Package: multiview 0.8

multiview: Cooperative Learning for Multi-View Analysis

Cooperative learning combines the usual squared error loss of predictions with an agreement penalty to encourage the predictions from different data views to agree. By varying the weight of the agreement penalty, we get a continuum of solutions that include the well-known early and late fusion approaches. Cooperative learning chooses the degree of agreement (or fusion) in an adaptive manner, using a validation set or cross-validation to estimate test set prediction error. In the setting of cooperative regularized linear regression, the method combines the lasso penalty with the agreement penalty (Ding, D., Li, S., Narasimhan, B., Tibshirani, R. (2021) <doi:10.1073/pnas.2202113119>).

Authors:Daisy Yi Ding [aut], Robert J. Tibshirani [aut], Balasubramanian Narasimhan [aut, cre], Trevor Hastie [aut], Kenneth Tay [aut], James Yang [aut]

multiview_0.8.tar.gz
multiview_0.8.zip(r-4.5)multiview_0.8.zip(r-4.4)multiview_0.8.zip(r-4.3)
multiview_0.8.tgz(r-4.4-x86_64)multiview_0.8.tgz(r-4.4-arm64)multiview_0.8.tgz(r-4.3-x86_64)multiview_0.8.tgz(r-4.3-arm64)
multiview_0.8.tar.gz(r-4.5-noble)multiview_0.8.tar.gz(r-4.4-noble)
multiview_0.8.tgz(r-4.4-emscripten)multiview_0.8.tgz(r-4.3-emscripten)
multiview.pdf |multiview.html✨
multiview/json (API)
NEWS

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

Peer review:

Uses libs:
  • c++– GNU Standard C++ Library v3

On CRAN:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

5 exports 0.00 score 11 dependencies 14 scripts 198 downloads

Last updated 1 years agofrom:71b29d2605. Checks:OK: 1 WARNING: 8. Indexed: yes.

TargetResultDate
Doc / VignettesOKSep 02 2024
R-4.5-win-x86_64WARNINGSep 02 2024
R-4.5-linux-x86_64WARNINGSep 02 2024
R-4.4-win-x86_64WARNINGSep 02 2024
R-4.4-mac-x86_64WARNINGSep 02 2024
R-4.4-mac-aarch64WARNINGSep 02 2024
R-4.3-win-x86_64WARNINGSep 02 2024
R-4.3-mac-x86_64WARNINGSep 02 2024
R-4.3-mac-aarch64WARNINGSep 02 2024

Exports:coef_orderedcv.multiviewmultiviewmultiview.controlview.contribution

Dependencies:codetoolsforeachglmnetiteratorslatticeMatrixRColorBrewerRcppRcppEigenshapesurvival

An Introduction to multiview

Daisy Yi Ding, Shuangning Li, Balasubramanian Narasimhan, Robert Tibshirani

Rendered frommultiview.Rmdusingknitr::rmarkdownon Sep 02 2024.

Last update: 2023-03-31
Started: 2022-09-02

Readme and manuals

Help Manual

Help pageTopics
Cooperative learning for multiple views using generalized linear modelsmultiview-package
Extract an ordered list of standardized coefficients from a 'multiview' or 'cv.multiview' objectcoef_ordered
Extract an ordered list of standardized coefficients from a cv.multiview objectcoef_ordered.cv.multiview
Extract an ordered list of standardized coefficients from a multiview objectcoef_ordered.multiview
Extract coefficients from a cv.multiview objectcoef.cv.multiview
Extract coefficients from a multiview objectcoef.multiview
Collapse a list of named lists into one list with the same namecollapse_named_lists
Elastic net objective function value for Cox regression modelcox_obj_function
Perform k-fold cross-validation for cooperative learningcv.multiview
Elastic net deviance valuedev_function
Solve weighted least squares (WLS) problem for a single lambda valueelnet.fit
Get lambda max for Cox regression modelget_cox_lambda_max
Helper function to get etas (linear predictions)get_eta
Get null deviance, starting mu and lambda maxget_start
Build a block row matrix for multiviewmake_row
Perform cooperative learning using the direct algorithm for two or more views.multiview
Internal multiview parametersmultiview.control
Fit a Cox regression model with elastic net regularization for a single value of lambdamultiview.cox.fit
Fit a Cox regression model with elastic net regularization for a path of lambda valuesmultiview.cox.path
Fit a GLM with elastic net regularization for a single value of lambdamultiview.fit
Fit a GLM with elastic net regularization for a path of lambda valuesmultiview.path
Elastic net objective function valueobj_function
Elastic net penalty valuepen_function
Plot coefficients from a "multiview" objectplot.multiview
Make predictions from a "cv.multiview" object.predict.cv.multiview
Get predictions from a 'multiview' fit objectpredict.multiview
Return a new list of x matrices of same shapes as those in x_listreshape_x_to_xlist
Make response for coxnetresponse.coxnet
Select x_list columns specified by (conformable) list of indicesselect_matrix_list_columns
Translate from column indices in list of x matrices to indices in '1:nvars'. No sanity checks for efficiencyto_nvar_index
Translate indices in '1:nvars' to column indices in list of x matrices. No sanity checksto_xlist_index
Evaluate the contribution of data views in making predictionview.contribution
Helper function to compute weighted mean and standard deviationweighted_mean_sd