Description Usage Arguments Details Value Author(s) References See Also Examples

fits a regression model such that each samples are following a different regression curve such that connected individuals in a network tend to have similar curves. The function currently fits linear, logistic and Cox's regression model.

1 2 3 |

`A` |
An nxn symmetric adjacency matrix for the network. |

`lambda` |
Tuning parameter for the cohesion penalty. |

`Y` |
An nx1 matrix of response, if the model to fit is linear or logistic. It will not be used if one fits Cox's model. |

`X` |
An nxp covariate matrix with each row being the covariates for one individual. If one want to fits a model without using any covariate, it can be empty. |

`dt` |
Only used to fit Cox's model. An nx2 data.frame such that the first column is the observed time while the second column is the event indicator which is 1 for truely observed events and 0 for censored events. |

`gamma` |
The amount of diagonal regularization added to graph Laplacian. |

`model` |
Can only be one of "linear", "logistic" or "cox". |

`max.iter` |
The maximum number of newton steps to iterate. Only used for logistic model or Cox's model. |

`tol` |
The tolerance level for convergence. Only used for logistic model or Cox's model. |

`init` |
The initial point for newton algoritm. It should be an (n+p)x1 matrix that stacks alpha and beta. Only used for logistic model or Cox's model. If not specified, zeros will be used. |

`cv` |
Number of folds for cross-validation. If unspecified, then no cross-validation will be done. |

`cv.seed` |
Random number generator seed for cross-validation. |

`low.dim` |
Only used for linear model. If the probelm is a low dimensional problem such that n>>p, then using low.dim=TRUE is potentially faster. |

`verbose` |
If TRUE, the log likelihood in each newton step will be printed. Only used for logistic model and Cox's model. |

The function solves

*max L(α, β) - λα^TLα.*

With a proper choice of L function according to the specific
model. When the model is linear regression, L is the negative squared
error (or gaussian kernel); when the model is logistic regression, L
is the binomial log likelihood; when the model is Cox's model, L is
the log partial likelihood. `gamma`

is used to regularize the graph Laplacian and is potentially helpful for numerical stability and Cox's model identifiability. Notice that having a positive `gamma`

tends to shrink individual effects to zeros. Thus in linear regression, we suggest first center the data (both predictors and response) before fitting the model.
For full details, please check the reference paper.

An object from class `rncReg`

will be
returned.

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

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 | ```
set.seed(100)
A <- matrix(0,200,200)
A[1:100,1:100] <- 1
A[101:200,101:200] <- 1
diag(A) <- 0
alpha <- c(rep(1,100),rep(-1,100)) + rnorm(200)*0.5
A <- A[c(1:50,101:150,51:100,151:200),c(1:50,101:150,51:100,151:200)]
alpha <- alpha[c(1:50,101:150,51:100,151:200)]
beta <- rnorm(2)
X <- matrix(rnorm(400),ncol=2)
Y <- X
delta <- Y
delta[Y>0] <- 1
delta[Y<=0] <- 0
A1 <- A[1:100,1:100]
X1 <- X[1:100,]
Y1 <- matrix(Y[1:100],ncol=1)
delta1 <- matrix(delta[1:100],ncol=1)
## If one wants to regularize the Laplacian
## by using gamma > 0 in rncreg,
## we suggest use centered data.
#mean.x <- colMeans(X1)
#mean.y <- mean(Y1)
#Y1 <- Y1-mean.y
#X1 <- t(t(X1)-mean.x)
#Y <- Y-mean.y
#X <- t(t(X)-mean.x)
m <- rncreg(A=A1,X=X1,Y=Y1,model="linear",lambda=10,gamma=0,cv=5)
p <- predict(m,full.A=A,full.X=X)
#m <- rncreg(A=A1,X=X1,Y=Y1,model="logistic",lambda=10,gamma=0.01,cv=5)
#m <- rncreg(A=A1,X=X1,dt=data.frame(y=Y1,delta=delta1),model="cox",lambda=10,gamma=0.01,cv=5)
``` |

Embedding an R snippet on your website

Add the following code to your website.

For more information on customizing the embed code, read Embedding Snippets.