.25,0.5,0.75,0.97) )

Value

Plots all the curves at centiles selected using fonctions splines1, splines2 and splines3 from the same package

Examples

#create a sample data frame
weights=c(500,600,1000,1150,1200,1260,1240,1300,1370,1500,2000,2100,2150,2500,
2800,2900,3050,3200,2980,3000,3300,3100,3200,3600,3500,3700,3900,3900,4000,
4200,3000,4500,4300,4900,4350,3700,4000,5000,4300)
age<-c(30,30,30,31,31,31,32,32,32,33,33,33,34,34,34,35,35,35,36,36,36,
37,37,37,38,38,38,39,39,39,40,40,40,41,41,41,42,42,42)
sample<-data.frame(weights,age)
colnames(sample)<-c("Gestational Age in weeks","Weight in gramms")
x<-sample$`Gestational Age in weeks`
y<-sample$`Weight in gramms`
bsplines(x,y,lambdas=seq(1,50))

amouchot/nparamEstim documentation built on Feb. 6, 2021, 9:16 p.m.