data("WeatherPlay", package = "partykit")
sp_o <- partysplit(1L, index = 1:3)
sp_h <- partysplit(3L, breaks = 75)
sp_w <- partysplit(4L, index = 1:2)
pn <- partynode(1L, split = sp_o, kids = list(
  partynode(2L, split = sp_h, kids = list(
    partynode(3L, info = "yes"),
    partynode(4L, info = "no"))),
  partynode(5L, info = "yes"),
  partynode(6L, split = sp_w, kids = list(
    partynode(7L, info = "yes"),
    partynode(8L, info = "no")))))
py <- party(pn, WeatherPlay)

autoplot(py)

n1 <- partynode(id = 1L, split = sp_o, kids = lapply(2L:4L, partynode))
t2 <- party(n1,
            data = WeatherPlay,
            fitted = data.frame(
              "(fitted)" = fitted_node(n1, data = WeatherPlay),
              "(response)" = WeatherPlay$play,
              check.names = FALSE),
            terms = terms(play ~ ., data = WeatherPlay)
)
t2 <- as.constparty(t2)            
            
autoplot(t2)

## Boston housing data
data("BostonHousing", package = "mlbench")
BostonHousing <- transform(BostonHousing,
                           chas = factor(chas, levels = 0:1, labels = c("no", "yes")),
                           rad = factor(rad, ordered = TRUE))

## linear model tree
bh_tree <- lmtree(medv ~ log(lstat) + I(rm^2) | zn +
                    indus + chas + nox + age + dis + rad + tax + crim + b + ptratio,
                  data = BostonHousing, minsize = 40)

autoplot(bh_tree, plot_var = "log(lstat)", show_fit = FALSE)

autoplot(bh_tree, plot_var = "I(rm^2)", show_fit = TRUE)

data("GBSG2", package = "TH.data")
GBSG2$time <- GBSG2$time/365

library("survival")
wbreg <- function(y, x, start = NULL, weights = NULL, offset = NULL, ...) {
  survreg(y ~ 0 + x, weights = weights, dist = "weibull", ...)
}


logLik.survreg <- function(object, ...)
  structure(object$loglik[2], df = sum(object$df), class = "logLik")

gbsg2_tree <- mob(Surv(time, cens) ~ horTh + pnodes | age + tsize +
                    tgrade + progrec + estrec + menostat, data = GBSG2,
                  fit = wbreg, control = mob_control(minsize = 80))

data("TeachingRatings", package = "AER")
tr <- subset(TeachingRatings, credits == "more")

tr_tree <- lmtree(eval ~ beauty | minority + age + gender + division + native +
                    tenure, data = tr, weights = students, caseweights = FALSE)

autoplot(tr_tree)