rncregpath: Fits regression models with network cohesion effects for a...
In netcoh: Statistical Modeling with Network Cohesion

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

View source: R/Rfunc.R

Fits models by rncreg for a sequence of lambda values.

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rncregpath(A,lambdaseq,Y=NULL,X=NULL,dt=NULL,gamma=0.05,
     model=c("linear","logistic","cox"), max.iter=50,tol=1e-4,
     init=NULL, cv=NULL,cv.seed=999,low.dim=NULL,verbose=FALSE)

`A`	An nxn symmetric adjacency matrix for the network.
`lambdaseq`	A vector of lambda values.
`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 repeatly calls rncreg for the values of lambda.

A list will be returned with the following slots:

`models`	A list of objects from `rncReg` class, each of which corresponds to a specific lambda value in lambdaseq.
`cv.seq`	The sequence of cross-validation loss corresponding to lambdaseq.
`cv.sd`	The sequence of corssivalidation standard deviation
`cv.min.index`	The index of model that corresponds to the one with miminum cv loss.
`cv.1sd.index`	The index of model that corresponds to the one selected by 1 sigma rule.

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

rncReg,predict.rncReg

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

A1 <- A[1:100,1:100]
X1 <- X[1:100,]
Y1 <- matrix(Y[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.list <- rncregpath(A=A1,X=X1,Y=Y1,model="linear",
                     lambdaseq=exp(seq(0,log(200),length.out=20)),gamma=0,cv=5)
m.list$cv.seq