Sentence examples for mixed model predictions from inspiring English sources

The mean CD4 count and percentage evolution were depicted by plotting the mixed model predictions.

The fundamental reason for this failure is that mixed model predictions of merit using the relationship matrix and applied to young animals can, with sufficient historical data, reliably predict the parent average (PA) effects, but are unable to predict the Mendelian sampling effects without having phenotypic observations on the individual or its descendants [ 6].

The additive relationship matrix plays an important role in mixed model prediction of breeding values.

Mixed model prediction of breeding values was conducted with the model (26) y = μ 1 + a + ε where y is the vector of phenotypes, μ is a fixed effect, 1 is a column vector of ones, a ∼ N (0, A ^ σ 2 ) is the vector of breeding values with estimated relationship matrix A ^, and the residuals are ε ∼ N (0, I σ e 2 ).

Estimation of additional effects of GA on PK parameters (i.e., not related to change in GFR) did not further improve model fit, indicating that the pregnancy-related effects could be fully explained by inclusion of the CrCL mixed effect model predictions.

Figure 3 illustrates in detail the mixed effects model predictions for CQ use in four countries with different trends: Senegal (intermediate starting CQ use, fast rate of reduction), Nigeria (intermediate starting use, slow rate of reduction), Ghana (high starting use, fast reduction), and Sierra Leone (high initial use, slow reduction).

The following a priori adjustments were made in the mixed models for prediction of personal endotoxin by stationary site endotoxin: personal temperature and relative humidity (both significantly and inversely associated with personal endotoxin), and study region.

The mixed model allows the prediction of the Sauter mean diameter without any adjustable parameter.

We validated the performance of the N model spatially with a linear mixed model of the observations and model predictions, estimated by residual maximum likelihood.

We used the generalized linear mixed model (GLMM) as the prediction model (Zeger and Karim 1991; Karim and Zeger 1992).

In this paper we provide an extension of a linear mixed model method for genomic prediction to the situation with non-genotyped animals.