bSplinesData: Centile curves using B-splines compared to noise data

In quantCurves: Estimate Quantiles Curves

Centile curves using B-splines compared to noise data

Description

Centile curves using B-splines compared to noise data

Usage

bSplinesData(
  x,
  y,
  lambdas,
  data,
  cents = c(0.03, 0.25, 0.5, 0.75, 0.97),
  leg = FALSE
)

Arguments

x	the explanatory variable - numeric
y	the response variable - numeric
lambdas	to be set by user. Can be a vector or a single numeric value. Tunes the tradeoff between the goodness of fit and the regularity of the spline - numeric value or numeric vector
data	the noise data to be compared to
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))
leg	Boolean. Should the legend be desplayed (TRUE) or not (FALSE).

Value

Plots centile curves with B-splines of different differential orders (d) and displays them on the same figure as the noise data

Examples

#create a sample data frame
sample<-data.frame(sample(30:42,30,rep=TRUE),sample(800:5000,30,rep=TRUE))
colnames(sample)<-c("Gestational Age in weeks","Weight in gramms")
x<-sample$`Gestational Age in weeks`
y<-sample$`Weight in gramms`
abnormal<-data.frame(sample(30:42,6,rep=TRUE),sample(800:5000,6,rep=TRUE))
colnames(abnormal)<-c("Gestational Age in weeks","Weight in gramms")
bSplinesData(x,y,lambdas=1,abnormal)

quantCurves documentation built on March 18, 2022, 5:11 p.m.