Nothing
## ---- eval=c(2,3), echo=2-----------------------------------------------------
install.packages("HydeNet")
library(HydeNet)
options(Hyde_fitModel = FALSE)
## ---- fig.width=7, eval=1-----------------------------------------------------
net <- HydeNetwork(~ wells
+ pe | wells
+ d.dimer | pregnant*pe
+ angio | pe
+ treat | d.dimer*angio
+ death | pe*treat)
plot(net)
## ---- echo=FALSE--------------------------------------------------------------
net
## -----------------------------------------------------------------------------
data(PE, package='HydeNet')
autoNet <- HydeNetwork(~ wells
+ pe | wells
+ d.dimer | pregnant*pe
+ angio | pe
+ treat | d.dimer*angio
+ death | pe*treat,
data = PE)
writeNetworkModel(autoNet, pretty=TRUE)
## -----------------------------------------------------------------------------
glm(treat ~ d.dimer+angio, data=PE, family="binomial")$coef
## -----------------------------------------------------------------------------
xtabs(~PE$pregnant) / nrow(PE)
## -----------------------------------------------------------------------------
g1 <- lm(wells ~ 1, data=PE)
g2 <- glm(pe ~ wells, data=PE, family="binomial")
g3 <- lm(d.dimer ~ pe + pregnant, data=PE)
g4 <- xtabs(~ pregnant, data=PE)
g5 <- cpt(angio ~ pe, data=PE)
g6 <- glm(treat ~ d.dimer + angio, data=PE, family="binomial")
g7 <- cpt(death ~ pe + treat, data=PE)
bagOfModels <- list(g1,g2,g3,g4,g5,g6,g7)
bagNet <- HydeNetwork(bagOfModels)
writeNetworkModel(bagNet, pretty=TRUE)
## -----------------------------------------------------------------------------
net <- setNode(network = net, node = pregnant,
nodeType = "dbern", prob=.4)
net
## -----------------------------------------------------------------------------
net <- setNode(net, wells,
nodeType = "dnorm",
mean = 5, sd = 1.5)
net$nodeType$wells
net$nodeParams$wells
## -----------------------------------------------------------------------------
net <- setNode(net, wells,
nodeType = "dcat",
pi = vectorProbs(p = c(.3, .6, .1), wells) )
net$nodeType$wells
net$nodeParams$wells
## -----------------------------------------------------------------------------
net <- setNode(net, wells,
nodeType = "dcat",
pi = vectorProbs(p = c(37, 162, 48), wells) )
net$nodeType$wells
net$nodeParams$wells
## -----------------------------------------------------------------------------
net <- setNode(net, wells,
nodeType = "dcat",
pi = "pi.wells[1] <- 0.15; pi.wells[2] <- 0.66; pi.wells[3] <- 0.19")
## ---- eval=FALSE--------------------------------------------------------------
# data(jagsDists, package='HydeNet')
# jagsDists[,c(1:3, 6:8)]
## ---- echo = FALSE------------------------------------------------------------
knitr::kable(jagsDists[, c(1:3, 6:8)])
## ---- eval=FALSE--------------------------------------------------------------
# net <- setNode(net, XYZ, nodeType = "dweib", shape=2, scale=5)
## ---- error=TRUE--------------------------------------------------------------
net <- setNode(net, d.dimer, nodeType = "dpois", lambda=-10)
## -----------------------------------------------------------------------------
net <- setNode(net, d.dimer, nodeType="dnorm",
mean=fromFormula(), sd=sqrt(30), #sigma^2 = 30
nodeFormula = d.dimer ~ 210 + 29*pregnant + 68*pe)
net$nodeType$d.dimer
net$nodeParams$d.dimer
net$nodeFormula$d.dimer
## ---- eval=FALSE--------------------------------------------------------------
# net <- setNode(net, d.dimer, nodeType="dnorm",
# mean="210 + 29*pregnant + 68*pe", sd = sqrt(30))
## ---- eval=FALSE--------------------------------------------------------------
# net <- setNode(net, d.dimer, nodeType="dt",
# mean="210 + 29*pregnant + 68*pe", sd=sqrt(20), df=2)
## ---- eval=FALSE--------------------------------------------------------------
# data(jagsFunctions, package='HydeNet')
# jagsFunctions
## ---- echo = FALSE------------------------------------------------------------
knitr::kable(jagsFunctions)
## -----------------------------------------------------------------------------
equation <- "-6.3 + 0.02*d.dimer + 2.9*angio - 0.005*d.dimer*angio"
net <- setNode(net, treat, nodeType="dbern",
prob=paste("ilogit(", equation, ")"),
validate=FALSE)
## -----------------------------------------------------------------------------
bagNet$nodeType$d.dimer
bagNet$nodeParams$d.dimer
bagNet$nodeFormula$d.dimer
## -----------------------------------------------------------------------------
new.DDimer.Model <- lm(d.dimer ~ pe * pregnant, data=PE)
bagNet <- setNodeModels(bagNet, new.DDimer.Model)
writeNetworkModel(bagNet, pretty=TRUE)
## -----------------------------------------------------------------------------
h <- cpt(death ~ pe + treat, data=PE)
## ---- eval=FALSE--------------------------------------------------------------
# h <- inputCPT(test ~ disease)
## ---- eval=FALSE--------------------------------------------------------------
# print(h)
## ---- fig.width=3, eval = -10-------------------------------------------------
craps <- HydeNetwork(~ d1 + d2 + diceSum | d1*d2
+ firstRollOutcome | diceSum)
craps <- setNode(craps, d1, nodeType="dcat",
pi = vectorProbs(p = rep(1/6,6), d1),
validate = FALSE)
craps <- setNode(craps, d2, nodeType="dcat",
pi = vectorProbs(p = rep(1/6,6), d2),
validate = FALSE)
craps <- setNode(craps, diceSum, nodeType = "determ",
define = fromFormula(),
nodeFormula = diceSum ~ di1 + di2)
craps <- setNode(craps, firstRollOutcome, nodeType = "determ",
define = fromFormula(),
nodeFormula = firstRollOutcome ~
ifelse(diceSum < 4 | diceSum > 11, -1,
ifelse(diceSum == 7 | diceSum == 11, 1,0)))
plot(craps)
## -----------------------------------------------------------------------------
writeNetworkModel(craps, pretty=TRUE)
## ---- fig.width=7, eval=1-----------------------------------------------------
net2 <- update(net, . ~ . + newTest | pe
+ treat | newTest
- pregnant)
plot(net2)
## -----------------------------------------------------------------------------
net2
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