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We will fit empirical Bayesian hierarchical bias models using the generalised linear mixed models (GLMM).
Model fitting was undertaken using the generalised linear latent and mixed models software implemented in Stata (version 10, StataCorp, College Station, TX, USA).
The univariate and multivariate linear regressions were conducted using the generalised linear mixed model function provided in the R base package.
ANOVA were performed using the generalised linear model (GLM) with maternal trees as random effects and stages (position) as fixed effects.
Differences in quantitative variables (such as intakes) were tested using the generalised linear model (PROC GLM) adjusted for energy (ANCOVA) or age and sex (multivariate ANCOVA).
The effect of female restlessness on time spent in the mating position was analysed using the Generalised Linear Model (GLM) with Gamma error structure and log link (GLM-g).
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A direct test of the relationship between male eyespan and offspring sex ratio was performed, using the same generalised linear model as above, testing the offspring sex ratio against thorax, absolute eyespan and relative eyespan.
The estimated covariances of the estimators of the regression coefficients of the excess hazard were provided by the iterative weighted least squares algorithm used to fit the generalised linear model of relative survival, and the variance of the estimate of the logarithm of the expected hazard of death was estimated by the inverse of number of deaths in national population.
Classification of nest-building events in the test set using accelerometer data with the generalised linear model indicated sensitivity of 87%, specificity of 85% and accuracy of 86%.
We identified 14 factors, which we used as covariates in the generalised linear regression analysis.
Comparisons may be further adjusted if necessary for between group differences in family background characteristics using regression methods based on the generalised linear model [ 58].
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