bn.fit utilities: Utilities to manipulate fitted Bayesian networks

Description

Assign or extract various quantities of interest from an object of class bn.fit, bn.fit.dnode or bn.fit.gnode.

Usage

## methods available for "bn.fit"
  ## S3 method for class 'bn.fit':
fitted(object, ...)
  ## S3 method for class 'bn.fit':
coef(object, ...)
  ## S3 method for class 'bn.fit':
residuals(object, ...)
  ## methods available for "bn.fit.dnode"
  ## S3 method for class 'bn.fit.gnode':
coef(object, ...)
  ## methods available for "bn.fit.gnode"
  ## S3 method for class 'bn.fit.gnode':
fitted(object, ...)
  ## S3 method for class 'bn.fit.gnode':
coef(object, ...)
  ## S3 method for class 'bn.fit.gnode':
residuals(object, ...)

Arguments

object

an object of class bn.fit, bn.fit.dnode or bn.fit.gnode.

...

additional arguments (currently ignored).

Value

A list with an element for each node in the network (if object has class bn.fit) or a numeric vector (if object has class bn.fit.dnode or bn.fit.gnode).

Details

coef (and its alias coefficients) extracts model coefficients (which are conditional probabilities in discrete networks and linear regression coefficients in Gaussian networks).

residuals (and its alias resid) extracts model residuals and fitted (and its alias fitted.values) extracts fitted values from fitted Gaussian networks.

Examples

Run this code

data(gaussian.test)
res = hc(gaussian.test)
fitted = bn.fit(res, gaussian.test)

coefficients(fitted)
# $A
# (Intercept)
#    1.007493
#
# $B
# (Intercept)
#    2.039499
#
# $C
# (Intercept)           A           B
#    2.001083    1.995901    1.999108
#
# $D
# (Intercept)           B
#    5.995036    1.498395
#
# $E
# (Intercept)
#    3.493906
#
# $F
#  (Intercept)            A            D            E            G
# -0.006047321  1.994853041  1.005636909  1.002577002  1.494373265
#
# $G
# (Intercept)
#    5.028076
#
coefficients(fitted$C)
# (Intercept)           A           B
#    2.001083    1.995901    1.999108
str(residuals(fitted))
# List of 7
#  $ A: num [1:5000] 0.106 -1.255 0.847 -0.174 -0.519 ...
#  $ B: num [1:5000] -0.107 9.295 0.993 1.818 2.473 ...
#  $ C: num [1:5000] -1.01 0.183 -0.677 -0.153 -1.997 ...
#  $ D: num [1:5000] -0.23 0.377 0.518 0.162 -0.22 ...
#  $ E: num [1:5000] -2.612 3.546 0.341 -2.488 0.591 ...
#  $ F: num [1:5000] -0.861 1.271 -0.262 -0.479 -0.782 ...
#  $ G: num [1:5000] 4.1883 -1.3492 -2.6036 1.0574 0.0895 ...

data(learning.test)
res2 = hc(learning.test)
fitted2 = bn.fit(res2, learning.test)

coefficients(fitted2$E)
# , , F = a
#
#    B
# E        a      b      c
#   a 0.1902 0.0126 0.0244
#   b 0.0230 0.0110 0.0234
#   c 0.0230 0.0376 0.1566
#
# , , F = b
#
#    B
# E        a      b      c
#   a 0.0946 0.0166 0.0498
#   b 0.1158 0.0192 0.1062
#   c 0.0258 0.0166 0.0536