selectQE: Estimate a graph in a Gaussian Graphical Model: Quasi... In GGMselect: Gaussian Graphs Models Selection

Description

Select a graph within the family of graphs `QE`

Usage

 ```1 2 3``` ```selectQE(X, dmax=min(3,nrow(X)-3,ncol(X)-1), K=2.5, min.ev=10**(-8), max.iter=10**6, max.nG=10**8, max.size=10**8, verbose=FALSE) ```

Arguments

 `X` `n x p` matrix where `n` is the sample size and `p` the number of variables. `n` should be greater than `3` and `p` greater than `1`. `dmax` integer or `p`-dimensional vector of integers smaller or equal to `min(n-3, p-1)`. When `dmax` is a scalar, it gives the maximum degree of the estimated graph. When `dmax` is a vector, `dmax[a]` gives the maximum degree of the node `a`. `K` scalar or vector with values greater than 1. Tuning parameter in the penalty function. `min.ev` minimum eigenvalue for matrix inversion. `max.iter ` integer. Maximum number of stepwise iterations. `max.nG` integer. Maximum number of graphs considered in the exhaustive search. Stepwise procedure beyond. `max.size` integer. Maximum number of calculations of the residuals sums of squares. Execution stopped beyond. `verbose` logical. If `TRUE` a trace of the current process is displayed in real time.

Details

More details are available on ../doc/Notice.pdf

Value

 `Neighb ` array of dimension `p x max(dmax) x length(K)` or, when `length(K)` equals 1, matrix of dimension `p x max(dmax)`. `Neighb[a, , k ]` contains the indices of the nodes connected to node `a` for `K[k]`. `crit.min ` vector of dimension `length(K)`. The minimal values of the selection criterion for each value of `K`. `G` array of dimension `p x p x length(K)` or, when `length(K)` equals 1, matrix of dimension `p x p`. `G[,,k]` gives the adjacency matrix for `K[k]`.

Author(s)

Bouvier A, Giraud C, Huet S, Verzelen N.

References

Please use `citation("GGMselect")`.

See Also

`selectFast`, `selectMyFam`, `simulateGraph`, `penalty`, `convertGraph`

Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17``` ```p=30 n=30 # simulate graph eta=0.11 Gr <- simulateGraph(p,eta) # simulate data X <- rmvnorm(n, mean=rep(0,p), sigma=Gr\$C) # estimate graph ## Not run: GQE <- selectQE(X) # plot the result ## Not run: library(network) ## Not run: par(mfrow=c(1,2)) ## Not run: gV <- network(Gr\$G) ## Not run: plot(gV,jitter=TRUE, usearrows = FALSE, label=1:p,displaylabels=TRUE) ## Not run: gQE <- network(GQE\$G) ## Not run: plot(gQE, jitter=TRUE, usearrows = FALSE, label=1:p,displaylabels=TRUE) ```

GGMselect documentation built on Jan. 10, 2020, 9:07 a.m.