xnet (version 0.1.11)

predict.tskrr: predict method for tskrr fits

Description

Obtains the predictions from a tskrr model for new data. To get the predictions on the training data, use the function fitted or set both k and g to NULL.

Usage

# S3 method for tskrr
predict(object, k = NULL, g = NULL, testdim = TRUE, ...)
# S4 method for tskrr
predict(object, k = NULL, g = NULL, testdim = TRUE, ...)

Arguments

object

an object of class tskrr.

k

a new K matrix or NULL. if NULL, the fitted values on the training data are returned.

g

a new G matrix or NULL. If NULL, K is used for both.

testdim

a logical value indicating whether the dimensions should be checked prior to the calculation. You can set this to FALSE but you might get more obscure errors if dimensions don't match.

...

arguments passed to or from other methods

Value

a matrix with predicted values.

Warning

This function is changed in version 0.1.9 so it's more consistent in how it expects the K and G matrices to be ordered. Up to version 0.1.8 the new vertices should be on the rows for the K matrix and on the columns for the G matrix. This lead to confusion.

If you're using old code, you'll get an error pointing this out. You need to transpose the G matrix in the old code to make it work with the new version.

Details

Predictions can be calculated between new vertices and the vertices used to train the model, between new sets of vertices, or both. Which predictions are given, depends on the kernel matrices passed to the function.

In any case, both the K and G matrix need the kernel values for every combination of the new vertices and the vertices used to train the model. This is illustrated for both homogeneous and heterogeneous networks in the examples.

To predict the links between a new set of vertices and the training vertices, you need to provide the kernel matrix for either the K or the G set of vertices. If you want to predict the mutual links between two new sets of vertices, you have to provide both the K and the G matrix. This is particularly important for homogeneous networks: if you only supply the k argument, you will get predictions for the links between the new vertices and the vertices on which the model is trained. So in order to get the mutual links between the new vertices, you need to provide the kernel matrix as the value for both the k and the g argument.

See Also

tskrr and tskrrTune for fitting the models.

Examples

# NOT RUN {
## Predictions for homogeneous networks

data(proteinInteraction)

idnew <- sample(nrow(Kmat_y2h_sc), 20)

trainY <- proteinInteraction[-idnew,-idnew]
trainK <- Kmat_y2h_sc[-idnew,-idnew]

testK <- Kmat_y2h_sc[idnew, - idnew]

mod <- tskrr(trainY, trainK, lambda = 0.1)
# Predict interaction between test vertices
predict(mod, testK, testK)

# Predict interaction between test and train vertices
predict(mod, testK)
predict(mod, g = testK)

## Predictions for heterogeneous networks
data("drugtarget")

idnewK <- sample(nrow(targetSim), 10)
idnewG <- sample(ncol(drugSim), 10)

trainY <- drugTargetInteraction[-idnewK, -idnewG]
trainK <- targetSim[-idnewK, -idnewK]
trainG <- drugSim[-idnewG, -idnewG]

testK <- targetSim[idnewK, -idnewK]
testG <- drugSim[idnewG, -idnewG]

mod <- tskrr(trainY, trainK, trainG, lambda = 0.01)

# Predictions for new targets on drugs in model
predict(mod, testK)
# Predictions for new drugs on targets in model
predict(mod, g = testG)
# Predictions for new drugs and targets
predict(mod, testK, testG)

# }