A name given to the log-odds function, which maps probabilities to the real line.
The logit function is defined as
$${\rm logit}(p) = \log \left( \frac{p}{1-p} \right) $$
Questions tagged [logit]
521 questions
36
votes
2 answers
Calculating confidence intervals for a logistic regression
I'm using a binomial logistic regression to identify if exposure to has_x or has_y impacts the likelihood that a user will click on something. My model is the following:
fit = glm(formula = has_clicked ~ has_x + has_y,
data=df,
…
celenius
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28
votes
1 answer
What is the difference between logistic and logit regression?
What is the difference between logistic and logit regression? I understand that they are similar (or even the same thing) but could someone explain the difference(s) between these two? Is one about odds?
user3788557
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24
votes
3 answers
How to set up and estimate a multinomial logit model in R?
I ran a multinomial logit model in JMP and got back results which included the AIC as well chi-squared p-values for each parameter estimate. The model has one categorical outcome and 7 categorical explanatory vars.
I then fit what I thought would…
paul
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22
votes
3 answers
Log-linear regression vs. logistic regression
Can anyone provide a clear list of differences between log-linear regression and logistic regression? I understand the former is a simple linear regression model but I am not clear on when each should be used.
user38133
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22
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2 answers
How to interpret coefficients from a logistic regression?
I have the following probability function:
$$\text{Prob} = \frac{1}{1 + e^{-z}}$$
where
$$z = B_0 + B_1X_1 + \dots + B_nX_n.$$
My model looks like
$$\Pr(Y=1) = \frac{1}{1 + \exp\left(-[-3.92 + 0.014\times(\text{gender})]\right)}$$
I understand what…
ATMathew
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22
votes
3 answers
Which optimization algorithm is used in glm function in R?
One can perform a logit regression in R using such code:
> library(MASS)
> data(menarche)
> glm.out = glm(cbind(Menarche, Total-Menarche) ~ Age,
+ family=binomial(logit), data=menarche)
>…
Marcin Kosiński
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19
votes
2 answers
Ordinal logistic regression in Python
I would like to run an ordinal logistic regression in Python - for a response variable with three levels and with a few explanatory factors. The statsmodels package supports binary logit and multinomial logit (MNLogit) models, but not ordered logit.…
Hadi
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18
votes
2 answers
Logit with ordinal independent variables
In a logit model, is there a smarter way to determine the effect of an independent ordinal variable than to use dummy variables for each level?
fgregg
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17
votes
1 answer
Negative coefficient in ordered logistic regression
Suppose we have the ordinal response $y:\{\text{Bad, Neutral, Good}\} \rightarrow \{1,2,3\}$ and a set of variables $X:=[x_1,x_2,x_3]$ that we think will explain $y$. We then do an ordered logistic regression of $X$ (design matrix) on $y$…
user14281
17
votes
2 answers
Why use the logit link in beta regression?
Recently, I have been interested in implementing a beta regression model, for an outcome that is a proportion. Note that this outcome would not fit into a binomial context, because there is no meaningful concept of a discrete "success" in this…
Ryan Simmons
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14
votes
2 answers
Binary Models (Probit and Logit) with a Logarithmic Offset
Does anyone have a derivation of how an offset works in binary models like probit and logit?
In my problem, the follow-up window can vary in length. Suppose patients get a prophylactic shot as treatment. The shot happens at different times, so if…
dimitriy
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14
votes
3 answers
How to test for simultaneous equality of choosen coefficients in logit or probit model?
How to test for simultaneous equality of choosen coefficients in logit or probit model ? What is the standard approach and what is the state of art approach ?
Qbik
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12
votes
1 answer
Can I interpret the inclusion of a quadratic term in logistic regression as indicating a turning point?
In a Logistic Regression with linear and quadratic terms only, if I have a linear coefficient $\beta_1$ and quadratic coefficient $\beta_2$, can I say that that there is turning point of the probability at $-\beta_1 / (2\beta_2)$?
FZo
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12
votes
3 answers
logit - interpreting coefficients as probabilities
I seem to be missing some vital piece of information. I am aware that the coefficient of logistic regression are in log(odds), called the logit scale. Therefore to interpret them, exp(coef) is taken and yields OR, the odds ratio.
If $\beta_1 =…
user1607
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12
votes
1 answer
Logistic quantile regression – how to best convey the results
In a previous post I’ve wondered how to deal with EQ-5D scores. Recently I stumbled upon logistic quantile regression suggested by Bottai and McKeown that introduces an elegant way to deal with bounded outcomes.
The formula is…
Max Gordon
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