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Regression analyses were performed using the lm package in the R statistical environment (R Development Core Team 2011).
Unless otherwise noted, all P values were generated by fitting linear models using the "lm" package of R (http://www.R-project.org) and testing for significant terms using analysis of variance.
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Analyses were performed in R (The R foundation for statistical computing; http://www.R-project.org) using the lmer package.
Relative gene expression values were analyzed using R software v3.1.1 (R Development Core Team 2012) by fitting a linear mixed-effect model to the data of each gene using the lmer package (v2.0-20 v2.0-20ring the effecomparingge (embryo, adulthealeffect adult female) with a Tukey's all-pair cofparistagesing thembryo padulte (v1.3-9).
To test for the possibility of reallocation of resources to supplementally pollinated racemes within plants, fruit and seed production were compared between the control raceme on experimental plants and the control raceme on control plants using the lm function of the 'stats' package and a Gaussian error distribution.
In a protein-by-protein analysis, we performed linear fits of ω R 4 S with either MSF (Flexibility Model) or MLmS (Stress Model) using the lm function of the base package of R for each protein.
Association analyses in the CEU samples were carried out using the lm function of the R Statistical Package [38].
We fit the model to the data using the "lm" function in the R statistical package (http://www.r-project.org/).org/
Multiple regression analyses were conducted in R version 2.15.2 using the lm function from the R stats package and Bonferroni correction for multiple comparisons.
A linear regression model was built for every gene using the "lm" function in the base R package, using expression levels in blood, and the age and gender of each individual into account as predictors.
The analyses were performed in R [ 11] using the lm and step functions and, for Bayesian model averaging, the bic.glm function in the BMA package [ 13, 14].
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