Package: SurrogateRegression 0.6.0.1

SurrogateRegression: Surrogate Outcome Regression Analysis

Performs estimation and inference on a partially missing target outcome (e.g. gene expression in an inaccessible tissue) while borrowing information from a correlated surrogate outcome (e.g. gene expression in an accessible tissue). Rather than regarding the surrogate outcome as a proxy for the target outcome, this package jointly models the target and surrogate outcomes within a bivariate regression framework. Unobserved values of either outcome are treated as missing data. In contrast to imputation-based inference, no assumptions are required regarding the relationship between the target and surrogate outcomes. Estimation in the presence of bilateral outcome missingness is performed via an expectation conditional maximization either algorithm. In the case of unilateral target missingness, estimation is performed using an accelerated least squares procedure. A flexible association test is provided for evaluating hypotheses about the target regression parameters. For additional details, see: McCaw ZR, Gaynor SM, Sun R, Lin X: "Leveraging a surrogate outcome to improve inference on a partially missing target outcome" <doi:10.1111/biom.13629>.

Authors:Zachary McCaw [aut, cre]

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SurrogateRegression/json (API)

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

Peer review:

Bug tracker:https://github.com/zrmacc/surrogateregression/issues

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

On CRAN:

4 exports 1 stars 0.74 score 2 dependencies 12 scripts 210 downloads

Last updated 6 months agofrom:830615149b. Checks:OK: 9. Indexed: yes.

TargetResultDate
Doc / VignettesOKAug 27 2024
R-4.5-win-x86_64OKAug 27 2024
R-4.5-linux-x86_64OKAug 27 2024
R-4.4-win-x86_64OKAug 27 2024
R-4.4-mac-x86_64OKAug 27 2024
R-4.4-mac-aarch64OKAug 27 2024
R-4.3-win-x86_64OKAug 27 2024
R-4.3-mac-x86_64OKAug 27 2024
R-4.3-mac-aarch64OKAug 27 2024

Exports:FitBNRPartitionDatarBNRTestBNR

Dependencies:RcppRcppArmadillo

Surrogate Outcome Regression Analysis

Rendered fromSurrogateRegression.Rnwusingutils::Sweaveon Aug 27 2024.

Last update: 2020-12-01
Started: 2020-12-01

Readme and manuals

Help Manual

Help pageTopics
Bivariate Regression Modelbnr-class
Check InitiationCheckInit
Check Test SpecificationCheckTestSpec
Extract Coefficients from Bivariate Regression Modelcoef.bnr
Covariance Information MatrixCovInfo
Tabulate Covariance ParametersCovTab
Covariate UpdateCovUpdate
Fit Bivariate Normal Regression Model via Expectation Maximization.FitBNEM
Fit Bivariate Normal Regression Model via Least SquaresFitBNLS
Fit Bivariate Normal Regression ModelFitBNR
Ordinary Least SquaresfitOLS
Format OutputFormatOutput
Update IterationIterUpdate
Matrix DeterminantmatDet
Matrix InversematInv
Matrix Inner ProductmatIP
Matrix Outer ProductmatOP
Quadratic FormmatQF
Matrix Matrix ProductMMP
Observed Data Log LikelihoodObsLogLik
Parameter InitializationParamInit
Partition Data by Outcome Missingness Pattern.PartitionData
Print for Bivariate Regression Modelprint.bnr
Simulate Bivariate Normal Data with MissingnessrBNR
Regression InformationRegInfo
Tabulate Regression CoefficientsRegTab
Regression UpdateRegUpdate
Extract Residuals from Bivariate Regression Modelresiduals.bnr
Schur complementSchurC
Score Test via Expectation Maximization.ScoreBNEM
Show for Bivariate Regression Modelshow,bnr-method
SurrogateRegression: Surrogate Outcome Regression AnalysisSurrogateRegression-package SurrogateRegression
Test Bivariate Normal Regression Model.TestBNR
Matrix Tracetr
EM UpdateUpdateEM
Extract Covariance Matrix from Bivariate Normal Regression Modelvcov.bnr
Wald Test via Expectation Maximization.WaldBNEM
Wald Test via Least Squares.WaldBNLS