PnetHub {Peanut} | R Documentation |
The hub-and-spoke model divides a complete model up into a central hub
model (call a proficiency or competency model in educational
applications) and spoke models (or evidence models) which reference
variables in the hub network. If a network is a spoke, then the field
PnetHub
should be set to the name of the corresponding hub
network.
PnetHub(net) PnetHub(net) <- value
net |
A |
value |
A character scalar giving the name of the new hub network. |
The getter method returns either a character vector of length 1 giving
the name of the hub, or NA
or the empty string if no hub is set.
The setter method returns the net
argument.
Russell Almond
Almond, R. G. & Mislevy, R. J. (1999) Graphical models and computerized adaptive testing. Applied Psychological Measurement, 23, 223–238.
Almond, R., Herskovits, E., Mislevy, R. J., & Steinberg, L. S. (1999). Transfer of information between system and evidence models. In Artificial Intelligence and Statistics 99, Proceedings (pp. 181–186). Morgan-Kaufman
Almond, R. G. (presented 2017, August). Tabular views of Bayesian networks. In John-Mark Agosta and Tomas Singlair (Chair), Bayeisan Modeling Application Workshop 2017. Symposium conducted at the meeting of Association for Uncertainty in Artificial Intelligence, Sydney, Australia. (International) Retrieved from http://bmaw2017.azurewebsites.net/
Pnet
, PnetAdjoin
(for merging hub and
spoke), Qmat2Pnet
, Pnet2Qmat
## Not run: library(PNetica) # Requires PNetica sess <- NeticaSession() startSession(sess) curd <- getwd() setwd(file.path(library(help="PNetica")$path, "testnets")) PM <- ReadNetworks("miniPP-CM.dne", session=sess) stopifnot(PnetHub(PM)=="") EM1 <- ReadNetworks("PPcompEM.dne", session=sess) stopifnot(PnetHub(EM1)=="miniPP_CM") foo <- CreateNetwork("foo",sess) stopifnot(is.na(PnetHub(foo))) PnetHub(foo) <- PnetName(PM) stopifnot(PnetHub(foo)=="miniPP_CM") DeleteNetwork(list(PM,EM1,foo)) stopSession(sess) setwd(curd) ## End(Not run)