Fitting spatial models in the R package: hglm

We present a new version of the hglm package for fittinghierarchical generalized linear models (HGLM) with spatially correlated random effects. A CAR family for conditional autoregressive random effects was implemented. Eigen decomposition of the matrix describing the spatial structure (e.g. the neighborhood matrix) was used to transform the CAR random effectsinto an independent, but heteroscedastic, gaussian random effect. A linear predictor is fitted for the random effect variance to estimate the parameters in the CAR model.This gives a computationally efficient algorithm for moderately sized problems (e.g. n<5000).

Fitting conditional and simultaneous autoregressive spatial models in hglm

We present a new version (> 2.0) of the hglm package for fitting hierarchical generalized linear models (HGLMs) with spatially correlated random effects. CAR() and SAR() families for conditional and simultaneous autoregressive random effects were implemented. Eigen decomposition of the matrix describing the spatial structure (e.g., the neighborhood matrix) was used to transform the CAR/SAR random effects into an independent, but eteroscedastic, Gaussian random effect. A linear predictor is fitted for the random effect variance to estimate the parameters in the CAR and SAR models. This gives a computationally efficient algorithm for moderately sized problems.

Processes and variations in language economisation

This article analyses the processes of reducing language in textchats produced by non-native speakers of English. We propose that forms are reduced because of their high frequency and because of the discourse context. A wide variety of processes are attested in the literature, and we find different forms of clippings in our data, including mixtures of different clippings, homophone respellings, phonetic respellings including informal oral forms, initialisms (but no acronyms), and mixtures of clipping together with homophone and phonetic respellings. Clippings were the most frequent process (especially back-clippings and initialisms), followed by homophone respellings. There were different ways of metalinguistically marking reduction, but capitalisation was by far the most frequent. There is much individual variation in the frequencies of the different processes, although most were within normal distribution. The fact that nonnative speakers seem to generally follow reduction patterns of native speakers suggests that reduction is a universal process.