Sentence examples for models y i from inspiring English sources

We assume that the full dataset {(x ij, y ij, t ij ), i = 1,..., n, j = 1,..., m i }, where n is the number of subjects and m i is the number of repeated measurements of the i th subject, is observed and can be modeled as the following partially linear models y i j = x i j T β + g ( t i j ) + e i j, (1.1).

Note that when y i gain is used as an input, it must be understood that βdrug is a vector that models y i gain, the change in performance, and not y i post.

When the number of collected samples, N, is large, the central limit theorem can be applied to model y i under both hypotheses with Gaussian distributions[7 10].

Consider the semiparametric regression model Y i = x i β + g (t i ) + ε i, i = 1,..., n, where the linear process errors ε i = ∑ j = - ∞ ∞ a j e i - j with ∑ j = - ∞ ∞ a j < ∞, and {e i } are identically distributed and strong mixing innovations with zero mean.

The model, Y i j k = μ + s i + t j + ε i j k (3)was fit for probe sets in this subset using the nine F1 arrays (six RNA, three DNA).

Letting X i and Y i denote the log and log values for gene i, respectively, and defining the indicator to be P i be equal to one if gene i was deemed to be under recent positive selection and equal to zero otherwise, we tested whether γ = 0 in the linear model Y i = α + βX i + γP i + ε i.

We applied the model Y i j k l   =   μ   +   ϕ j   +   ϕ j i   +   β l   +   β l k   +   δ m   +   ε i j k l, where Y ijkl indicates performance of genotype i within hybrid family j evaluated on the block k of the environment l.

The fixed effects model, Y i j = μ + s i + ε i j (1 was fit for each probe set in the 3′expression and exon modules, where Yij is the signal for probe set i, sample j is as described above, μ is the overall mean, si is the fixed effect of sex, and εij is the random error.

Phenotypic data from the IBM density trial were analyzed using SAS PROC MIXED version 9.2 (SAS Institute) with the following mixed linear model: Y i j k = μ + d i + R (D ) j + G k + G D i k + ε i j k [1]where Yijk is the response variable of the kth genotype (G) in the jth replicate (R) nested in the ith density (D).

To identify metabolite and gene expression profiles that differed significantly for the effects of diet, genetic line, and the genetic-by-diet interaction, we used the linear model: Y i j m = μ + G i + D j + G × D i j + ε i j m for measurements taken from the mth individual sample in the ith genetic line (G) raised on the jth diet (D).

Finally, we let A = 1 K G and consider the observation model y i = α i A f i ∗ + n i, i ∈ { 1, …, M } (33).