The effects of line, and low versus high groups of lines (LvsH) were determined using linear models (line) or linear mixed effects models (LvsH; treating line as a random effect) in R (www.r-project.org).org
To be able to relate mCitrine fluorescence intensity with endogenous EGFR expression levels, this data was fitted with a linear model (straight lines with dashed lines for 95% confidence intervals), yielding a slope of m=1.15 ± 0.13 and an abscissa intercept of b=190 ± 25 (all data).
(a) Scatterplot of the data and fitted EP regression model based on π r 1 (solid line), π U (dotted line) and considering Gaussian linear model (dot-dashed line).
Fig. 4 Stiffness (a), UTS (b) and porosity (c) of composites reinforced with untreated and treated fibres versus fibre volume (Vf) contents (k is slope of the linear regression model lines).
Figure 4(a) also shows the fitted Gaussian linear model (dot-dashed line).
Each year of data within each experiment was first analyzed separately by fitting a mixed linear model that included line as a fixed effect, silking date as a fixed linear covariate, and replication (Galicia only), block within replication (Galicia only), set (North Carolina only), and block within set (North Carolina only) as random effects.
The objective of this study was to investigate the accuracy of multi-line genomic prediction using non-linear models by kernel learning and a linear model that modelled trait by line combinations as separate but correlated traits, and to compare the prediction accuracy of these models to that of commonly used linear genomic prediction models presented by Calus et al. [ 22].
If so, this line will represent the linear model, also called a 'regression line' or 'least squares line' because the sum of the squared distances of all the data points from the line is the lowest possible.
However, the particular distribution of the measured errors suggests a strong predictability and repeatability, also confirmed by the high coefficient of determination (about 0.993) obtained while fitting this error distribution using a linear model (the black solid line in Fig. 5).
We assessed differences in stationary time using a general linear model with sex and line as cofactors (SPSS version 20).
