Description Objects from the Class Slots Methods Author(s) References See Also

A generic class for regression objects with network cohesion.

Objects can be created by calls of the form `new("rncReg", ...)`

.

`alpha`

:The individual effects of the regression.

`beta`

:The fixed effects or covariate coefficients of the regression.

`A`

:The network adjacency matrix for which cohession is assumed.

`lambda`

:Parameter for cohesion penalty.

`X`

:Covariate matrix.

`Y`

:Response matrix.

`dt`

:The response data frame with the first column being the observed time and the second column being the event indicator.

`gamma`

:Regularization parameter for graph Laplacian.

`cv`

:Number of folds in cross-validation.

`cv.loss`

:Cross-validated prediciton loss. It is MSE for linear regression, binomial deviance for logistic regression and test partial loglikelihood for Cox's model (see reference paper).

`cv.sd`

:Standard deviation of cross-validation loss. It can be used for cross-validation by 1 sigma rule. It is more robust to noises.

`model`

:The specific regression model used.

No methods defined with class "rncReg" in the signature.

Tianxi Li, Elizaveta Levina, Ji Zhu

Maintainer: Tianxi Li tianxili@umich.edu

Tianxi Li, Elizaveta Levina and Ji Zhu. (2016)
*Regression with network cohesion*,
http://arxiv.org/pdf/1602.01192v1.pdf

Verweij, Pierre JM, and Hans C. Van Houwelingen. (1993)
*Cross-validation in survival analysis*, Statistics in medicine 12, no. 24: 2305-2314.

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