impute_gradients

impute gradients from two vectors with x and y coordinates.

This is a shape preserving spline <doi:10.1137/0720057>
which is guaranteed to be monotonic and concave or convex if the
data is monotonic and concave or convex. It does not use any
optimisation and is therefore quick and smoothly converges to a
fixed point in economic dynamics problems including value function
iteration. It also automatically gives the first two derivatives
of the spline and options for determining behaviour when evaluated
outside the interpolation domain.

Stuart Baumann

schumaker

Schumaker Shape-Preserving Spline

Margaryta Klymak

impute_gradients function

<dl><dt>x</dt>
<dd>A vector of x coordinates</dd>
<dt>y</dt>
<dd>A corresponding vector of y coordinates</dd>
<dt>edgeGradients</dt>
<dd>This gives the options of specifing the gradients at either edge of the domain. By default this is c(NA,NA) meaning that the defaults from the original paper are used. If this is set to c(0,NA) for instance this will mean that the left edge gradient is zero and the right edge gradient is as recommended in the original paper. This setting has no impact if a full set of gradients is input.</dd></dl>

Arguments

impute gradients from two vectors with x and y coordinates. — impute_gradients

<dl>

<dt>x</dt>
<dd>A vector of x coordinates</dd>


<dt>y</dt>
<dd>A corresponding vector of y coordinates</dd>


<dt>edgeGradients</dt>
<dd>This gives the options of specifing the gradients at either edge of the domain. By default this is c(NA,NA) meaning that the defaults from the original paper are used. If this is set to c(0,NA) for instance this will mean that the left edge gradient is zero and the right edge gradient is as recommended in the original paper. This setting has no impact if a full set of gradients is input.</dd>

</dl>

impute_gradients: impute gradients from two vectors with x and y coordinates.

Description

Usage

Arguments

References

Examples