# \donttest{
## artificial example data
data("DepressionDemo", package = "glmertree")
## fit normal linear regression LMM tree for continuous outcome
lt <- lmertree(depression ~ treatment | cluster | age + anxiety + duration,
data = DepressionDemo)
print(lt)
plot(lt, which = "all") # default behavior, may also be "tree" or "ranef"
coef(lt)
ranef(lt)
predict(lt, type = "response") # default behavior, may also be "node"
predict(lt, re.form = NA) # excludes random effects, see ?lme4::predict.merMod
residuals(lt)
VarCorr(lt) # see lme4::VarCorr
## fit logistic regression GLMM tree for binary outcome
gt <- glmertree(depression_bin ~ treatment | cluster | age + anxiety + duration,
data = DepressionDemo)
print(gt)
plot(gt, which = "all") # default behavior, may also be "tree" or "ranef"
coef(gt)
ranef(gt)
predict(gt, type = "response") # default behavior, may also be "node" or "link"
predict(gt, re.form = NA) # excludes random effects, see ?lme4::predict.merMod
residuals(gt)
VarCorr(gt) # see lme4::VarCorr
## Alternative specification for binomial family: no. of successes and failures
DepressionDemo$failures <- as.numeric(DepressionDemo$depression_bin) - 1
DepressionDemo$successes <- 1 - DepressionDemo$failures
gt <- glmertree(cbind(failures, successes) ~ treatment | cluster | age + anxiety + duration,
data = DepressionDemo, ytype = "matrix") ## see also ?partykit::mob_control
# }
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