omnet simulation in New Hampshire

Omnet simulation in New Hampshire:

Omnet simulation in New Hampshire The discriminating power is always measured through a statistical omnet simulation in New Hampshire criterion, and we have chosen here to work with three critera that are computationally adapted to our data, based, respectively, on LDA,

and logistic regression models In the end, the accuracy of the stepwise selection is measured by a confusion matrix assessing the proportion of well-classified individuals in each omnet simulation in New Hampshire group. It happens that the method that gives the best results in terms of confusion matrices is a backward selection based on logistic regression models.

For sake of conciseness, we will thus only give an account on this specific procedure. Indeed, the backward selection based on logistic regression models is implemented in R through a omnet simulation in New Hampshire function called glm. By running this function on our data, we obtain selected variables,

with a confusion matrix given in Table I hereafter. In our context, it seems reasonable to work with variables omnet simulation in New Hampshire for classification purposes, this number being consistent with the size of our sample.

Since the logistic classifier performs better than the ones based on LDA or Wilks’ methods, we have chosen omnet simulation in New Hampshire to keep all those variables for the end of our study.

For sake of completeness, our sele cted variables are : It isworthmentioning at this point omnet simulation in New Hampshire that most of the fibers contribute to the selected variables, which means that a restriction to one fiber only would lead to a dramatic loss of information.

Categories: Blog