MHserviceDemo: Artificial mental-health service outcomes dataset

Description

Artificial dataset of treatment outcomes (N = 3739) of 13 mental-health services to illustrate fitting of (G)LMM trees with constant fits in terminal nodes.

Usage

data("MHserviceDemo")

Arguments

Format

A data frame containing 3739 observations on 8 variables:

age: numeric. Variable representing age in years (range: 4.8 - 23.6, M = 11.46).
impact: numeric. Continuous variable representing severity of and impairment due to mental-health problems at baseline. Higher values indicate higher severity and impairment.
gender: factor. Indicator for gender.
emotional: factor. Indicator for presence of emotional disorder at baseline.
autism: factor. Indicator for presence of autistic disorder at baseline.
conduct: factor. Indicator for mental-health service provider.
cluster_id: factor. Binarized treatment outcome variable (0 = recovered, 1 = not recovered.
outcome: numeric. Variable representing treatment outcome as measured by a total mental-health difficulties score assessed about 6 months after baseline, corrected for the baseline assessment. Higher values indicate poorer outcome.

Details

Dataset was modelled after Edbrooke-Childs et al. (2017), who analyzed a sample of $N = 3,739$ young people who received treatment at one of 13 mental-health service providers in the UK. Note that the data were artificially generated and do not reflect actual empirical findings.

Examples

Run this code

# \donttest{
data("MHserviceDemo", package = "glmertree")
summary(MHserviceDemo)
lt <- lmertree(outcome ~ 1 | cluster_id | age + gender + emotional + 
               autism + impact + conduct, data = MHserviceDemo)
plot(lt)

gt <- glmertree(factor(outcome > 0) ~ 1 | cluster_id | age + gender + 
                emotional + autism + impact + conduct, 
                data = MHserviceDemo, family = "binomial")
plot(gt)
# }