The time @master of the master clock and the time @slave of the slave
clock are added to the list of observations. If enough observations
are available, a linear regression algorithm is run on the
observations and @clock is recalibrated.
If this functions returns %TRUE, @r_squared will contain the
correlation coefficient of the interpolation. A value of 1.0
means a perfect regression was performed. This value can
be used to control the sampling frequency of the master and slave
clocks.
The time @master of the master clock and the time @slave of the slave clock are added to the list of observations. If enough observations are available, a linear regression algorithm is run on the observations and @clock is recalibrated.
If this functions returns %TRUE, @r_squared will contain the correlation coefficient of the interpolation. A value of 1.0 means a perfect regression was performed. This value can be used to control the sampling frequency of the master and slave clocks.