Bayesian Networks in the Presence of Temporal Information - Bayesian Networks in R: With Applications in Systems Biology - page 77

Bayesian Information Criterion

50

0

−50

−100

0 1 2 3 4 5

Penalty level

Fig. 3.6 Graphical output of the simone package for dynamic Bayesian network learning via BIC
criterion minimization

257710_at 255764_at 255070_at 253425_at
0.0000000 0.0000000 0.0000000 0.0000000
253174_at 251324_at 245319_at 245094_at
0.0000000 0.0000000 0.0000000 -0.6961228
In addition to the LASSO, the lars package implements Least Angle Regression
(LAR) from Efron et al. ( 2004 ) and stepwise regression. Both can be fitted with the
same functions used for the LASSO by setting the type arguments of lars and
cv.lars to either "lar" or "stepwise" , as shown below.
> lar.fit = lars(y = y, x = x, type = "lar")
> plot(lar.fit)
> lar.cv = cv.lars(y = y, x = x, type = "lar")
> step.fit = lars(y = y, x = x, type = "stepwise")
> plot(step.fit)
> step.cv = cv.lars(y = y, x = x, type = "stepwise")
The simone package for Statistical Inference for MOdular NEtworks by
Chiquet et al. ( 2009 ) provides an implementation of the LASSO specifically tar-
geted to dynamic Bayesian network learning. Model estimation is performed using
the simone function with clustering = FALSE .
> library(simone)
> simone(arth12, type = "time-course")
The simone function allows clustering assumption, i.e., modular network. Model
estimation is now performed using the simone function with clustering.
> ctrl = setOptions(clusters.crit = "BIC")
> simone(arth12, type = "time-course",
+ clustering = TRUE, control = ctrl)
The optimal value of the L 1 penalty can be chosen by minimizing the BIC cri-
terion, which is computed by simone when the model is fitted with output =
"BIC" .