bnboot {bnlearn} | R Documentation |
Apply a user-specified function to Bayesian networks learned from bootstrap samples of the original data.
bnboot(data, statistic, R = 200, m = nrow(data), sim = "ordinary", algorithm, algorithm.args = list(), statistic.args = list(), debug = FALSE)
data |
a data frame, containing the variables in the model. |
statistic |
a function or a character string (the name of a function) to be applied to each bootstrap replicate. |
R |
a positive integer, the number of bootstrap replicates. |
m |
a positive integer, the size of each bootstrap replicate. |
sim |
a character string indicating the type of simulation
required. Possible values are "ordinary" (the default)
and "parametric" . |
algorithm |
a character string, the learning algorithm to be
applied to the bootstrap replicates. Possible values are gs ,
iamb , fast.iamb , inter.iamb , mmpc
and hc . See bnlearn-package and
documentation of each algorithm for details. |
algorithm.args |
a list of extra arguments to be passed to the learning algorithm. |
statistic.args |
a list of extra arguments to be passed to
the function specified by statistic . |
debug |
a boolean value. If TRUE a lot of debugging output
is printed; otherwise the function is completely silent. |
A list containing the results of the calls to statistic
.
Marco Scutari
Friedman N, Goldszmidt M, Wyner A (1999). "Data Analysis with Bayesian Networks: A Bootstrap Approach". In "UAI '99: Proceedings of the 15th Annual Conference on Uncertainty in Artificial Intelligence", pp. 196-20. Morgan Kaufmann.
constraint-based algorithms
,
local discovery algorithms
,
score-based algorithms
, hybrid algorithms
.
## Not run: data(learning.test) bnboot(data = learning.test, R = 2, m = 500, algorithm = "gs", statistic = arcs) # [[1]] # from to # <arcs for the first replicate> # # [[2]] # from to # <arcs for the second replicate> ## End(Not run)