| bn.fit utilities {bnlearn} | R Documentation |
Assign or extract various quantities of interest from an
object of class bn.fit, bn.fit.dnode or
bn.fit.gnode.
## 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, ...)
object |
an object of class bn.fit, bn.fit.dnode
or bn.fit.gnode. |
... |
additional arguments (currently ignored). |
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.
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).
Marco Scutari
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