predictTensorBF: Predict Missing Values using the Bayesian tensor...

In tensorBF: Bayesian Tensor Factorization

Description

predictTensorBF predicts the missing values in the data Y using the learned model res.

Usage

predictTensorBF(Y, res)

Arguments

Y	is a 3-mode tensor containing missing values as NA's. See function tensorBF for details.
res	the model object returned by the function tensorBF.

Details

If the original data Y contained missing values (NA's), this function predicts them using the model. The predictions are returned in the un-normalized space if res$pre contains appropriate preprocessing information.

Value

A tensor of the same size as Y containing predicted values in place of NA's.

Examples

#Data generation
## Not run: K <- 2
## Not run: X <- matrix(rnorm(20*K),20,K)
## Not run: W <- matrix(rnorm(30*K),30,K)
## Not run: U <- matrix(rnorm(3*K),3,K)
## Not run: Y = 0
## Not run: for(k in 1:K) Y <- Y + outer(outer(X[,k],W[,k]),U[,k])
## Not run:  Y <- Y + array(rnorm(20*30*3,0,0.25),dim=c(20,30,3))

#insert missing values
## Not run: m.inds = sample(prod(dim(Y)),100)
## Not run: Yobs = Y[m.inds]
## Not run: Y[m.inds] = NA

#Run the method with default options and predict missing values
## Not run: res <- tensorBF(Y)
## Not run: pred = predictTensorBF(Y=Y,res=res)
## Not run: plot(Yobs,pred[m.inds],xlab="obs",ylab="pred",main=round(cor(Yobs,pred[m.inds]),2))

tensorBF documentation built on May 1, 2019, 8:39 p.m.