Nothing
## ---- eval=FALSE--------------------------------------------------------------
# install.packages("HydeNet")
## ---- eval=FALSE--------------------------------------------------------------
# setRepositories(ind=1:2)
# devtools::install_github("nutterb/HydeNet")
## -----------------------------------------------------------------------------
library(HydeNet)
mtcars2 <- transform(mtcars,
cyl = factor(cyl),
gear=factor(gear),
am = factor(am))
## -----------------------------------------------------------------------------
carNet <- HydeNetwork(~ cyl
+ disp | cyl
+ hp | disp
+ wt
+ gear
+ mpg | disp*hp*wt*gear,
data=mtcars2)
## ---- fig.width = 5, eval=FALSE-----------------------------------------------
# plot(carNet)
## -----------------------------------------------------------------------------
HydeNet:::writeJagsModel(carNet, node = "cyl")
HydeNet:::writeJagsModel(carNet, node = "mpg")
writeNetworkModel(carNet, pretty = TRUE)
## -----------------------------------------------------------------------------
carNet1 <- compileJagsModel(carNet)
## -----------------------------------------------------------------------------
carNet2 <- compileJagsModel(carNet, data = list(cyl = "8") )
## -----------------------------------------------------------------------------
carNet3 <- compileJagsModel(carNet, data=list(cyl="8"))
## ---- fig.width=6, fig.height=6-----------------------------------------------
post1 <- HydePosterior(carNet1,
variable.names = c("cyl","hp","mpg"),
n.iter = 10000,
bind=FALSE)
post2 <- HydePosterior(carNet2,
variable.names = c("cyl","hp","mpg"),
n.iter = 10000,
bind = FALSE)
str(post1, max.level = 3)
plot(post1$codas[,c("hp","mpg")])
## -----------------------------------------------------------------------------
bp1 <- bindPosterior(post1)
bp2 <- bindPosterior(post2)
head(bp1)
head(bp2) #notice cyl = "8" for all samples
plot(density(bp1$hp), ylim=c(0,0.06), main = "hp");
lines(density(bp2$hp), col="red", lty=5)
plot(density(bp1$mpg), main = "mpg");
lines(density(bp2$mpg), col="red", lty=5)
## ---- eval=FALSE--------------------------------------------------------------
# options(Hyde_fitModel=FALSE)
## ---- echo=FALSE--------------------------------------------------------------
data(PE, package='HydeNet')
autoNet <- HydeNetwork(~ wells
+ pe | wells
+ d.dimer | pregnant*pe
+ angio | pe
+ treat | d.dimer*angio
+ death | pe*treat,
data = PE)
autoNet <- setNode(autoNet, treat,
nodeFormula = treat ~ poly(d.dimer, 2) + angio,
prob = fromData())
## ---- echo=FALSE--------------------------------------------------------------
print(autoNet, treat)
## ---- echo=FALSE--------------------------------------------------------------
library(HydeNet)
options(Hyde_fitModel=TRUE)
data(PE, package='HydeNet')
autoNet <- HydeNetwork(~ wells
+ pe | wells
+ d.dimer | pregnant*pe
+ angio | pe
+ treat | d.dimer*angio
+ death | pe*treat,
data = PE)
autoNet <- setNode(autoNet, treat,
nodeFormula = treat ~ poly(d.dimer, 2) + angio,
prob = fromData())
print(autoNet, treat)
## -----------------------------------------------------------------------------
library(HydeNet)
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)
## -----------------------------------------------------------------------------
library(HydeNet)
options(Hyde_maxDigits=2)
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)
## ---- eval=FALSE--------------------------------------------------------------
# > #* Object based on a list of models
# > 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 <- glm(angio ~ pe, data=PE, family="binomial")
# > g6 <- glm(treat ~ d.dimer + angio, data=PE, family="binomial")
# > g7 <- glm(death ~ pe + treat, data=PE, family="binomial")
#
# > bagOfModels <- list(g1,g2,g3,g4,g5,g6,g7)
# > bagNet <- HydeNetwork(bagOfModels)
#
# > #* Time to print bagNet implicitly
# > a <- Sys.time()
# > bagNet
# > b <- Sys.time()
# > b-a
# Time difference of 33.53736 secs
#
# > #* Time to print bagNet explicitly
# > a <- Sys.time()
# > print(bagNet)
# > b <- Sys.time()
# > b-a
# Time difference of 0 secs
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