{"name":"parabolaSlopes","title":"Estimate the slope of an interpolating function using a parabola","pagetitle":"Estimate the slope of an interpolating function using a parabola — parabolaSlopes","aliases":"parabolaSlopes","author":"Norbert Nemec, Institute of Theoretical Physics, University of Regensburg. Translation from Python code by Halldor Bjornsson","keywords":["math","ts","smooth"],"description":"

Returns estimates of the slope of an interpolating function that \n runs through a set of points in the xy-plane.

","usage":"parabolaSlopes(x,y)","arguments":[{"name":"x,y","description":"

coordinates of points defining the interpolating function.

"}],"has_args":true,"examples":"# NOT RUN {\nx <- seq(0,2*pi,by=pi/6)\ny <- sin(x)\n# }\n# NOT RUN {\nparabolaSlopes(x,y)\n# }\n","sections":[],"value":"

Returns an estimate of the slope of the interpolant at (x,y).

","note":"

This function is used as part of the Stineman\ninterpolation function stinterp. \nIt is rarely called directly by the user, \nand checking of x and y must be performed by the calling function.

The parabola method provides a better approximation of the slope for smooth functions\nthan the original method suggested by Stineman (1980), which is provided by\nthe function stinemanSlopes (see the documentation \nof the function stinterp for further information), \nbut it results in higher slopes near abrupt steps or spikes \nand can lead to some overshooting where Stineman's method does not.

","seealso":"

stinterp and stinemanSlopes.

","package":{"package":"stinepack","version":"1.3"}}

Description

Usage

Arguments

Value

See Also

Examples