Sentence examples for mixed models have from inspiring English sources

Multiplicative mixed models have recently been proposed for the analysis of data from series of plant variety trials.

Mixed models have the advantage both of allowing for including all data points produced by a participant (instead of computing means for each participant) and accounting for both by-participant and by-item variance.

The probable applications of recent advanced tools of linear and non-linear mixed models like the linear mixed model, generalized linear model, and generalized linear mixed models have been presented.

Two sets of mixed models have been proposed on the basis of individual layer as well as equivalent single layer (ESL) theories by selectively incorporating non-linear components of Green's strain tensor.

Mixed models have been successfully used to address this type of problem (e.g. Lappi 1997; Calama and Montero 2004; Castedo Dorado et al. 2006).

Mixed models have been recommended for analyses of trial data (Gueorguieva & Krystal, 2004).

In addition, the wide range of Poisson and NB panel and mixed models has been developed.

We acknowledge that the attrition has resulted in missing data over time, however the use of linear mixed models has been shown to be robust when missing data depends on baseline values [ 75].

There seems to be little difference whether BH or the mixed model approach is used to estimate FDR for limma and Cyber-T on our data set, but since the BH FDR produce values that tend to increase stepwise whereas the mixed model have a more smooth increase (more obvious if Figure 3C is plotted in higher resolution, not shown) we chose to use the mixed model FDR in the following.

From the forecasting results of weekly prices, the mixed model has better forecasting results of price changes.

By comparing MSE and MAE, we draw conclusion that the mixed model has better forecasting results in most cases.