spliunes {BoomSpikeSlab}  R Documentation 
Spline basis expansions of a continuous variable.
BsplineBasis(x, knots = NULL, numknots = 3) MsplineBasis(x, knots = NULL, numknots = 3) IsplineBasis(x, knots = NULL, numknots = 3) ## S3 method for class 'SplineBasis' knots(Fn, ...)
x 
A numeric vector to be expanded. 
knots 
A numeric vector of knots defining the expansion. The
smallest and largest elements in 
numknots 
If the knot vector is 
Fn 
A spline basis matrix. 
... 
Unused, but required to match the signature of the

Bsplines are the basis most commonly used for additive regression models.
Msplines are an alternative to Bsplines, but are rarely used.
Isplines are integrated Msplines. These are monotonic functions, which is useful in monotonic regression problems. If all regression coefficients are positive then the resulting function is nondecreasing.
XsplineBasis
returns a matrix formed by the spline basis
expansion of x
.
knots(Fn)
returns the knots
attribute of Fn
,
which might be useful in a second call to the basis expansion
function.
Steven L. Scott
Bsplines are described in deBoor (2001), "A Practical Guide to Splines". Springer.
Msplines and Isplines are reviewed by Ramsay (1988), Statistical Science pp. 425461.
# Plot the Bspline basis for x with knots determined by 3 quantiles. x < sort(rnorm(1000)) basis < BsplineBasis(x, numknots=3) par(mfrow=c(2,3)) for(i in 1:5) plot(x, basis[, i], type="l") # Plot the Ispline basis for x with the same knots. basis < IsplineBasis(x, numknots=3) par(mfrow=c(2,3)) for(i in 1:5) plot(x, basis[, i], type="l") # Bring you own knots... basis < BsplineBasis(x, knots = quantile(x, c(.2, .5, .8, .9))) par(mfrow=c(2,3)) for(i in 1:6) plot(x, basis[, i], type="l") knots(basis)