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

The predictions at level 0 correspond to predictions based only on the fixed effects estimates. The predictions at level 1 are obtained by adding the best linear predictions of the random effects to the predictions at level 0. See details for interpretation. The function `predint`

will produce 1-alpha confidence intervals based on bootstrap centiles.

1 2 3 4 5 |

`object` |
an |

`level` |
an optional integer vector giving the level of grouping to be used in obtaining the predictions. |

`alpha` |
1- |

`R` |
number of bootstrap replications. |

`seed` |
optional random number generator seed. |

`...` |
not used. |

As discussed by Geraci and Bottai (2014), integrating over the random effects will give "weighted averages" of the cluster-specific quantile effects. These may be interpreted strictly as population regression quantiles only for the median (`tau=0.5`

). Therefore, predictions at the population level (`code=0`

) should be interpreted analogously.

a vector or a matrix of predictions for `predict.lqmm`

. A data frame or a list of data frames for `predint.lqmm`

containing predictions, lower and upper bounds of prediction intervals, and standard errors.

Marco Geraci

Geraci M and Bottai M (2014). Linear quantile mixed models. Statistics and Computing, 24(3), 461–479.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 | ```
## Orthodont data
data(Orthodont)
# Random intercept model
fitOi.lqmm <- lqmm(distance ~ age, random = ~ 1, group = Subject,
tau = c(0.1,0.5,0.9), data = Orthodont)
# Predict (y - Xb)
predict(fitOi.lqmm, level = 0)
# Predict (y - Xb - Zu)
predict(fitOi.lqmm, level = 1)
# 95% confidence intervals
predint(fitOi.lqmm, level = 0, alpha = 0.05)
``` |

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