Contents
Do you know the sample size for logistic regression?
However, since logistic regression is a nonlinear model, knowledge of the first probability is necessary since the sample size for a .1 change in probability starting at .2 is larger than a .1 change starting at .5. It is also necessary to reiterate that the sample sizes generated by powerlog should be considered to be a lower bound.
How to do a power analysis of logistic regression?
We will stick with the original p1 = .08 and p2 = .23 and alpha of .05. As you can see, as the R-squared with the other predictors goes up, the number of observations needed also goes up. The power analysis for logistic regression looks, on the surface, to be relatively straight forward.
Which is an example of a multiple logistic regression model?
We will follow up this example with a multiple logistic regression model with five predictors. A small and very exclusive liberal arts college wishes to do a quantitative analysis of their admission process. Currently, the college uses an admissions committee made up of administrators, faculty and students to admit 70 freshmen each school year.
Is the EPV of 10 acceptable for logistic regression?
According to Concato et al. and Peduzzi et al., the concept of EPV of 10 is acceptable for both logistic regression and cox regression (6–7). Based on EPV, researchers need to estimate the proportion for the outcome in the least category and divide it by 10 in order to determine the number of independent variables which can be studied.
When does an unbalanced sample matter when doing logistic?
My problem is the following: whatever set of predictor variables I use, the classifications never get better than a specificity of 100% and a sensitivity of 0%. However unsatisfactory, this could actually be the best possible result, given the set of candidate predictor variables (from which I can’t deviate).
Why are minority classes imbalanced in logistic regression?
The problem is not that the classes are imbalanced per se, it is that there may not be sufficient patterns belonging to the minority class to adequately represent its distribution. This means that the problem can arise for any classifier (even if you have a synthetic problem and you know you have the true model), not just logistic regression.