R: Two-Step Kernel Ridge Regression for Network Predictions

# Two-Step Kernel Ridge Regression for Network Predictions

## Documentation for package ‘xnet’ version 0.1.11

- DESCRIPTION file
- User guides, package vignettes and other documentation
- Package NEWS

## Help Pages

A C D E F G H I K L M N P R S T U V W X misc

xnet-package | Two-step kernel ridge regression for network analysis

## -- A --

alpha | Getters for linearFilter objects
alpha-method | Getters for linearFilter objects
as_tskrr | convert tskrr models
as_tskrr-method | convert tskrr models
as_tuned | convert tskrr models
as_tuned-method | convert tskrr models

## -- C --

colMeans-method | Getters for linearFilter objects
colnames-method | Extract labels from a tskrr object
create_grid | Create a grid of values for tuning tskrr

## -- D --

dim-method | Get the dimensions of a tskrr object
dim.tskrr | Get the dimensions of a tskrr object
dimnames-method | Extract labels from a tskrr object
dimnames.tskrr | Extract labels from a tskrr object
drugSim | drug target interactions for neural receptors
drugtarget | drug target interactions for neural receptors
drugTargetInteraction | drug target interactions for neural receptors

## -- E --

eigen2hat | Calculate the hat matrix from an eigen decomposition
eigen2map | Calculate the hat matrix from an eigen decomposition
eigen2matrix | Calculate the hat matrix from an eigen decomposition
Extract-permtest | Getters for permtest objects

## -- F --

fitted-method | extract the predictions
fitted.linearFilter | extract the predictions
fitted.tskrr | extract the predictions

## -- G --

getters_linearFilter | Getters for linearFilter objects
get_eigen | Getters for tskrr objects
get_grid | Getters for tskrrTune objects
get_kernel | Getters for tskrr objects
get_kernelmatrix | Getters for tskrr objects
get_loo_fun | Retrieve a loo function
get_loo_fun-method | Retrieve a loo function
get_loss_values | Getters for tskrrTune objects

## -- H --

has_hat | Getters for tskrr objects
has_imputed_values | Getters for tskrrImpute objects
has_onedim | Getters for tskrrTune objects
hat | Return the hat matrix of a tskrr model
hat-method | Return the hat matrix of a tskrr model

## -- I --

impute_tskrr | Impute missing values in a label matrix
impute_tskrr.fit | Impute values based on a two-step kernel ridge regression
is_heterogeneous | Getters for tskrr objects
is_homogeneous | Getters for tskrr objects
is_imputed | Getters for tskrrImpute objects
is_square | Functions to check matrices
is_symmetric | Test symmetry of a matrix
is_tskrr | Getters for tskrr objects
is_tuned | Getters for tskrrTune objects

## -- K --

Kmat_y2h_sc | Protein interaction for yeast

## -- L --

labels-method | Extract labels from a tskrr object
labels.tskrr | Extract labels from a tskrr object
lambda | Getters for tskrr objects
lambda-method | Getters for tskrr objects
linearFilter | Class linearFilter
linearFilter-class | Class linearFilter
linear_filter | Fit a linear filter over a label matrix
loo | Leave-one-out cross-validation for tskrr
loo-method | Leave-one-out cross-validation for tskrr
loo.b | Leave-one-out cross-validation for two-step kernel ridge regression
loo.c | Leave-one-out cross-validation for two-step kernel ridge regression
loo.e.skew | Leave-one-out cross-validation for two-step kernel ridge regression
loo.e.sym | Leave-one-out cross-validation for two-step kernel ridge regression
loo.e0.skew | Leave-one-out cross-validation for two-step kernel ridge regression
loo.e0.sym | Leave-one-out cross-validation for two-step kernel ridge regression
loo.i | Leave-one-out cross-validation for two-step kernel ridge regression
loo.i.lf | Leave-one-out cross-validation for two-step kernel ridge regression
loo.i0 | Leave-one-out cross-validation for two-step kernel ridge regression
loo.i0.lf | Leave-one-out cross-validation for two-step kernel ridge regression
loo.r | Leave-one-out cross-validation for two-step kernel ridge regression
loo.v | Leave-one-out cross-validation for two-step kernel ridge regression
loo_internal | Leave-one-out cross-validation for two-step kernel ridge regression
loss | Calculate or extract the loss of a tskrr model
loss-method | Calculate or extract the loss of a tskrr model
loss_auc | loss functions
loss_functions | loss functions
loss_mse | loss functions

## -- M --

match_labels | Reorder the label matrix
mean-method | Getters for linearFilter objects
mean.linearFilter | Getters for linearFilter objects

## -- N --

na_removed | Getters for linearFilter objects
na_removed-method | Getters for linearFilter objects

## -- P --

permtest | Calculate the relative importance of the edges
permtest-class | Class permtest
permtest-method | Calculate the relative importance of the edges
permutations | Getters for permtest objects
plot.tskrr | plot a heatmap of the predictions from a tskrr model
plot_grid | Plot the grid of a tuned tskrr model
predict-method | predict method for tskrr fits
predict.tskrr | predict method for tskrr fits
print.permtest | Calculate the relative importance of the edges
proteinInteraction | Protein interaction for yeast

## -- R --

residuals | calculate residuals from a tskrr model
residuals-method | calculate residuals from a tskrr model
residuals.tskrr | calculate residuals from a tskrr model
response | Getters for tskrr objects
response-method | Getters for tskrr objects
rowMeans-method | Getters for linearFilter objects
rownames-method | Extract labels from a tskrr object

## -- S --

symmetry | Getters for tskrr objects

## -- T --

targetSim | drug target interactions for neural receptors
test_symmetry | test the symmetry of a matrix
tskrr | Fitting a two step kernel ridge regression
tskrr-class | Class tskrr
tskrr.fit | Carry out a two-step kernel ridge regression
tskrrHeterogeneous | Class tskrrHeterogeneous
tskrrHeterogeneous-class | Class tskrrHeterogeneous
tskrrHomogeneous | Class tskrrHomogeneous
tskrrHomogeneous-class | Class tskrrHomogeneous
tskrrImpute | Class tskrrImpute
tskrrImpute-class | Class tskrrImpute
tskrrImputeHeterogeneous | Class tskrrImputeHeterogeneous
tskrrImputeHeterogeneous-class | Class tskrrImputeHeterogeneous
tskrrImputeHomogeneous | Class tskrrImputeHomogeneous
tskrrImputeHomogeneous-class | Class tskrrImputeHomogeneous
tskrrTune | Class tskrrTune
tskrrTune-class | Class tskrrTune
tskrrTuneHeterogeneous | Class tskrrTuneHeterogeneous
tskrrTuneHeterogeneous-class | Class tskrrTuneHeterogeneous
tskrrTuneHomogeneous | Class tskrrTuneHomogeneous
tskrrTuneHomogeneous-class | Class tskrrTuneHomogeneous
tune | tune the lambda parameters for a tskrr
tune-method | tune the lambda parameters for a tskrr

## -- U --

update | Update a tskrr object with a new lambda
update-method | Update a tskrr object with a new lambda

## -- V --

valid_dimensions | Functions to check matrices
valid_labels | Test the correctness of the labels.

## -- W --

weights | Extract weights from a tskrr model
weights-method | Extract weights from a tskrr model
which_imputed | Getters for tskrrImpute objects

## -- X --

xnet | Two-step kernel ridge regression for network analysis

## -- misc --

[-method | Getters for permtest objects