bn.var {bnlearn} | R Documentation |
Measure the variability of the structure of a Bayesian network.
# first and second moments' estimation bn.moments(data, R = 200, m = nrow(data), algorithm, algorithm.args = list(), reduce = NULL, debug = FALSE) # descriptive statistics bn.var(x, method) # Monte Carlo test for entropy bn.var.test(x, method, R, B, debug = FALSE)
data |
a data frame, containing the variables in the model. |
R |
a positive integer, the number of bootstrap replicates (in
bn.moments ) or the number of Monte Carlo samples (in
bn.var.test ). |
m, B |
a positive integer, the size of each bootstrap (in
bn.moments ) or Monte Carlo (in bn.var.test ) replicate. |
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 the
documentation of each algorithm for details. |
algorithm.args |
a list of extra arguments to be passed to the learning algorithm. |
x |
a covariance matrix or an object of class mvber.moments
(the return value of the bn.moments function). |
method |
a character string, the label of the statistic used
in bn.var or bn.var.test . Possible values are
tvar (total variance), gvar (generalized
variance ), nvar (Frobenius matrix norm, which is
equivalent to Nagao's test). |
reduce |
a character string, either first or second .
If first all the arcs with first moment equal to zero are
dropped; if if second all the arcs with zero variance
are dropped. |
debug |
a boolean value. If TRUE a lot of debugging output
is printed; otherwise the function is completely silent. |
bn.moments
returns an object of class mvber.moments
.
bn.var
returns a vector of two elements, the observed value of
the statistic (named statistic
) and its normalized equivalent
(named normalized
).
bn.var.test
returns an object of class htest
.
These functions are experimental implementations of techniques still in development; their form (name, parameters, etc.) will likely change without notice in the future.
Marco Scutari
Scutari M (2009). "Structure Variability in Bayesian Networks". ArXiv Statistics - Methodology e-prints. http://arxiv.org/abs/0909.1685.
## Not run: z = bn.moments(learning.test, algorithm = "gs", R = 100) bn.var(z, method = "tvar") # statistic normalized # 1.29060 0.34416 bn.var.test(z, method = "nvar") # # squared Frobenius norm # # data: covariance matrix # nvar = 0.5471, B = 5000, R = 100, p-value < 2.2e-16 # alternative hypothesis: true value is greater than 0 ## End(Not run)