Prior to latency regression analysis, latency data were transformed using inverse transformation due to non-normal distribution of residuals.
The meta-analysis estimate and confidence limits are then transformed back using the inverse transformation.
Optimization is done in the unconstrained, transformed space, and the inverse transformation is used to retrieve the underlying parameter values in the original space of the rate function.
One can then transform the incomplete variable in order to obtain a symmetrical distribution, impute transformed values and apply the inverse transformation to the imputed values.
The cut-off θ is estimated by applying the Normal method in the transformed sample following the application of the inverse transformation function.
The vertex information was then automatically transformed back to native space using the inverse transformation matrix where the boundaries were corrected.
An inverse transformation [1/(x+1)] was used to correct skew and transformed values were used in analyses.
Function F represents the Fourier transform from the time domain to the frequency domain; F−1 represents the inverse transformation from the frequency domain to the time domain.
After that, the inverse transformation was performed.
Conduct the inverse transformation of (3).
Recover u x,t) via the inverse transformation (3b). .
