Two-Step Kernel Ridge Regression for Network Predictions

Documentation for package ‘xnet’ version 0.1.11

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

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