Exact(10)
Predictor 1 (circadian nose poking activity) and different sleep measures as predictor 2 were used.
Each regression was performed 24 times every time by shifting Predictor 1 or Predictor 2 forward by one time bin (i.e. lag) with respect to the behavioral parameter.
To highlight the phase relationship between the Predictor 1 (circadian) and Predictor 2 (Process S), we performed a cross-correlation analysis between the R-squared profiles of the two processes.
Then a multiple regression analysis conducted for each combination of Predictor 1 and Predictor 2 provided us with the best predictor for each behavioral parameter with its contribution (defined by the R-squared).
We performed a linear regression of each individual behavioral parameter (i.e. error rate, switch latencies, etc.) on Predictor 1, defined as the Circadian activity (nose-poking activity) or on Predictor 2, defined as sleep parameters (i.e. sleep duration, NREM, REM, etc).
To further assess the contribution of the circadian process (Predictor 1) and sleep processes (Predictor 2) to the modulation of all behavioral measures, we investigated which process was the best predictor of behavioral parameters (e.g. error rate, decision-making and interval timing parameters).
Similar(50)
Also High self prediction is a well known predictor [ 14, 15, 28, 29].
In addition, posterior distributions for parameters capture the potential variation in effect sizes of predictor variables45.
All methods had unacceptably high error rates for penicillin (11.7% for Mykrobe predictor, 15.1% for disc and 16.0% for Phoenix).
The Glasgow coma scale (GCS) was the strongest independent predictor (r=0.76, P≤0.001) for in-hospital death.
A stepwise multiple regression analysis showed that the BBS was a significant and independent predictor (AdjR2 =.373) for the TUG performance.
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