Two-Step Kernel Ridge Regression for Network Predictions
convert tskrr models
Create a grid of values for tuning tskrr
Get the dimensions of a tskrr object
drug target interactions for neural receptors
Calculate the hat matrix from an eigen decomposition
extract the predictions
Retrieve a loo function
Getters for permtest objects
Getters for tskrr objects
Getters for tskrrImpute objects
Getters for tskrrTune objects
Getters for linearFilter objects
Return the hat matrix of a tskrr model
Impute values based on a two-step kernel ridge regression
Impute missing values in a label matrix
Test symmetry of a matrix
Extract labels from a tskrr object
Fit a linear filter over a label matrix
Class linearFilter
Leave-one-out cross-validation for tskrr
Leave-one-out cross-validation for two-step kernel ridge regression
Calculate or extract the loss of a tskrr model
loss functions
Reorder the label matrix
Class permtest
Calculate the relative importance of the edges
plot a heatmap of the predictions from a tskrr model
Plot the grid of a tuned tskrr model
predict method for tskrr fits
calculate residuals from a tskrr model
test the symmetry of a matrix
Class tskrr
Carry out a two-step kernel ridge regression
Fitting a two step kernel ridge regression
Class tskrrHeterogeneous
Class tskrrHomogeneous
Class tskrrImpute
Class tskrrImputeHeterogeneous
Class tskrrImputeHomogeneous
Class tskrrTune
Class tskrrTuneHeterogeneous
Class tskrrTuneHomogeneous
tune the lambda parameters for a tskrr
Update a tskrr object with a new lambda
Functions to check matrices
Test the correctness of the labels.
Extract weights from a tskrr model
Two-step kernel ridge regression for network analysis
Fit a two-step kernel ridge regression model for predicting edges in networks, and carry out cross-validation using shortcuts for swift and accurate performance assessment (Stock et al, 2018 <doi:10.1093/bib/bby095> ).
Useful links