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We considered the following generalized linear model, using restricted maximum likelihood (REML) as implemented in the MIXED procedure of SAS v. 9.2: CVE = Line + Gene.
To avoid over-fitting, the multivariable model was limited to 10 covariates based on frequencies within the ordered outcome variable.[23] For all models, continuous covariates were first included in the model using restricted cubic splines to avoid linearity assumptions.
During the last decade, the study of evolutionary quantitative genetics in wild populations has made a transition from the traditional use of close-kin comparisons to the more powerful 'animal model' using restricted maximum likelihood (REML) [4], [5] to estimate quantitative genetic parameters in natural populations [6].
BMI was included in the model using restricted cubic spline functions on 5 knots.
Linearity assumptions of the ordinal and continuous variables were relaxed by fitting the model using restricted cubic splines.
First, the amount of residual/heterogeneity was estimated by random effects model using restricted maximum-likelihood method [ 24, 25].
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However, rather than assuming linearity with ln(t) the flexible parametric model uses restricted cubic splines for ln(t) [ 19].
The second logistic regression model used restricted cubic smoothing splines with four knots and three degrees of freedom to model the relationship between continuous covariates and the log-odds of death (Harrell, 2001).
First, partitioned variance component models using restricted maximum likelihood (REML) can explore the relative contribution of variants across the allele frequency spectrum or relevant annotations to phenotypic variance.
Intent-to-treat marginal and mixed effect models using restricted maximum likelihood estimation will be used to evaluate the primary hypotheses.
There was consistency in the ORs for Aboriginality in the different multivariate models using restricted sub-cohorts and different Aboriginal identifiers.
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