Description Usage Arguments Details Value Author(s) References See Also Examples
Fits models by rncreg
for a sequence of lambda values.
1 2 3 |
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 |
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
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 | 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
|
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