Each predictor variable x i (i = 1, 2,..., d) is standardized before presentation to the network, so as to have zero mean and unit standard deviation, because standardization has been shown to increase the efficiency of ANN training.
Alternatively, when such reference dataset does not exist and a research-level dataset is the only available material, NTP also can use the input dataset to calculate the mean and sample standard deviation for the standardization (current default of the GenePattern module).
In many analyses, a 'z-score' for a subject is determined by subtracting the mean and dividing by the standard deviation from a standardization population.
The average of the corresponding normal limits was taken as the mean, and the range of the normal limits divided by 4 as the standard deviation in the standardization calculations.
Shadowing Log-normal distribution with standardization deviation 7 dB Multiple-path fading Rayleigh distribution.
To calculate the B-score, the resulting residuals within each plate are divided by their median absolute deviation as a standardization step.
As different AD techniques often generate different types of threshold values (number of neighbours, standard deviation, and density percentile), this standardization also allows a simpler and more intuitive visual analysis of the readouts.
The number of individuals who obtained T-scores >60 (i.e., one standard deviation above the mean in the standardization sample) was counted on each dimension and sum score.
NTP can take vectors of the mean and sample standard deviation provided separately from input dataset for the standardization (supported in the R code packaged in the GenePattern NTP module).
In fact, on a scale that measured locus of control, they scored more than two standard deviations away from the standardization group.
Standardization was based on the pooled standard deviation producing effect size.
