ShapeSelectForest-package: Shape Selection for Landsat Time Series of Forest Dynamics

Description

Given a scatterplot of $(x_i, y_i)$, $i = 1,\ldots,n$, where $\bold{x}$ could be a vector of years and $\bold{y}$ could be a vector of Landsat signals, constrained least-squares spline fits are obtained for the following shapes:

1. flat
2. decreasing
3. one-jump, i.e., decreasing, jump up, decreasing
4. inverted vee (increasing then decreasing)
5. vee (decreasing then increasing)
6. linear increasing
7. double-jump, i.e., decreasing, jump up, decreasing, jump up, decreasing.

The shape with the smallest information criterion may be considered a "best" fit. This shape-selection problem was motivated by a need to identify types of disturbances to areas of forest, given Landsat signals over a number of years. The satellite signal is constant or slowly decreasing for a healthy forest, with a jump upward in the signal caused by mass destruction of trees.

The main routine to select the shape for a scatterplot is "shape". See shape for more details.

Arguments

Details

Package:

ShapeSelectForest

Type:

Package

Version:

1.2

Date:

2015-12-25

License:

GPL (>= 2)

References

Meyer, M. C. and Woodroofe M (2000) On the Degrees of Freedom in Shape-Restricted Regression. The Annals of Statistics 28, 1083--1104.

Meyer, M. C. (2013a) Semi-parametric additive constrained regression. Journal of Nonparametric Statistics 25(3), 715.

Meyer, M. C. (2013b) A simple new algorithm for quadratic programming with applications in statistics. Communications in Statistics 42(5), 1126--1139.

Liao, X. and M. C. Meyer (2014) coneproj: An R package for the primal or dual cone projections with routines for constrained regression. Journal of Statistical Software 61(12), 1--22.