Description Usage Arguments Value References Examples
est.USVT
is a generic matrix estimation method first
proposed for the case where a noisy realization of the matrix is given.
Universal Singular Value Thresholding (USVT), as its name suggests,
utilizes singular value decomposition of observations in addition to
thresholding over singular values achieved from the decomposition.
1 | est.USVT(A, eta = 0.01)
|
A |
either
|
eta |
a positive number in (0,1) to control the level of thresholding. |
a named list containing
a vector of sorted singular values.
a threshold to disregard singular values.
a matrix of estimated edge probabilities.
Chatterjee2015graphon
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 | ## generate a graphon of type No.1 with 3 clusters
W = gmodel.preset(3,id=1)
## create a probability matrix for 100 nodes
graphW = gmodel.block(W,n=100)
P = graphW$P
## draw 5 observations from a given probability matrix
A = gmodel.P(P,rep=5,symmetric.out=TRUE)
## run USVT algorithm with different eta values (0.01,0.1)
res2 = est.USVT(A,eta=0.01)
res3 = est.USVT(A,eta=0.1)
## compare true probability matrix and estimated ones
opar = par(no.readonly=TRUE)
par(mfrow=c(1,3), pty="s")
image(P, main="original P matrix")
image(res2$P, main="USVT with eta=0.01")
image(res3$P, main="USVT with eta = 0.1")
par(opar)
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