# Shape Parameters 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. ```r shapeparams(shapenum, ic, thetab, x) ``` ## 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. ## Returns - **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 ## 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. ## Author(s) Gretchen G. Moisen ## See Also `shape` ## Examples ```r ## Not run: # 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) ## End(Not run) ```

shapeparams function