bsplines {quantCurves}

Cubic Penalized B-splines quantile regression

Description

Cubic Penalized B-splines quantile regression

Usage

bsplines(
  x,
  y,
  lambdas,
  d = 3,
  cents = c(0.03, 0.25, 0.5, 0.75, 0.97),
  leg = TRUE,
  axes.lab = NULL
)

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

d

differentiation order - 1, 2 or 3. Default is set to d=3.

cents

numeric vector that represents the centiles calculated. Default is set to cents=c(0.03,0.25,0.5,0.75,0.97)).

leg

Boolean. Should the legend be desplayed (TRUE) or not (FALSE).

axes.lab

NULL or c("Nom_axe_X, Nom_axe_Y").

Value

Plots the curves at centiles selected and returns an object of class gcrq.

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)
ages<-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)
bsplines(ages,weights,lambdas=50)