Sentence examples for mean animal model from inspiring English sources

This is a classical linear mean animal model for body weight at a given age.

The mean sub-model of the DHGLM is a classical linear mean animal model for body weight at a given age, as given above.

Hence, in the real data analysis, the mean sub-models of the two DHGLM models are expected to give similar results as a linear mean animal model.

With the linear mean animal model (ignoring heterogeneity of within-family variance), genetic variance is estimated based on covariance between relatives.

Since the linear mean animal model is expected to give unbiased estimates of genetic variance (on the mean), the discrepancy between estimates from the linear mean animal model and the DHGLM1 indicates that the latter model is substantially biased, while the DHGLM2 appears appropriate.

Since there were no repeated observations, the fitting algorithm biased the estimates of the variance components of the DHGLM animal mean model (DHGLM1), but not of a classical linear mean animal model and a pure sire-dam DHGLM (DHGLM2) model.

For 18 of 21 trait combinations higher estimates of rA were calculated with the parent-offspring method (mean traits animal model: mean rA = 0.22±0.24 SD; parent-offspring model: mean rA = 0.37±0.22 SD; ZW = 3.39, N = 21, P<0.001).

The standard errors of rA generated from the two methods was highly correlated (r = 0.904, N = 21; P<0.001) and the mean traits animal model generated overall (19 of 21 cases) smaller standard errors (mean traits animal model: SE rA) = 0.16±0.05 SD; parent-offspring model: SE rA) = 0.19±0.07 SD; ZW = 3.60, N = 21, P<0.001).

This will henceforth be referred to as the mean traits animal model.

These data are very similar to the results obtained when using the mean traits animal model approach.

The differences in h2 (parent-offspring h2 minus mean traits animal model h2) ranged between −0.13 and 0.14.