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Later, Kaleva [2] avoided the well-known computational troubles associated with crispLagrange interpolation by using linear spline and not-a-knot cubic splineapproximations.
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Linearity was evaluated using linear splines with three boundaries and there was no significant deviation from linearity.
All continuous variables were tested for linearity, and non-linear relationships were accounted for using linear splines.
To ensure a flexible specification and minimize residual confounding, age was modeled using linear splines.
Therefore, temperature was modeled using linear splines with a knot at 11.5°C.
We tested for nonlinearity in these associations using fractional polynomials, and any nonlinear relationships were approximated using linear splines.
We used linear splines to test for nonlinear period and cohort patterns.
We also evaluated whether the relative change in quarterly bloodstream infection rate was different during the initial evaluation period compared with the sustainability period by using a linear spline model, which allowed the relative change in quarterly infection rate to change at the end of the initial 18 month period.
Annual trends in age adjusted mortality rates were assessed using linear regression spline analysis.
However, when using linear or cubic spline interpolation for processing, a broad shoulder can be seen in the profile that has also been reported by others [ 5, 19, 21].
The interference fringes were processed using linear interpolation with FFT, cubic spline interpolation with FFT, NDFT and NFFT.
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