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Similarly, we addressed the simultaneity problem through measuring social capital indicators by taking the average prevalence of the indicators across the individual's postal code area after the person's contribution to the mean was subtracted.
Except for the MACCS fingerprints and the binarized atom pairs, all descriptors were auto-scaled, i.e. the column mean was subtracted and the mean-centred data were afterwards divided by the standard deviation of that column.
Details can be found at http://cran.r-project.org/web/packages/lme4/. Centering was performed with respect to the mean of each variables; i.e., the mean was subtracted in the original variable.
All the independent variables, except for trial (inverse), were centered (the sample mean was subtracted from each individual score) [44].
The datasets were changed to log2 scale and to each individual value the population mean was subtracted.
However, despite the high-pass filtering of 0.3 Hz during recording, we noticed DC artifacts in several trials; therefore the overall trial mean was subtracted from the data of each trial before sub-band filtering.
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The mean is subtracted from the original signal to get h(t) =���x t) - m(t).
In each iteration, the envelope mean is subtracted from the data vector.
Regarding the first problem, LSDV estimation is affected by applying OLS on a transformed series, where individual time mean is subtracted from each observation to sweep out the individual effect but is not suitable for the regional growth model (Islam 1995; Tondl 1999).
Typically, data normalization is performed prior to clusterization: the mean is subtracted from each row and divided by the norm so that the norm of each row is 1.
First, the observed summary statistics S∗, and the summary statistics of the prior samples S = (S 1, …, S n ), are standardized using the means and standard deviations of the statistics from the prior sample (i.e., the prior mean is subtracted from each statistic, and the difference is divided by the prior standard deviation).
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