# GrowthCurveDemo

##### Artificial dataset for partitioning of linear growth curve models

Artificial dataset to illustrate fitting of LMM trees with growth curve models in the terminal nodes.

- Keywords
- datasets

##### Usage

`data("GrowthCurveDemo")`

##### Details

Data were generated so that `x1`

, `x2`

and `x3`

are
true partitioning variables, `x4`

through `x8`

are noise
variables. The (potential) partitioning variables are time invariant.
Time-varying covariates can also be included in the model. For partitioning
growth curves these should probably not be potential partitioning variables,
as this could result in observations from the same person ending up in
different terminal nodes. Thus, time-varying covariates are probably
best included as predictors in the node-specific regression model. E.g.:
`y ~ time + timevarying_cov | person | x1 + x2 + x3 + x4`

.

##### Format

A data frame containing 1250 repeated observations on 250 persons. x1 - x8 are time-invariant partitioning variables. Thus, they are measurements on the person (i.e., cluster) level, not on the individual observation level.

- person
numeric. Indicator linking repeated measurements to persons.

- time
factor. Indicator for timepoint.

- y
numeric. Response variable.

- x1
numeric. Potential partitioning variable.

- x2
numeric. Potential partitioning variable.

- x3
numeric. Potential partitioning variable.

- x4
numeric. Potential partitioning variable.

- x5
numeric. Potential partitioning variable.

- x6
numeric. Potential partitioning variable.

- x7
numeric. Potential partitioning variable.

- x8
numeric. Potential partitioning variable.

##### See Also

##### Examples

```
# NOT RUN {
data("GrowthCurveDemo", package = "glmertree")
head(GrowthCurveDemo)
## Fit LMM tree with a random intercept w.r.t. person:
form <- y ~ time | person | x1 + x2 + x3 + x4 + x5 + x6 + x7 + x8
lt.default <- lmertree(form, data = GrowthCurveDemo)
plot(lt.default, which = "tree") ## yields too large tree
VarCorr(lt.default)
## Account for measurement level of the partitioning variables:
lt.cluster <- lmertree(form, cluster = person, data = GrowthCurveDemo)
plot(lt.cluster, which = "tree") ## yields correct tree
VarCorr(lt.cluster) ## yields slightly larger ranef variance
## Fit LMM tree with random intercept and random slope of time w.r.t. person:
form.s <- y ~ time | (1 + time | person) | x1 + x2 + x3 + x4 + x5 + x6 + x7 + x8
lt.s.cluster <- lmertree(form.s, cluster = person, data = GrowthCurveDemo)
plot(lt.s.cluster, which = "tree") ## same tree as before
VarCorr(lt.s.cluster)
# }
```

*Documentation reproduced from package glmertree, version 0.2-0, License: GPL-2 | GPL-3*