Newton methods together with incomplete expressions of gradients are used.
Purely data-driven methods scale up poorly and have limited interpretability, whereas literature-constrained methods cannot deal with incomplete networks.
The aim of this study was to demonstrate two novel methods for dealing with incomplete item scores in outcome variables in longitudinal studies.
And there are also some methods for dealing with incomplete data (Friedman et al. 1997; Basak et al. 2012), such as the expectation maximization (EM) method and the Gibbs sample method.
Second, we found that the prognostic method is superior to the traditional method for trials with incomplete information which abandons trials with missing information.
Furthermore, as the traditional method for trials with incomplete information always abandons trials with incomplete information, we also compared our methods with this traditional method.
However, the differences were small and may be a result of soil contamination at harvest of some of the silages, which made the method with using incomplete faecal collection and internal marker acid insoluble ash less reliable.
Here, we find that converged result can be achieved for N = 50 using the conventional bi-conjugate gradients iteration method with the incomplete LU factorization as a preconditioner.
If the last trial in this study is assumed to be with partial information, the traditional analysis method for dealing with incomplete information that ignores this trial would give 6.49 ± 1.36 while our prognostic method gives 5.02 ± 1.15, and our interval method provides two intervals as Mean ∈ [4.25, 5.63] and SEM ∈ [1.01, 1.24].
In Table 5, the second row starting with "X1 " means that if the SEM value of X1 is missing, then from the prognostic method, we get X P 1 ~ N (7.55, 2.53), from the interval method, we get X I 1 ~ N ([7.33, 7.83], [2.12, 2.80]), and from the traditional method for trials with incomplete information, we get X I n c 1 ~ N (6.60, 3.57).
