# interpSpline

0th

Percentile

##### Create an Interpolation Spline

Create an interpolation spline, either from x and y vectors (default method), or from a formula / data.frame combination (formula method).

Keywords
models
##### Usage
interpSpline(obj1, obj2, bSpline = FALSE, period = NULL,
ord = 4L,
na.action = na.fail, sparse = FALSE)
##### Arguments
obj1

either a numeric vector of x values or a formula.

obj2

if obj1 is numeric this should be a numeric vector of the same length. If obj1 is a formula this can be an optional data frame in which to evaluate the names in the formula.

bSpline

if TRUE the b-spline representation is returned, otherwise the piecewise polynomial representation is returned. Defaults to FALSE.

period

an optional positive numeric value giving a period for a periodic interpolation spline.

ord

an integer specifying the spline order, the number of coefficients per interval. $ord = d+1$ where $d$ is the degree polynomial degree. Currently, only cubic splines (ord = 4) are implemented.

na.action

a optional function which indicates what should happen when the data contain NAs. The default action (na.omit) is to omit any incomplete observations. The alternative action na.fail causes interpSpline to print an error message and terminate if there are any incomplete observations.

sparse

logical passed to the underlying splineDesign. If true, saves memory and is faster when there are more than a few hundred points.

##### Value

An object that inherits from (S3) class spline. The object can be in the B-spline representation, in which case it will be of class nbSpline for natural B-spline, or in the piecewise polynomial representation, in which case it will be of class npolySpline.

splineKnots, splineOrder, periodicSpline.
library(splines) # NOT RUN { <!-- % tests also in ../tests/spline-tst.R --> # } # NOT RUN { require(graphics); require(stats) ispl <- interpSpline( women$height, women$weight ) ispl2 <- interpSpline( weight ~ height, women ) # ispl and ispl2 should be the same plot( predict( ispl, seq( 55, 75, length.out = 51 ) ), type = "l" ) points( women$height, women$weight ) plot( ispl ) # plots over the range of the knots points( women$height, women$weight ) splineKnots( ispl ) # }