wafers

This data set was reported and analyzed by Robinson et al. (2006) and reanalyzed by Lee et al. (2011). The data 
  <dquote>deal with wafers in a single etching process in semiconductor manufacturing.
Wafers vary through time since there are some variables that are not perfectly controllable in the etching process. For this
reason, wafers produced on any given day (batch) may be different from those produced on another day (batch). To measure
variation over batch, wafers are tested by choosing several days at random. In this data, resistivity is the response of interest.
There are three variables, gas flow rate (x1), temperature (x2), and pressure (x3) and one random effect (batch or day).</dquote>  (Lee et al 2011).

datasets

Implements a collection of functions for inference in mixed models. It was developed in particular for GLMMs with spatial correlations, but also fits models with non-Gaussian random effects (e.g., Beta Binomial, or negative-binomial mixed models). Heteroskedasticity can further be fitted by a linear model. The algorithms are currently various Laplace approximations methods for ML or REML, in particular h-likelihood and penalized-likelihood methods.  

Franois Rousset

spaMM

Mixed Models, Particularly Spatial GLMMs

wafers function

This data set was manually pasted from Table 3 of Lee et al. (2011). Transcription errors may have occurred.

wafers: Data from a resistivity experiment for semiconductor materials.

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