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# Logit

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### Logit

Plot of logit(p) in the domain of 0 to 1, where the base of logarithm is e

The logit ( ) function is the inverse of the sigmoidal "logistic" function or logistic transform used in mathematics, especially in statistics. When the function's parameter represents a probability p, the logit function gives the log-odds, or the logarithm of the odds p/(1 − p).[1]

## Contents

• Definition 1
• History 2
• Uses and properties 3
• Comparison with probit 4
• See also 5
• References 6
• Further reading 7

## Definition

The logit of a number p between 0 and 1 is given by the formula:

\operatorname{logit}(p)=\log\left( \frac{p}{1-p} \right) =\log(p)-\log(1-p)=-\log\left( \frac{1}{p} - 1\right). \!\,

The base of the logarithm function used is of little importance in the present article, as long as it is greater than 1, but the natural logarithm with base e is the one most often used. The choice of base corresponds to the choice of logarithmic unit for the value: base 2 corresponds to a bit, base e to a nat, and base 10 to a ban (dit, hartley); these units are particularly used in information-theoretic interpretations. For each choice of base, the logit function takes values between negative and positive infinity.

The "logistic" function of any number \alpha is given by the inverse-logit:

\operatorname{logit}^{-1}(\alpha) = \frac{1}{1 + \operatorname{exp}(-\alpha)} = \frac{\operatorname{exp}(\alpha)}{ \operatorname{exp}(\alpha) + 1}

If p is a probability, then p/(1 − p) is the corresponding odds; the logit of the probability is the logarithm of the odds. Similarly, the difference between the logits of two probabilities is the logarithm of the odds ratio (R), thus providing a shorthand for writing the correct combination of odds ratios only by adding and subtracting:

\operatorname{log}(R)=\log\left( \frac, which makes the slopes the same at the y-origin.

Closely related to the logit function (and logit model) are the probit function and probit model. The logit and probit are both sigmoid functions with a domain between 0 and 1, which makes them both quantile functions—i.e., inverses of the cumulative distribution function (CDF) of a probability distribution. In fact, the logit is the quantile function of the logistic distribution, while the probit is the quantile function of the normal distribution. The probit function is denoted \Phi^{-1}(x), where \Phi(x) is the CDF of the normal distribution, as just mentioned:

\Phi(x) = \int_{-\infty}^{x} \frac{1}{\sqrt{2\pi}} e^{-\frac{z^2}{2}} \operatorname{d}\!z

As shown in the graph, the logit and probit functions are extremely similar, particularly when the probit function is scaled so that its slope at y=0 matches the slope of the logit. As a result, probit models are sometimes used in place of logit models because for certain applications (e.g., in Bayesian statistics) the implementation is easier.

## References

1. ^ "LOG ODDS RATIO". nist.gov.
2. ^
3. ^ a b J. S. Cramer (2003). "The origins and development of the logit model" (PDF). Cambridge UP.
4. ^ Hilbe, Joseph M. (2009), Logistic Regression Models, CRC Press, p. 3, .
5. ^ Cramer, J. S. (2003), Logit Models from Economics and Other Fields, Cambridge University Press, p. 13, .
6. ^ http://www.stat.ucl.ac.be/ISdidactique/Rhelp/library/msm/html/expit.html

## Further reading

• Ashton, Winifred D. (1972). The Logit Transformation: with special reference to its uses in Bioassay. Griffin's Statistical Monographs & Courses 32. Charles Griffin.
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