Exact(33)
Firstly, the algorithm reads in the predictor data (see Section 3).
A regression model, by nature, estimates the mean of the predictor data.
In this paper we propose a very transparent and versatile method for simulating response and predictor data from a multiple linear regression model which hopefully may serve as a standard tool simulating linear model data.
We found GAM's to perform as well as nonlinear models with respect to fit and validation statistics; however, when applied to the population of predictor data the bootstrap confidence interval was less efficient than the null model.
The magnitude of the constant has no effect on the strength of the association, or on the significance levels, as it simply rescales the predictor data (in this case V2).
The resultant predictor variable loadings (coefficients) were used to transform the predictor variables into a featurespace of "canonical variates" that maximized the inertia in the response data that could be explained by the predictor data.
Similar(27)
where the (k×J -matrix (X_{0}^{(text {valid})}) and the k×J -matrixal vector (X_{1}^{(text {valid})}) contain time averages of the predictors' data for the validation period, which for the application at hand ranges from 1981 to 1990}^
In the training period, one makes use of the (k×J -matrix (X_{0}^{(text {train})}) and the k×J -matrixal vector (X_{1}^{(text {train})}), containing time averages of the predictors' data for the donor units and the treated unit, respectively6.
This effect was more pronounced for ANOLEA and CHARMM based predictors which are more sensitive to small coordinate changes than ProsaII based predictors, data not shown.
As predictors, data on the remaining items on the scale were used.
SAM carried out the accumulation of datasets, preparation of the predictors, data analysis and drafted the manuscript.
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