Description Usage Arguments Value Author(s) References See Also Examples
This is a function that solves the L0 fused problem via the primal dual active set algorithm in sparse condition. It fits a piecewise constant regression model by minimizing the number of breaks in derivative with constraints on the least squares error.
1 | fsfused.ada(y, tau=1, s.max=20, T, eps=0.1)
|
y |
Numeric vector of inputs. |
tau |
Step length for searching the best model, i.e., in the t-th iteration, a model with tau*t knots will be fitted. |
s.max |
The maximum nubmer of knots in the piecewise constant(breaks in the (k+1)-st derivative), default is 20 |
T |
Number of non-zero values in fitted coefficient. |
eps |
Early stop criterion. The algorithm stops when mean squared error is less than eps |
y |
The observed response vector. Useful for plotting and other methods. |
beta |
Fitted value |
v |
Primal coefficient. The indexes of the nonzero values correspond to the locations of the breaks. |
beta.all |
Solution path of fitted value, beta, corresponding to different degrees of freedom. |
df |
A vector giving an unbiased estimate of the degrees of freedom of the fit, i.e., the number of nonzero values in |
Canhong Wen, Xueqin Wang, Yanhe Shen, Aijun Zhang
Wen,C., Wang, X., Shen, Y., and Zhang, A. (2017). "L0 trend filtering", technical report.
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