spline_funs: Helpful closures

In sctyner/dprocsim: Distribution Estimation Accounting for Prior Uncertainty in a Bayesian Analysis

Description

Create linear approximation or spline approximation functions from a given set of points.

Usage

spline_funs(clicks, xrange, spar = 0.5)

linear_funs(clicks, xrange)

mean_funs(upper, lower, meth)

Arguments

clicks	Clicked data
xrange	Vector of length 2 giving the minimum and maximum domain values for the spline function.
spar	Numeric. Value of the smoothing parameter (between 0-1) to be passed to smooth.spline().
upper	Data frame. The upper bound of points for the cdf
lower	Data frame. The lower bound of points for the cdf
meth	Character. Approximation method. either "splines" for smooth.spline() or "linear" for approxfun()

Functions

• linear_funs: Linear function closure

• mean_funs: Mean function closure

Examples

## Not run: 
splinefuns(clicks)(seq(xmin, xmax, length.out = 100))

## End(Not run)

sctyner/dprocsim documentation built on June 30, 2019, 11:51 a.m.