loglikLOOCVcontourVAR1: Contourplot of LOOCV log-likelihood of VAR(1) model

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

View source: R/loglikLOOCVcontourVAR1.r

Description

Evaluates the leave-one-out cross-validated log-likelihood of the VAR(1) model for a given grid of the ridge penalty parameters (λ_a and λ_{ω}) for the autoregression coefficient matrix \mathbf{A} and the inverse error covariance matrix \mathbf{Ω}_{\varepsilon} (=\mathbf{Σ_{\varepsilon}^{-1}}), respectively). The result is plotted as a contour plot, which facilitates the choice of optimal penalty parameters. The function also works with a (possibly) unbalanced experimental set-up. The VAR(1)-process is assumed to have mean zero.

Usage

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loglikLOOCVcontourVAR1(lambdaAgrid, lambdaPgrid, Y, figure=TRUE, 
                       verbose=TRUE, ...)

Arguments

lambdaAgrid

A numeric of length larger than one, comprising positive numbers only. It contains the grid points corresponding to the λ_a (the penalty parameter for the autoregression coefficient matrix \mathbf{A}).

lambdaPgrid

A numeric of length larger than one, comprising positive numbers only. It contains the grid points corresponding to the λ_{ω} (the penalty parameters for the inverse error covariance matrix \mathbf{Ω}_{\varepsilon} (=\mathbf{Σ_{\varepsilon}^{-1}})).

Y

Three-dimensional array containing the data. The first, second and third dimensions correspond to covariates, time and samples, respectively. The data are assumed to centered covariate-wise.

figure

A logical, indicating whether the contour plot should be generated.

verbose

A logical indicator: should intermediate output be printed on the screen?

...

Other arguments to be passed on (indirectly) to ridgeVAR1.

Value

A list-object with slots:

lambdaA

A numeric with the grid points corresponding to λ_a (the penalty parameter for the autoregression coefficient matrix \mathbf{A}).

lambdaP

A numeric with the grid points corresponding to λ_{ω} (the penalty parameter for the inverse error covariance matrix \mathbf{Ω}_{\varepsilon} (=\mathbf{Σ_{\varepsilon}^{-1}})).

llLOOCV

A matrix of leave-one-out cross-validated log-likelihoods. Rows and columns correspond to λ_a and λ_{ω} values, respectively.

Note

Internally, this function calls the loglikLOOCVVAR1-function, which evaluates the minus (!) LOOCV log-likelihood (for practical reasons). For interpretation purposes loglikLOOCVcontourVAR1 provides the regular LOOCV log-likelihood (that is, without the minus).

Author(s)

Wessel N. van Wieringen <[email protected]>

References

Miok, V., Wilting, S.M., Van Wieringen, W.N. (2017), "Ridge estimation of the VAR(1) model and its time series chain graph from multivariate time-course omics data", Biometrical Journal, 59(1), 172-191.

See Also

loglikLOOCVVAR1.

Examples

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# set dimensions (p=covariates, n=individuals, T=time points)
p <- 3; n <- 4; T <- 10

# set model parameters
SigmaE <- diag(p)/4
A <- createA(p, "chain")

# generate data
Y <- dataVAR1(n, T, A, SigmaE)

## plot contour of cross-validated likelihood
## Not run:  lambdaAgrid <- seq(0.01, 1, length.out=20) 
## Not run:  lambdaPgrid <- seq(0.01, 1000, length.out=20) 
## Not run:  loglikLOOCVcontourVAR1(lambdaAgrid, lambdaPgrid, Y) 

## determine optimal values of the penalty parameters
## Not run: optLambdas <- constrOptim(c(1,1), loglikLOOCVVAR1, gr=NULL, 
## Not run:               ui=diag(2), ci=c(0,0), Y=Y, 
## Not run:               control=list(reltol=0.01))$par 

## add point of optimum
## Not run:  points(optLambdas[1], optLambdas[2], pch=20, cex=2, 
## Not run:  col="red") 

ragt2ridges documentation built on Nov. 21, 2017, 5:06 p.m.