ShapeSelectForest (version 1.7)

shapeparams: Shape Parameters

Description

Given the output from the shape function (including the chosen shape, chosen information criteria value ic, vector of fitted values thetab, and corresponding \(\bold{x}\), e.g., years), this routine calculates a set of parameters that describe the behavior of the fitted trajectory.

Usage

shapeparams(shapenum, ic, thetab, x)

Value

shapenum

the shapenum argument

pre.rate

annual rate of decline prior to the primary change point

pre.rate2

annual rate of decline prior to the secondary change point

dist.yr

year of the primary change points

dist2.yr

year of the secondary change points

dist.mag

difference in predicted values before and after primary change events

dist2.mag

difference in predicted values before and after secondary change events

dist.mag2

difference in predicted values before and after primary change points scaled by starting value

dist2.mag2

difference in predicted values before and after secondary change points scaled by starting value

dist.dur

duration of the change event before resuming a downward turn

dist2.dur

duration of the change event before resuming a downward turn

post.rate

annual rate of decline after the end of the primary change event

post2.rate

annual rate of decline after the end of the secondary change event

my.ic

information criteria value for the chosen shape

Arguments

shapenum

A number with the index \(1\) to \(7\).

ic

A \(k\) by \(N\) matrix where the \(i\)th column is the vector of "BIC" or "CIC" values used to choose the best shape for the \(i\)th scatterplot. \(k\) is the number of shapes allowed by the user.

thetab

A \(n\) by \(N\) matrix where the \(i\)th column is the vector of predicted values for the chosen shape for the \(i\)th scatterplot.

x

A \(n\) by \(1\) predictor vector, e.g., years.

Author

Gretchen G. Moisen

References

Moisen, G.G., M. Meyer, T.A. Schroeder, C. Toney, X. Liao, E.A. Freeman, K. Schleeweis. Shape-selection in Landsat time series: A tool for monitoring forest dynamics (In Review). Global Change Biology.

See Also

shape

Examples

if (FALSE) {
	# import the matrix of Landsat signals 
	data("ymat")
	
	# define the predictor vector: the year 1985 to the year 2010
	x <- 1985:2010
 	
	# call the shape routine allowing a double-jump shape using "CIC"
	ans <- shape(x, ymat, "CIC")

	# Example 1: parameters for a flat shape
	flat_id <- which(ans$shape == 1)
	i <- flat_id[1]
	ans_flat <- shapeparams(ans$shape[i], ans$ic[, i], ans$thetab[, i], x)	

	# Example 2: parameters for a one-jump shape
	jp_id <- which(ans$shape == 3)
	i <- jp_id[1]
	ans_jp <- shapeparams(ans$shape[i], ans$ic[, i], ans$thetab[, i], x)	

	# Example 3: parameters for a double-jump shape
	db_id <- which(ans$shape == 7)
	i <- db_id[1]
	ans_db <- shapeparams(ans$shape[i], ans$ic[, i], ans$thetab[, i], x)		
}