Sentence examples for mixed models see from inspiring English sources

To illustrate the interactions of time and exposure, we extracted mean values for residential exposure classes and years from the mixed models (see details in Additional file 1).

When data are missing completely at random or missing at random, likelihood based mixed models (see glossary) can yield unbiased estimates of treatment effect.

For an overview and a comparison of different methods for mixtures of LMs and linear mixed models, see Scharl et al. (2010) in the context of time-course gene expression data analysis.

However, as our method of analysis (linear mixed models, see statistical methods section) does not require list-wise deletion, and therefore loss to follow-up is expected to have negligible impact.

As linear changes are unlikely for patients with SAEs, we assumed that SAEs and reductions in visual acuity caused a sudden drop in utility on the day of onset, followed by a linear rise as the patient recovered; the rate of this linear rise was estimated using mixed models (see online supplementary appendix).

From a certain point of view it can be seen as a mixed model (see next section) when referring to its depth of aggregation.

A generalized form of the mixed model (see Bailey and Alimadhi 2007) was applied to the symptom severity indices, which followed a Poisson distribution.

We tested for the main effects of signal (feedforward and feedback) and depth using a 2 (signal) × 6 (depth) linear mixed model (see the Experimental Procedures).

Some of these contributions also use a stated preference method, acknowledging the original contribution by Hensher (estimated as multinomial logit and mixed logit models– see [6]), while other studies use a different, more traditional method, in which a satisfaction scale is multiplied by an importance scale (in various forms) to obtain an overall customer satisfaction index.

In general, linear fixed effect models provided consistent differential expression evidence as linear mixed effect models but were less powerful since only 16 samples were analyzed each time in contrast to 48 samples used in linear mixed effect models (see Figure S7b for an example).

This rank based analysis has recently been extended to mixed and nonlinear models; see [Kloke et al. (2009]) and [Abebe and McKean (2013]), respectively.