TRMF_columns | R Documentation |
Adds a regularization model to TRMF object created by create_TRMF()
to constrain the fitting process of the coefficient matrix.
TRMF_columns(obj,
reg_type = c("l2", "nnls", "constrain", "interval", "none"), lambda = 0.0001,mu0=NULL)
obj |
TRMF object created by |
reg_type |
regularization type to apply when fitting TRMF model. |
lambda |
L2 regularization parameter used for all regularization types. Can be a single value, a vector, or a matrix. If not a scalar, the dimension must match the number of rows of |
mu0 |
The prior value for matrix coefficient. Can be a single value or vector. If not a scalar, the dimension must match the number of rows of |
This function doesn't do any computations, it just sets up regularization parameters for the coefficient matrix. This function should only be called once on a TRMF object. If called twice, it will overwrite previous model with a warning. In addition to the possible constraints, a possible L2 regularization term is added to the fit. The regularization term for each column i
is (Fm_i - \mu_0)^TP(Fm_i - \mu_0)
where P = \lambda I
or P = diag(\lambda)
or P = \lambda
depending on the size of provided lambda. A nonzero value for lambda
is recommended to ensure stability of fit.
Returns an updated object of class TRMF.
Chad Hammerquist
Yu, Hsiang-Fu, Nikhil Rao, and Inderjit S. Dhillon. "High-dimensional time series prediction with missing values." arXiv preprint arXiv:1509.08333 (2015).
train.TRMF
, create_TRMF
, TRMF_trend
# create test data
xm = poly(x = (-10:10)/10,degree=4)
fm = matrix(abs(rnorm(40)),4,10)
Am = xm%*%fm+rnorm(210,0,.2)
# create model
obj = create_TRMF(Am)
obj = TRMF_columns(obj,reg_type ="nnls")
out = train(obj)
plot(out)
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.