Many statistical techniques involve optimization. The path from a set of data to a statistical estimate often lies through a patch of code whose purpose is to find the minimum (or maximum) of a function. Likelihood-based methods (such as structural equation modeling, or logistic regression) and least squares estimates all depend on optimizers for their estimates and for certain goodness-of-fit tests. Base-R offers the optim function for general-purpose optimization.
RelTol — This tolerance is a measure of the error relative to the size of each solution component. Roughly, it controls the number of correct digits in all solution components, except those smaller than thresholds AbsTol(i).
The default, 1e-3, corresponds to 0.1% accuracy.
AbsTol — AbsTol(i) is a threshold below which the value of the ith solution component is unimportant. The absolute error tolerances determine the accuracy when the solution approaches zero.
If AbsTol is a vector, the length of AbsTol must be the same as the length of the solution vector y. If AbsTol is a scalar, the value applies to all components of y.
NormControl — Set this property on to request that the solvers control the error in each integration step with norm(e)
Many statistical techniques involve optimization. The path from a set of data to a statistical estimate often lies through a patch of code whose purpose is to find the minimum (or maximum) of a function. Likelihood-based methods (such as structural equation modeling, or logistic regression) and least squares estimates all depend on optimizers for their estimates and for certain goodness-of-fit tests. Base-R offers the optim function for general-purpose optimization.
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