blup.dosresmeta: Best Linear Unbiased Predictions from dosresmeta Models

In dosresmeta: Multivariate Dose-Response Meta-Analysis

Description

This method function computes (empirical) best linear unbiased predictions from fitted dose-response meta-analytical models represented in objects of class "dosresemta".

Usage

## S3 method for class 'dosresmeta'
blup(object, ...)

Arguments

object	objects of classe "dosresmeta".
...	further arguments passed to or from other methods.

Details

The method function blup produces (empirical) best linear unbiased predictions from dosresmeta objects. Predictions are expressed in terms of study-specific deviations as random effects. Predicted random effects from blup are a shrunk version of study-specific realizations, where study-specific predictions borrow strength from the assumption of an underlying distribution in a (usually hypothetical) population of studies. Blup are not avaialable for fixed-effects models since the are meaningless in that context.

Examples

## Load data and run the linear and quadratic models
data("alcohol_cvd")
lin <- dosresmeta(formula = logrr ~ dose, type = type, id = id,
                  se = se, cases = cases, n = n, data = alcohol_cvd) 
quadr <- dosresmeta(formula = logrr ~ dose + I(dose^2), type = type, id = id,
                    se = se, cases = cases, n = n, data = alcohol_cvd) 

## blup prediction for the previous models
blup(lin)
blup(quadr)

dosresmeta documentation built on May 2, 2019, 6:30 a.m.