View source: R/Functions_LpS.R
first.step.detect | R Documentation |
Rolling window scheme function for the first step
first.step.detect(
data,
h,
step.size = NULL,
lambda,
mu,
alpha_L = 0.25,
skip = 3,
lambda.1.seq = NULL,
mu.1.seq = NULL,
cv = FALSE,
nfold = NULL,
verbose = FALSE
)
data |
the whole data matrix |
h |
window size |
step.size |
rolling step size, default is NULL. If Null, the step size is 1/4 of the window size |
lambda |
a 2-d vector of tuning parameters for sparse components, available when cv is FALSE |
mu |
a 2-d vector of tuning parameters for low rank components, available when cv is FALSE |
alpha_L |
a numeric value, indicates the size of constraint space of low rank component |
skip |
the number of observations we should skip near the boundaries, default is 3 |
lambda.1.seq |
the sequence of sparse tuning parameter to the left segment, only available when cv is TRUE |
mu.1.seq |
the sequence of low rank tuning, only available for cv is TRUE |
cv |
a boolean argument, indicates whether use cross validation or not |
nfold |
a positive integer, indicates the number of folds of cross validation |
verbose |
if TRUE, then all information for current stage are printed |
A vector which includes all candidate change points selected by rolling window
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