scaleLocAdjust: Estimate dispersion as a function of predicted values
In qra: Quantal Response Analysis for Dose-Mortality Data

Description Usage Arguments Details Value Note See Also Examples

A loess smooth is applied to the square roots of the standardized deviance residuals. The inverses of values from the smooth, raised to the power of lambda, are then used as prior weights to update the model. A value of lambda that is a little more than 2.0 has often worked well.

1	scaleLocAdjust(x, lambda = 2, span = 0.75)

`x`	Model fitted using `glm` or, possibly `lm`
`lambda`	Power of smooth of square roots of absolute values of residuals, to try for values whose inverses will be used as weights
`span`	span parameter for use in smoothing the square root of standardized deviance residuals.

This function is primarily for experimental use, in investigating possible ways to model a dispersion factor that varies with the fitted value.

A list, with elements

`model`	Model updated to use the newly calculated weights
`estDisp`	Estimated dispersions

The dispersion estimates that correspond to the updated model are obtained by dividing the dispersion value given by summary() for the updated model by the (prior) weights supplied when the model was updated. The approach for obtaining varying dispersion estimates is used because, empirically, it has been found to work well for at least some sets of data. In particular, there seems no obvious theoretical basis for the choice of lambda. In the example given, used because the data is publicly available, the method has limited success.

checkDisp

ROYAL <- subset(qra::codling1988, Cultivar=="ROYAL")
ROYAL.glm <- glm(cbind(dead,total-dead)~ct, data=ROYAL,
                  family=quasibinomial(link='cloglog'))
ROYALFix <- qra::scaleLocAdjust(ROYAL.glm)
## Check range of indicated prior weights
range(ROYALFix[[2]])
## Range of updated dispersion estimates
range(summary(ROYALFix[[1]])[['dispersion']]/ROYALFix[[2]])