Description Usage Arguments Value Note References See Also Examples

Produce a plot or send back the values of some penalized criteria
accompanied with the vector(s) of parameters selected
accordingly. The default behavior plots the BIC and the AIC (with
respective factor *log(n)* and *2*) yet the user can specify any
penalty.

1 2 3 4 5 6 7 8 | ```
criteria(object, Y, penalty = setNames(c(2, log(length(Y))), c("AIC",
"BIC")), sigma = NULL, log.scale = TRUE, xvar = "lambda",
plot = TRUE)
## S4 method for signature 'blockSeg'
criteria(object, Y, penalty = setNames(c(2,
log(length(Y))), c("AIC", "BIC")), sigma = NULL, log.scale = TRUE,
xvar = "lambda", plot = TRUE)
``` |

`object` |
output of a fitting procedure of the blockseg
package (e.g. |

`Y` |
matrix of observations. |

`penalty` |
a vector with as many penalties a desired. The
default contains the penalty corresponding to the AIC and the BIC
( |

`sigma` |
scalar: an estimate of the residual variance. When
available, it is plugged-in the criteria, which may be more
relevant. If |

`log.scale` |
logical; indicates if a log-scale should be used
when |

`xvar` |
variable to plot on the X-axis: either |

`plot` |
logical; indicates if the graph should be plotted on
call. Default is |

When `plot`

is set to `TRUE`

, an invisible
ggplot2 object is returned, which can be plotted via the
`print`

method. On the other hand, a list with a two data
frames containing the criteria and the chosen vector of parameters
are returned.

When `sigma`

is provided, the criterion takes the form

When it is unknown, it writes

Estimation of the degrees of freedom (for the elastic-net, the LASSO and also bounded regression) are computed by applying and adapting the results of Tibshirani and Taylor (see references below).

Ryan Tibshirani and Jonathan Taylor. Degrees of freedom in lasso problems, Annals of Statistics, 40(2) 2012.

1 2 3 4 5 6 | ```
n <- 100
K <- 5
mu <- suppressWarnings(matrix(rep(c(1,0),ceiling(K**2/2)), K,K))
Y <- rblockdata(n,mu,sigma=.5)$Y
res <- blockSeg(Y, 50)
criteria(res, Y, sigma=.5)
``` |

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