Description Usage Arguments Details Value Author(s) References See Also Examples
The generic prediction function that makes predictions according an "rncReg" object, which is a regression model fitted with network cohesion.
1 2 
object 
An object returned by the function 
full.X 
A matrix with first n rows being the original training covariates and the last m rows being the new test covariates. If not provided, covariates will not be used in prediction. 
full.A 
An adjacency matrix of the complete network with both training and test samples. The first n vertices are representing the training samples (in the original order) and the last m vertices are reprenting the test samples. 
... 
further arguments passed to or from other methods. 
The function first predicts individual effects on test sample by
minimizing network cohesion penalty and then incorporates the covariate effects.
For full details, please check the reference paper. The predicted test
sample individual effects will be returned, as well as the
corresponding linear terms. For linear
regression model, the predicted response y
is also given, which
is exactly the same as the linear term. For logistic regression, the
predicted probability is also given.
A list with following slots:

the linear term in the model. 

the predicted individual effects. 

the predicted responses in linear model. 

the predicted probabilities in logistic regression. 

the model used in prediction. This is the same as
in 
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 < Y1mean.y
#X1 < t(t(X1)mean.x)
#Y < Ymean.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)

Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.