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