graphon (version 0.3.2)

est.completion: Estimate graphons based on matrix completion scheme

Description

est.completion adopts a matrix completion scheme, which is common in missing data or matrix reconstruction studies. When given a multiple of, or a single observation, we consider non-existent edges as missing entries and apply the completion scheme. See OptSpace for a more detailed introduction.

Usage

est.completion(A, rank = NA, tolerance = 0.001, maxiter = 20,
  progress = FALSE, adjust = TRUE)

Arguments

A

either

Case 1.

an \((n\times n)\) binary adjacency matrix, or

Case 2.

a list containing multiple of \((n\times n)\) binary adjacency matrices.

rank

an estimated rank condition for the matrix; NA for automatic guessing of a rank, or a positive integer for a user-supplied rank assumption.

tolerance

a tolerance level for singular value thresholding from OptSpace method.

maxiter

the number of maximum iterations for OptSpace method.

progress

a logical value; FALSE for not showing intermediate flags during the process, TRUE otherwise.

adjust

a logical value; TRUE to ignore a guessed rank and set it as 2 upon numerical errors, FALSE to stop the code.

Value

an \((n\times n)\) corresponding probability matrix.

References

Keshavan2010graphon

Examples

# NOT RUN {
## generate a graphon of type No.5 with 3 clusters
W = gmodel.preset(3,id=5)

## create a probability matrix for 100 nodes
graphW = gmodel.block(W,n=100)
P = graphW$P

## draw 10 observations from a given probability matrix
A = gmodel.P(P,rep=10)

## apply the method
res_r3 = est.completion(A,rank=3)       # use rank-3 approximation
res_r9 = est.completion(A,rank=9)       # use rank-9 approximation
res_rN = est.completion(A,adjust=FALSE) # stop the code if guess works poorly

## visualize
par(mfrow=c(1,3))
image(res_r3); title("rank 3")
image(res_r9); title("rank 9")
image(res_rN); title("guessed rank")
# }
# NOT RUN {
# }