Circular(2)-linear regression analysis with iteration order manipulation

Abstract

The selection of statistical analysis techniques to be used must be adjusted to the data. Data in the form of time cycle or point position to the angle of possibility is no longer suitable to be analyzed using classical linear statistic method because the direction and the angle influence the position between one data with other data. This paper aims to examine the comparison of Linear Regression Analysis with Circular Regression Analysis. The writing method used is literature review using simulation data. Simulation of data and analysis is done with the help of R program. The results showed that circular data is better in Circular Regression Analysis than Classical Linear Regression Analysis, so the conclusion is Selection of statistical analysis techniques to be used must be adjusted with the data that already held. Data in the form of time cycles or position of the point to the angle of probability is no longer suitable to be analyzed using classical linear statistic method because the direction and the angle influence the position between one data with other data.

Slow fluctuations in ongoing brain activity decrease in amplitude with ageing yet their impact on task-related evoked responses is dissociable from behavior

“…We quantified the association between EEG/pupil phase of slow fluctuations estimated at cue-onset (θ) and the amplitude of the respective evoked responses using linear regression. This relationship can be captured in a linear regression by representing the phase as a pair of variables: the cosine and the sine ( Nurhab et al, 2017 ). We multiplied the cosine and sine functions by the amplitude envelop (r) of the slow signal fluctuations because the amplitude envelop varies throughout the recordings and these fluctuations will have differential effects on the evoked responses depending on their amplitude.…”

“…Before applying the Hilbert transform, the pre-processed EEG signal was bandpass filtered between.1 Hz and 2 Hz and the pre-processed pupil signal was bandpass filtered between.1 and.9 Hz. Phase (θ) and amplitude envelope (r) at the time of cue-onset were extracted and used to calculate two variables that represent the circular phase of the signal in Cartesian coordinates: r • sin θ and r • cos θ ( Nurhab et al, 2017 ). These variables were included as predictors in the linear regressions studying the effect of pre-stimulus phase angle on the amplitude of the evoked responses.…”

Slow fluctuations in ongoing brain activity decrease in amplitude with ageing yet their impact on task-related evoked responses is dissociable from behaviour

“…We quantified the association between EEG/pupil phase of slow fluctuations estimated at cue-onset (θ) and the amplitude of the respective evoked responses using linear regression. This relationship can be captured in a linear regression by representing the phase as a pair of variables: the cosine and the sine (Nurhab et al, 2017). We multiplied the cosine and sine functions by the amplitude envelop (r) of the slow signal fluctuations because the amplitude envelop varies throughout the recordings and these fluctuations will have differential effects on the evoked responses depending on its amplitude.…”

The heartbeat-evoked potential in young and older adults during attention orienting

“…Moreover, as the effect of heart rate on the HEP depends on the phase of the cardiac cycle, this effect might be modulated by the cardiac phase. We investigated this hypothesis using within-subject multiple regression analyses where we included as response variable reaction time and as predictors heart rate at the time of target onset, the sine and cosine of cardiac phase at the time of target onset [capturing the effect of cardiac phase as a circular variable (Nurhab et al, 2017)], and interaction terms between heart rate and sine and cosine of cardiac phase. Given that the effect of heart rate on the HEP did not differ across age groups, we analysed both groups together.…”