Package: xnet 0.1.11

Joris Meys

xnet: Two-Step Kernel Ridge Regression for Network Predictions

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> ).

Authors:Joris Meys [cre, aut], Michiel Stock [aut]

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xnet.pdf |xnet.html
xnet/json (API)
NEWS

# Install 'xnet' in R:
install.packages('xnet', repos = c('https://centerforstatistics-ugent.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/centerforstatistics-ugent/xnet/issues

Datasets:

On CRAN:

5.30 score 11 stars 12 scripts 99 downloads 50 exports 0 dependencies

Last updated 3 years agofrom:4093905ae8. Checks:OK: 7. Indexed: yes.

TargetResultDate
Doc / VignettesOKOct 31 2024
R-4.5-winOKOct 31 2024
R-4.5-linuxOKOct 31 2024
R-4.4-winOKOct 31 2024
R-4.4-macOKOct 31 2024
R-4.3-winOKOct 31 2024
R-4.3-macOKOct 31 2024

Exports:alphacolMeanscolnamescreate_grideigen2hateigen2mapeigen2matrixfittedget_eigenget_gridget_kernelget_kernelmatrixget_loo_funget_loss_valueshas_imputed_valueshatimpute_tskrrimpute_tskrr.fitis_heterogeneousis_homogeneousis_imputedis_symmetricis_tunedlabelslambdalinear_filterloolossloss_aucloss_msematch_labelsmeanna_removedpermtestpermutationsplot_gridpredictresidualsresponserowMeansrownamessymmetrytest_symmetrytskrrtskrr.fittuneupdatevalid_dimensionsweightswhich_imputed

Dependencies:

Preparation of the example data

Rendered fromPreparation_example_data.Rmdusingknitr::rmarkdownon Oct 31 2024.

Last update: 2019-12-17
Started: 2018-09-17

A short introduction to cross-network analysis with xnet

Rendered fromxnet_ShortIntroduction.Rmdusingknitr::rmarkdownon Oct 31 2024.

Last update: 2019-12-17
Started: 2019-04-04

S4 class structure of the xnet package

Rendered fromxnet_ClassStructure.Rmdusingknitr::rmarkdownon Oct 31 2024.

Last update: 2019-12-13
Started: 2019-03-28

Readme and manuals

Help Manual

Help pageTopics
Two-step kernel ridge regression for network analysisxnet-package xnet
Getters for linearFilter objectsalpha alpha,linearFilter-method colMeans,linearFilter-method getters_linearFilter mean,linearFilter-method mean.linearFilter na_removed na_removed,linearFilter-method rowMeans,linearFilter-method
convert tskrr modelsas_tskrr as_tskrr,tskrr-method as_tskrr,tskrrImpute-method as_tskrr,tskrrTune-method as_tuned as_tuned,tskrrHeterogeneous-method as_tuned,tskrrHomogeneous-method
Create a grid of values for tuning tskrrcreate_grid
Get the dimensions of a tskrr objectdim,tskrr-method dim.tskrr
drug target interactions for neural receptorsdrugSim drugtarget drugTargetInteraction targetSim
Calculate the hat matrix from an eigen decompositioneigen2hat eigen2map eigen2matrix
extract the predictionsfitted,linearFilter-method fitted,tskrr-method fitted.linearFilter fitted.tskrr
Retrieve a loo functionget_loo_fun get_loo_fun,character-method get_loo_fun,linearFilter-method get_loo_fun,tskrrHeterogeneous-method get_loo_fun,tskrrHomogeneous-method get_loo_fun,tskrrTune-method
Getters for tskrrImpute objectshas_imputed_values is_imputed which_imputed
Return the hat matrix of a tskrr modelhat hat,tskrrHeterogeneous-method hat,tskrrHomogeneous-method
Impute missing values in a label matriximpute_tskrr
Impute values based on a two-step kernel ridge regressionimpute_tskrr.fit
Test symmetry of a matrixis_symmetric
Getters for tskrrTune objectsget_grid get_loss_values has_onedim is_tuned
Extract labels from a tskrr objectcolnames,tskrr-method dimnames,tskrr-method dimnames.tskrr labels,tskrr-method labels.tskrr rownames,tskrr-method
Fit a linear filter over a label matrixlinear_filter
Class linearFilterlinearFilter linearFilter-class
Leave-one-out cross-validation for tskrrloo loo,linearFilter-method loo,tskrrHeterogeneous-method loo,tskrrHomogeneous-method
Leave-one-out cross-validation for two-step kernel ridge regressionloo.b loo.c loo.e.skew loo.e.sym loo.e0.skew loo.e0.sym loo.i loo.i.lf loo.i0 loo.i0.lf loo.r loo.v loo_internal
Calculate or extract the loss of a tskrr modelloss loss,permtest-method loss,tskrr-method loss,tskrrTune-method
loss functionsloss_auc loss_functions loss_mse
Reorder the label matrixmatch_labels
Calculate the relative importance of the edgespermtest permtest,tskrrHeterogeneous-method permtest,tskrrHomogeneous-method permtest,tskrrTune-method print.permtest
Class permtestpermtest-class
Getters for permtest objectsExtract-permtest permutations [,permtest-method
Plot the grid of a tuned tskrr modelplot_grid
plot a heatmap of the predictions from a tskrr modelplot.tskrr
predict method for tskrr fitspredict,tskrr-method predict.tskrr
Protein interaction for yeastKmat_y2h_sc proteinInteraction
calculate residuals from a tskrr modelresiduals residuals,tskrr-method residuals.tskrr
Getters for tskrr objectsget_eigen get_kernel get_kernelmatrix has_hat is_heterogeneous is_homogeneous is_tskrr lambda lambda,tskrrHeterogeneous-method lambda,tskrrHomogeneous-method response response,tskrr-method symmetry
test the symmetry of a matrixtest_symmetry
Fitting a two step kernel ridge regressiontskrr
Class tskrrtskrr-class
Carry out a two-step kernel ridge regressiontskrr.fit
Class tskrrHeterogeneoustskrrHeterogeneous tskrrHeterogeneous-class
Class tskrrHomogeneoustskrrHomogeneous tskrrHomogeneous-class
Class tskrrImputetskrrImpute tskrrImpute-class
Class tskrrImputeHeterogeneoustskrrImputeHeterogeneous tskrrImputeHeterogeneous-class
Class tskrrImputeHomogeneoustskrrImputeHomogeneous tskrrImputeHomogeneous-class
Class tskrrTunetskrrTune tskrrTune-class
Class tskrrTuneHeterogeneoustskrrTuneHeterogeneous tskrrTuneHeterogeneous-class
Class tskrrTuneHomogeneoustskrrTuneHomogeneous tskrrTuneHomogeneous-class
tune the lambda parameters for a tskrrtune tune,matrix-method tune,tskrrHeterogeneous-method tune,tskrrHomogeneous-method
Update a tskrr object with a new lambdaupdate update,tskrrHeterogeneous-method update,tskrrHomogeneous-method
Functions to check matricesis_square valid_dimensions
Test the correctness of the labels.valid_labels
Extract weights from a tskrr modelweights weights,tskrrHeterogeneous-method weights,tskrrHomogeneous-method