The main objectives of PTM are the ability to re-light pixels using the regression parameters obtained, as well as the recovery of surface properties: surface normal, colour and albedo.
For calibration, 1100, 1200 and 1300 µl samples of the standard seawater (batch 90) were measured, a regression line drawn and the area for 1200 µl calculated using the regression parameters.
For these reasons, we fit a linear regression model to each read's quality scores and use the regression parameters as features for classification: Intercept: The quality of the first base in the read.
For the steady-state methods (Methods 1 and 2), we used the regression parameters reported by Samtani and Nathan et al. The possibility of reestimating the parameter values using, e.g., week 12 data from each treatment arm would likely have resulted in biased parameter values as HbA1c, contrary to MGP, would not be at steady state at this time point.
Because log-transformed values were used, the regression parameters in the model are interpreted as a proportion of urinary phthalate concentrations in the reference group for a given independent variable for example, the urinary phthalate concentration of the medium-product-use group as a proportion of that in the low-product-use group.
We used the regression parameters to estimate the percent increase or decrease in response (ACTH, CORT) associated with an interquartile range (IQR) increase in log-transformed Pb (with log-BPb or log-TPb modeled as continuous variables) and associated with Pb being above versus below the median value (i.e., with BPb or TPb modeled as dichotomous variables).
They used the regression parameter from this analysis to predict lifetime excess risks for several lifetime occupational and environmental exposure scenarios, and also to predict the fraction of annual lung cancer deaths attributable to diesel exhaust.
Uninformative uniform priors (from -10 to 10) were used for the regression parameters (the entries of β) and an informative τ∼logN -1.83,1.52) prior was used, as suggested by Turner et al.[ 27], where 'logN' denotes a log-normal distribution.
The peak and the area under the curve (AUC) calculated for each patient were correlated with the clinical parameters using the regression for quantiles test.
Using the regression coefficient of these three parameters, we determined a prognostic index which allowed us to distinguish three risk groups whose theoretical median survival is 4, 20 and 54, months respectively in patients with non HIV related PCL.
Seasonal effects ϑ m are represented by a Fourier series, as in (3), and precision parameters modelled using the regression form (4). To represent summer extremes (no ice cover at all), a logit regression for the probability of zero inflation is used, with logit ( α 0 t ) = ξ 0 + ∑ J 2 j = 1 ( ξ 2 j - 1 cos ( ω jm t ) + ξ 2 j sin ( ω jm t ) ).
