Therefore, taking random calls for the analysis of a nestling's hunger-related changes in call characteristics may not capture the biologically important information.
However, most of the detected multicopy accessions were monophyletic in the phylogenetic tree and thus, a copy could be chosen at random for the further analysis.
A random regression model for the analysis of "repeated" records in animal breeding is described which combines a random regression approach for additive genetic and other random effects with the assumption of a parametric correlation structure for within animal covariances.
We assume a random utility model for the analysis.
We have used a random effects model for the analysis.
A simulation study by Austin [ 34] suggested that the statistical power of GEE is the highest among t-test, Wilcoxon rank sum test, permutation test, adjusted chi-square test and logistic random-effects model for the analysis of CRTs.
Nonparametric bootstrapping was performed to assess branch support (100 replicates with single random additions for the ML analysis and 1,000 replicates with 10 random additions for the MP analysis).
Bayes B, which fits all SNPs simultaneously as random effects, was used for the analysis.
A Markov chain Monte Carlo (MCMC) method using a multi-trait generalised linear mixed effect model (glmm) in a Bayesian estimation framework, with animal breeding value fitted as a random effect, was used for the analysis R Package, MCMCglmm, [ 58], http://www.cran.r-project.org.org
A Markov chain Monte Carlo (MCMC) method using a multi-trait generalised linear mixed effect model (glmm) in a Bayesian estimation framework, with animal breeding value and ID fitted as a random effects, was used for the analysis (R Package, MCMCglmm, [ 84], http://www.cran.r-project.org).org
