impute_tskrr.fit: Impute values based on a two-step kernel ridge regression
In xnet: Two-Step Kernel Ridge Regression for Network Predictions

Description Usage Arguments Details Value See Also Examples

This function provides an interface for the imputation of values based on a tskrr model and is the internal function used by impute_tskrr.

1	impute_tskrr.fit(y, Hk, Hg, naid = NULL, niter, tol, start, verbose)

`y`	a label matrix
`Hk`	a hat matrix for the rows (see also `eigen2hat` on how to calculate them from an eigen decomposition)
`Hg`	a hat matrix for the columns. For homogeneous networks, this should be Hk again.
`naid`	an optional index with the values that have to be imputed, i.e. at which positions you find a `NA` value. It can be a vector with integers or a matrix with `TRUE`/`FALSE` values.
`niter`	an integer giving the maximum number of iterations
`tol`	a numeric value indicating the tolerance for convergence of the algorithm. It is the maximum sum of squared differences between to iteration steps.
`start`	a numeric value indicating the value with which NA's are replaced in the first step of the algorithm. Defaults to 0.
`verbose`	either a logical value, 1 or 2. `1` means "show the number of iterations and the final deviation", `2` means "show the deviation every 10 iterations". A value `TRUE` is read as `1`.

This function is mostly available for internal use. In most cases, it makes much more sense to use impute_tskrr, as that function returns an object one can work with. The function impute_tskrr.fit could be useful when doing simulations or creating fitting algorithms.

a list with two elements:

a matrix y with the imputed values filled in.
a numeric value niter with the amount of iterations

impute_tskrr for the user-level function, and
eigen2hat for conversion of a eigen decomposition to a hat matrix.

data(drugtarget)

K <- eigen(targetSim)
G <- eigen(drugSim)

Hk <- eigen2hat(K$vectors, K$values, lambda = 0.01)
Hg <- eigen2hat(G$vectors, G$values, lambda = 0.05)

drugTargetInteraction[c(3,17,123)] <- NA

res <- impute_tskrr.fit(drugTargetInteraction, Hk, Hg,
                        niter = 1000, tol = 10e-10,
                        start = 0, verbose = FALSE)