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How to compare observed data to simulated data?
A possible approach is to look at deviations from the mean of the simulations. You could use Euclidean distance, or Euclidean distance on the square-roots (which has variance-stabilizing properties), or Mahalanobis distance, or anything else you come up with.
How to get started with negative binomial regression?
Getting started with Negative Binomial Regression Modeling. When it comes to modeling counts (ie, whole numbers greater than or equal to 0), we often start with Poisson regression. This is a generalized linear model where a response is assumed to have a Poisson distribution conditional on a weighted sum of predictors.
Is the variance of a negative binomial distribution the same as the mean?
Unlike the Poisson distribution, the variance and the mean are not equivalent. This suggests it might serve as a useful approximation for modeling counts with variability different from its mean. The variance of a negative binomial distribution is a function of its mean and has an additional parameter, k, called the dispersion parameter.
How is a negative binomial regression different from a Poisson distribution?
One approach that addresses this issue is Negative Binomial Regression. The negative binomial distribution, like the Poisson distribution, describes the probabilities of the occurrence of whole numbers greater than or equal to 0. Unlike the Poisson distribution, the variance and the mean are not equivalent.
How are observed values different from simulated values?
The observed values are outside of the 95% confidence intervals of the simulated dataset so it seems pretty obvious that there is a significant difference between the observed and the simulated; but in some cases, the difference might not be so obvious.
Can a simulation be used to compare two different results?
In a manner to compare results obtained in two different ways be sure that assumptions are the same. Otherwise you risk mixing oranges and apples. In your case, both analytical and simulation results can be significant and the extent of significance is measured by reliability.
What’s the difference between simulation and real world?
There’s a lot of difference between different simulations with equal parameters, which really shows the challenges of simulation. Like Y. Azizi wrote, a simulation model always abstracts away from the real world.
How to visualize the scaled model in OpenSim?
Visualize the scaled model’s anatomical marker positions relative to the corresponding experimental markers to see how well the model “fits” the data. Use the pictures you took to assess the results, comparing the joint angles in the “Coordinates” window to the angles you observe in the pictures.
When do you know a simulation is good enough?
There is no scientific consensus on when a simulation is “good enough”. Ultimately, you need to be sure you are asking the right question (such as “which muscles can contribute to knee flexion in this posture?”) and that your results are robust to variations in model parameters.
How to filter raw coordinate data in OpenSim?
Filter your raw coordinate data, since noise is amplified by differentiation. Without filtering, the calculated forces and torques will be very noisy. Compare your results to data reported in the literature. Your results should be within one standard deviation of reported values.