Sentence examples for model uses restricted from inspiring English sources

However, rather than assuming linearity with ln(t) the flexible parametric model uses restricted cubic splines for ln(t) [ 19].

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.

First, the amount of residual/heterogeneity was estimated by random effects model using restricted maximum-likelihood method [ 24, 25].

Linearity assumptions of the ordinal and continuous variables were relaxed by fitting the model using restricted cubic splines.

The logistic EuroSCORE was included in the model using restricted cubic spline functions applied to the a priori natural logarithm transformation with 4 knots.

In the final model, we investigated the linearity assumption of continuous variables by likelihood ratio tests of a model using restricted cubic splines for these variables against a model with the variable entering as linear.

The condom use outcome was normally distributed, and we estimated the three-level hierarchical linear model using restricted maximum likelihood (REML) that assumes multivariate normality for the model residuals.

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).