Predictions from an lqmm Object
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.
## S3 method for class 'lqmm' predict(object, level = 0, ...) ## S3 method for class 'lqmm' predint(object, level = 0, alpha = 0.05, R = 50, seed = round(runif(1, 1, 10000)))
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.
## 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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