Description Usage Arguments Details Value Note Author(s) References See Also Examples

View source: R/lambdaSelection.r

`lambdaSelection`

is a function designed to select the sparsity regularization
parameter in graphical models.

There are available seven different criterion to select lambda with risk functions
based on network characteristics: Path connectivity (PC), AGlommerative NESted (AGNES), Augmented-MSE (A-MSE),
Vulnerability (VUL), AIC/BIC and StARS (from `huge`

package).

1 2 | ```
lambdaSelection(obj, criterion = c("PC","AGNES",
"A-MSE","VUL","STARS", "AIC", "BIC", "eBIC"), ...)
``` |

`obj` |
an object of class |

`criterion` |
regularization parameter selection approach: name that uniquely identifies |

`...` |
arguments passed to or from other methods to the low level. See |

This function considers seven ways of selecting the regularization parameter in
graphical models by minimizing a certain risk function based only on network characteristics
of the underlying structure of *Ω*

*
\hatλ = \arg \min_{λ} R(λ, \hat{G}_λ),
*

where *\hat{G}_λ* is the estimated graph structure of *\hat{Ω}*. For
instance see `pcLambdaSelection`

,

`agnesLambdaSelection`

, `amseLambdaSelection`

, `vulLambdaSelection`

,

`aicAndbicLambdaSelection`

and `huge`

for the implemented criterions to select *λ*.

For `wfgl`

objects, only `criterion = c("PC","AGNES","VUL")`

are available.

An object of class `lambdaSelection`

containing the following components:

`opt.lambda ` |
optimal lambda. |

`crit.coef ` |
coefficients for each lambda given the criterion. |

`criterion ` |
criterion used to select lambda. |

For large dimensions, `"A-MSE"`

, `"VUL"`

(the most) and `"StARS"`

can be computationally intensive.

Caballe, Adria <a.caballe@sms.ed.ac.uk>, Natalia Bochkina and Claus Mayer.

Caballe, A., N. Bochkina, and C. Mayer (2016). Selection of the Regularization Parameter in Graphical Models using network charactaristics. eprint arXiv:1509.05326, 1-25.

`pcLambdaSelection`

, `agnesLambdaSelection`

, `amseLambdaSelection`

`vulLambdaSelection`

, `aicAndbicLambdaSelection`

and `huge`

.

1 2 3 4 5 6 7 8 9 | ```
# example to use agnes function
EX1 <- pcorSimulator(nobs = 50, nclusters = 3, nnodesxcluster = c(40,30,30),
pattern="powerLaw")
y <- EX1$y
Lambda.SEQ <- seq(.35,0.70, length.out = 40)
out3 <- huge(y, method = "mb", lambda = Lambda.SEQ)
PC.COEF <- lambdaSelection(out3, criterion = "PC")
#AG.COEF <- lambdaSelection(out3, criterion = "AGNES")
``` |

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