Sentence examples for liability value from inspiring English sources

An individual who develops the disease is assumed to have a liability value above a certain threshold.

The environmental value corresponds to a permanent cow effect, which means that the liability value represents a mean liability over lactation.

Scores were therefore harmonised and transformed into values on the underlying normal distribution (average liability value) within data source and parity prior to analysis.

The performance of a prediction model can be measured by r L 2, the squared correlation between each individual's predicted risk Gi and their liability value (r L 2 can be computed from r O 2, the squared correlation between Gi and case/control status, using the transformation above).

Thus, the liability value of the kth individual in the jth line in the ith environment is explained by Finally, model 9 extends model 8 by adding marked epistatic additive × additive relationships and the interaction between the epistatic additive × additive term and the environments.

The liability value of the kth individual in the jth line in the ith environment is explained by The nine models were fitted using the R-package BGLR (de los Campos and Pérez-Rodríguez 2013) in the R-software (R Core Team 2014).

The liability-threshold model assumes an unobserved, normally-distributed liability (Q); individuals with liability values above a threshold are affected.

STINMOD contains a range of additional economic information such as continuous data on individual income, government support payments, income tax liability, values of individuals' financial assets such as cash, superannuation, shares, property investment and owner-occupied home.

For the best case scenario, we picked controls at random; for the worst case scenario, the controls were individuals with Ф−1 0.99) < Li < Ф−1 0.995), i.e. liability values just below the case/control threshold.

We find that the ability of such a model to predict which single-seizure individuals will have subsequent seizures depends heavily on the distribution of liability values of individuals for whom the first seizure remains an isolated event.

For example, to construct a prediction model explaining 10% of liability heritability, we first sampled predicted values, Gi, for 1 000 000 individuals from a Gaussian distribution with mean 0 and variance 0.1, then generated liability values, Li, by adding to each Gi a Gaussian error term with mean 0 and variance 0.9.