Editing Logistic regression (section)

==Alternatives==
A common alternative to the logistic model (logit model) is the [[probit model]], as the related names suggest. From the perspective of [[generalized linear model]]s, these differ in the choice of [[link function]]: the logistic model uses the [[logit function]] (inverse logistic function), while the probit model uses the [[probit function]] (inverse [[error function]]). Equivalently, in the latent variable interpretations of these two methods, the first assumes a standard [[logistic distribution]] of errors and the second a standard [[normal distribution]] of errors.<ref>{{cite book|title=Lecture Notes on Generalized Linear Models|last=Rodríguez|first=G.|year=2007|pages=Chapter 3, page 45|url=http://data.princeton.edu/wws509/notes/}}</ref> Other [[sigmoid function]]s or error distributions can be used instead.

Logistic regression is an alternative to Fisher's 1936 method, [[linear discriminant analysis]].<ref>{{cite book |author1=Gareth James |author2=Daniela Witten |author3=Trevor Hastie |author4=Robert Tibshirani |title=An Introduction to Statistical Learning |publisher=Springer |year=2013 |url=http://www-bcf.usc.edu/~gareth/ISL/ |page=6}}</ref>  If the assumptions of linear discriminant analysis hold, the conditioning can be reversed to produce logistic regression.  The converse is not true, however, because logistic regression does not require the multivariate normal assumption of discriminant analysis.<ref>{{cite journal|last1=Pohar|first1=Maja|last2=Blas|first2=Mateja|last3=Turk|first3=Sandra|year=2004|title=Comparison of Logistic Regression and Linear Discriminant Analysis: A Simulation Study|url=https://www.researchgate.net/publication/229021894|journal=Metodološki Zvezki|volume= 1|issue= 1}}</ref>

The assumption of linear predictor effects can easily be relaxed using techniques such as [[Spline (mathematics)|spline functions]].<ref name=rms/>