Description Usage Arguments Details Value Author(s) See Also Examples

A near-equivalent of `wbm()`

that instead uses Stan,
via rstan and brms.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 | ```
wbm_stan(
formula,
data,
id = NULL,
wave = NULL,
model = "w-b",
detrend = FALSE,
use.wave = FALSE,
wave.factor = FALSE,
min.waves = 2,
model.cor = FALSE,
family = gaussian,
fit_model = TRUE,
balance.correction = FALSE,
dt.random = TRUE,
dt.order = 1,
chains = 3,
iter = 2000,
scale = FALSE,
save_ranef = FALSE,
interaction.style = c("double-demean", "demean", "raw"),
weights = NULL,
offset = NULL,
...
)
``` |

`formula` |
Model formula. See details for crucial
info on |

`data` |
The data, either a |

`id` |
If |

`wave` |
If |

`model` |
One of |

`detrend` |
Adjust within-subject effects for trends in the predictors? Default is FALSE, but some research suggests this is a better idea (see Curran and Bauer (2011) reference). |

`use.wave` |
Should the wave be included as a predictor? Default is FALSE. |

`wave.factor` |
Should the wave variable be treated as an unordered factor instead of continuous? Default is FALSE. |

`min.waves` |
What is the minimum number of waves an individual must
have participated in to be included in the analysis? Default is |

`model.cor` |
Do you want to model residual autocorrelation?
This is often appropriate for linear models ( |

`family` |
Use this to specify GLM link families. Default is |

`fit_model` |
Fit the model? Default is TRUE. If FALSE, only the model code is returned. |

`balance.correction` |
Correct between-subject effects for unbalanced panels following the procedure in Curran and Bauer (2011)? Default is FALSE. |

`dt.random` |
Should the detrending procedure be performed with a random slope for each entity? Default is TRUE but for short panels FALSE may be better, fitting a trend for all entities. |

`dt.order` |
If detrending using |

`chains` |
How many Markov chains should be used? Default is 3, to leave you with one unused thread if you're on a typical dual-core machine. |

`iter` |
How many iterations, including warmup? Default is 2000, leaving 1000 per chain after warmup. For some models and data, you may need quite a few more. |

`scale` |
Standardize predictors? This can speed up model fit. Default is FALSE. |

`save_ranef` |
Save random effect estimates? This can be crucial for predicting from the model and for certain post-estimation procedures. On the other hand, it drastically increases the size of the resulting model. Default is FALSE. |

`interaction.style` |
The best way to calculate interactions in within
models is in some dispute. The conventional way ( |

`weights` |
If using weights, either the name of the column in the data that contains the weights or a vector of the weights. |

`offset` |
this can be used to specify an |

`...` |
Additional arguments passed on to |

See `wbm()`

for details on the formula syntax, model types,
and some other stuff.

A `wbm_stan`

object, which is a list containing a `model`

object
with the `brm`

model and a `stan_code`

object with the model code.

If `fit_model = FALSE`

, instead a list is returned containing a `stan_code`

object and a `stan_data`

object, leaving you with the tools you need to
run the model yourself using `rstan`

.

Jacob A. Long

1 2 3 4 5 6 7 8 |

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