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An additive residual error on log-transformed data (essentially equivalent to an exponential error on normal scale data) was used.
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Statistical tests for OR therefore test whether they are different from 1. Odds ratios and their 95%% confidence intervals (CIs) are given in the text on their normal scale but graphed on a log scale to improve interpretation (sensu Galbraith 1988).
This linear model on the loge loge scale can be interpreted on a normal scale as concentration=e β 0+ β 2 × dose β 1+ β 3, with β0 (intercept) and β1 (dose, Figure 1).
In typical for FFPE data sets situations where Np >> NT, ROC curves on a normal scale are of little use and are much more informative on logarithmic scale; hence we present our result on log10 η scale.
Figure 1 displays the 95% confidence interval for the mean density values for each site and flowering period (thin blue lines around the mean) and the 95% confidence values for single leaf density values with a factor of 48 around the mean on a normal scale (green and cyan lines).
This threshold was equivalent to an expression difference of about 1.5 fold on the normal scale.
Data were correlated on a normal scale in Figures 1H and 1I.
Adjusted values were then back-transformed taking the exponential for all parameters, in order to present posterior densities on a normal scale [ 51, 52].
The absolute difference of the log2 transformed FPKM values [log2 (FPKM + 1); approximately equivalent to fold change on the normal scale] was calculated and the top 1% of the genes of the wild-type plants grown in long-day conditions was used to define a cut-off for all other conditions to determine the number (and identity) of genes with a difference (i.e. variation) larger than this cut-off.
On a standard Normal scale, these correspond to 0 and approximately ±1, i.e. mean ± 1SD.
Estimates of the residual standard deviation range from 0.3 to 0.5 on a standard normal scale.
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