impute_TRMF: Impute missing values in a matrix
In TRMF: Temporally Regularized Matrix Factorization

Description Usage Arguments Details Value Author(s) References See Also Examples

Impute missing values in matrix from a pre-trained TRMF object.

1	impute_TRMF(obj)

obj

a trained TRMF object

Essentially an accessor function. Replaces the missing values in data matrix with values from the fitted TRMF object.

data matrix with missing values imputed

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(rnorm(40),4,10)
Am = xm%*%fm+rnorm(210,0,.2)
Am[sample.int(210,20)] = NA

# create model
obj = create_TRMF(Am)
obj = TRMF_trend(obj,numTS=4,order=2)
out = train(obj)
impute_TRMF(out)