Thank you so much!BvU said:
New one on me...BvU said:
Just a technicality : Why are you using #[\-infty, \infty]#, i.e., are you considering #\pm \infty # as actual inputs?adan said:
The Logit function is a mathematical function that maps any real number between 0 and 1. In adaptation, it is commonly used to model binary response variables, such as success or failure, and to predict the probability of an event occurring based on a set of predictor variables.
The Logit function differs from other types of regression models, such as linear regression, in that it models the relationship between the predictor variables and the probability of an event occurring, rather than the relationship between the predictor variables and the outcome variable itself.
The Logit function relies on the assumption that the relationship between the predictor variables and the probability of an event occurring is linear. It also assumes that the observations are independent and that there is no multicollinearity among the predictor variables.
The Logit function can be adapted for non-binary response variables by using a multinomial or ordinal logistic regression model. These models allow for more than two possible outcomes and use the Logit function to model the probability of each outcome occurring.
One limitation of using the Logit function in adaptation is that it assumes a linear relationship between the predictor variables and the probability of an event occurring. If this assumption is not met, the model may not accurately predict the probability of the event. Additionally, the Logit function may not perform well with small sample sizes or with rare events.