LQMM used by researchers at the National Aeronautics and Space Administration (NASA). The abstract cites “The data are longitudinal and result from a relatively few number of subjects; typically 10 - 20. A longitudinal study refers to an investigation where participant outcomes and possibly treatments are collected at multiple follow-up times. Standard statistical designs such as mean regression with random effects and mixed-effects regression are inadequate for such data because the population is typically not approximately normally distributed. Hence, more advanced data analysis methods are necessary. This research focuses on four such methods for longitudinal data analysis: the recently proposed linear quantile mixed models (lqmm) [...]“. The full technical report is available here.
Read my #OpenAccess article ‘A Gradient Search Maximization Algorithm for the Asymmetric Laplace Likelihood’ at http://bit.ly/1j99BKN!
The new version of lqmm with bug fixes, amendments and new features is now on CRAN. A vignette is also available Geraci M (2014). Linear Quantile Mixed Models: The lqmm Package for Laplace Quantile Regression, Journal of Statistical Software 57(13), 1-29.
The R package pawacc in now available on CRAN. This package collects functions to read, process and store accelerometer files. Models supported: Actigraph GT1M, GT3X and ActiSleep.
A typical summary statistic for temporal trends is the average percent change (APC). The APC is estimated by using a generalized linear model, usually under the assumption of linearity on the logarithmic scale. A serious limitation of least-squares type estimators is their sensitivity to outliers. We propose a robust and easy-to-compute measure of the temporal trend based on the median of the rates (median percent change – MPC), rather than their mean, under the hypothesis of constant relative change.
An overview on recent advances in transformations toward linearity (Royal Statistical Society 2013 International Conference, Newcastle). Download here.