Refers generally to statistical procedures that utilize the logistic function, most commonly various forms of logistic regression

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Calculating variable importance at the individual prediction level

I'm trying to build a model which shows the significance of each feature in contributing to the score of each individual prediction. As a dirty hack, I'm running a logistic regression with ...
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Multiclass logistic regression update algorithm

http://web.engr.oregonstate.edu/~xfern/classes/cs534/notes/logistic-regression-note.pdf See section 2 called Multi-class Logistic regression, especially the update rules. (The entire section is only ...
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From logistic regression coefficients to marginal distributions of the dependent variable

I have the coefficients for a logistic regression model with $25$ independent variables of mixed continuous and categorical types. Considering one particular categorical independent variable with $2$ ...
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37 views

confidence intervals for values estimated from the nonlinear regression model

I have a question about nonlinear regression and confidence intervals for values estimated from the model. Here is my problem. I have sets of data where $X$ is the logarithm of the dose of a chemical ...
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Fit and predict logistic regression model for censored data?

Suppose I have a data frame such that: ...
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What does it mean to “fit a regression function” and then use it to update other functions?

Referring to the algorithm on page 11 in this paper on boosting algorithms, I really don't understand step 2, (ii) and (iii). What does this mean: (ii) Fit the regression function $g_j^h (x)$ by ...
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Logistic regression when some variables are measured just for a subset of samples

I have been asked an interesting modelling question by a clinician. The outcome he is trying to predict whether a particular type of cancer will occur. One of the covariates is an indicator variable ...
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Which steps have to be done before fitting logistic curve to time-series?

I want to cluster time-series concerning sales of products. In the database I have 26weeks after launching each products and units sold each week. One of the method of clustering is to cluster ...
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186 views

Why is logistic regression a linear classifier?

Since we are using the logistic function to transform a linear combination of the input into a non-linear output, how can logistic regression be considered a linear classifier? Linear regression is ...
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20 views

S shaped curve for logistic regression

I was trying to fit logistic regression with a binary response variable and a continuous predictor variable. But when I plotted the fitted probabilities vs the predictor, I got an almost straight line ...
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What is the minimum training set size required for a given number of features for document classification?

For document classification problems, is there a rule of thumb for the number of training instances required for the number of terms in the vocabulary? I am using a logistic regression classifier ...
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How do I run Ordinal Logistic Regression analysis in R with both numerical / categorical values?

Base Data: I have ~1,000 people marked with assessments: '1,' [good] '2,' [middle] or '3' [bad] -- these are the values I'm trying to predict for people in the future. In addition to that, I have some ...
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Partitioning variance from logistic regression

Short version How can I partition the variance from the different levels in a nested mixed-effects logistic regression? Preferably using R, but even general principles would be helpful as a start. ...
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Adding predicted probabilites from logistic regression instead of using cut value

I am using a logistic regression model to predict a binary decision (purchase, don't purchase) based on several independent variables (income, age, education, etc.) for a population of individuals ...
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Model selection based on bic.glm function output and K-fold cross validation

I'm looking for a logistic regression model with high prediction power, and the best model returned by the bic.glm function has a posterior probability of 0.348. I ...
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47 views

Understaning coefficients in summary output of logistic regression in R

This question is about understanding the logistic regression output using R. Here is my sample data frame: ...
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Calculating relative importance of independent variables in Linear and Logistic regression

After fitting Logistic/Linear regression model, we get estimates of parameters showing importance of attribute in predicting dependent variable and can be considered as weight of that independent ...
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How do we know which variables (X1,X2,…,X6) are affecting the outcomes (y)?

The data I am using are sales person data. The data contain six independent variables (six vulnerabilities) scored 1-20 and the output is win/loss of the deal. My question is how do we know which ...
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Ranking based on multiple parameters

I am xyz organization. I have recruit entry level engineers from 100 colleges. I have their performance, attrition & promotion data for last 3 yrs. I have 4 parameters performance, promotion, ...
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Criteria for choosing the most appropriate logistic regression model

I've fitted 16 logistic regression models to my data and I'm not sure as to which model to choose as my final model. I looked at a couple of things to help me choose my final model. 1) significance of ...
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What is a good way to deal with convergence? Do the same set of data produce the same result? [closed]

I'm working on a project producing the probability of a user to be authenticate user. We collect 10 sets of data from the user, then we spoof 10 sets of false data for the same user. We use these 20 ...
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Why no mention of penalized regression techniques in Applied Logistic Regression, 3rd edition, by Hosmer, Lemeshow, and Sturdivant?

Just ordered this textbook, and Wow, the complete omission of this subject from an otherwise excellent reference on logistic regression is a bit surprising. The 2nd edition was published in 2000 - ...
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MCA vs Multinomial Logistic Regression

Lets say I have made survey using a sample of a given number of people, containing a set of 25 questions that have 6 possible answers (Fully agree/ Partially Agree/ Neutral/ Partially Disagree/ Fully ...
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42 views

What is the difference between forecasting based on ARIMA and logistic curve? R

I'm making a project connected with identifying the dynamics of sales. My database concerns 26 weeks (so equally in 26 time-series observations) after launching the product. This is what my database ...
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Ensemble of models with different feature spaces

BACKGROUND I have data in which the dependent variable is binary with a highly-skewed distribution: <1% records are 1 (doers), >99% records are 0 (non-doers). I'm using logistic regression to ...
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How to calculate p values in logistic regression with gradient descent algorithm

In logistic regression, the gradient descent algorithm for calculating coefficients can be described this way: Until convergence, do $$ \beta := \beta + \alpha \frac{\partial L}{\partial \beta} ...
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Intercept term in logistic regression

Suppose we have the following logistic regression model: $$\text{logit}(p) = \beta_0+\beta_{1}x_{1} + \beta_{2}x_{2}$$ Is $\beta_0$ the odds of the event when $x_1 = 0$ and $x_2=0$? In other words, ...
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Likert Item, Regression analysis

HI there: I have a one likert item I would like to analyze. I know that most scales require multiple likert items, but this is just an exploratory exercise of an existing survey. It's out of my ...
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Odds Ratio Coefficient [duplicate]

Suppose you are given an odds ratio of an interaction term in logistic regression. Is the coefficient associated with this odds ratio simply $\log( \text{OR})$?
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Binary logistic regression

im new to binary logistic regression. I don't know whether the test is giving me real results based on my data set. My dependent variable will be the presence of STEC in fecal samples (Presence= Yes, ...
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Detecting heteroscedasticity - Can I use Breusch-Pagan Test on binary logistic regression?

I'm currently testing a (binary) logistic regression model, which seems to have at least some issues with multicollinearity. Now I don't really trust the data anymore and would like to also test it on ...
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mvoutlier vs influence measures

I am exploring the mvoutlier package and comparing it against conventional influence measures such as Cook's Distance, Leverage, DFFITS etc. I have not been able to get my hands around comparative ...
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28 views

External validation of a regression in Stata

What I'm essentially trying to do is a temporal external validation of a Cox Proportional Hazards model and also a logistic regression model on the newest year of a dataset that was not included ...
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Should statsmodels's GLM produce the same results as R's lm?

Should Python's statsmodels.api.GLM(train_y, train_X, family=sm.families.Binomial()).fit().predict(test_X) always produce the same results as R's ...
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Deviance Residuals

I'm a complete rookie when it comes to logistic regression and I seem not to be quite aware of the concept of deviance residuals. Could anyone help me interpret this plot? As far as I know these ...
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Logistic mixed model

In the logistic mixed model ${\rm logit}(P(Y_i=1))= α + βX_i + u_i + ε_i , i=1,...,m$, when we know $u_i\sim \mathcal N(0,σu^2)$, and $ε_i\sim\mathcal N(0,σi^2)$, and if we know $σi^2$ in each area ...
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When optimizing a logistic regression model, sometimes more data makes things go *faster*. Any idea why?

I've been toying around with logistic regression with various batch optimization algorithms (conjugate gradient, newton-raphson, and various quasinewton methods). One thing I've noticed is that ...
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Select the most probable learning algorithm from the testing set and output of a classifier?

Let's say I have a "mystery classifier". It is a "black box", I don't know exactly what it is doing, I know neither the training set nor the learning algorithm that was used. What I do have is a ...
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mlogit query with multiple options: response rate not good for third variable

I am stuck with one query on mlogit. I have three options (0,1,2) for prediction of probabilities. The model equation is: ...
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Clustering Variable

I have a set of 10 variables (v1..v10) which are continuous. I have another two control variables ctrl11 and ctrl12 which of course are categorical. ctrl11 can take any values from 1 to 50 and ctrl12 ...
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Use of the F statistic in logistic regression

This paper uses a generalised linear mixed model assuming a binomial distribution for the errors. In the results section, the F statistic and associated P-value is used for the model (page 2150, ...
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coefficients and p-value in logistic regression

In logistic regression, I have a variable with larger coefficient and larger p-value and another variable with smaller coefficient and smaller p-value. If use p-value then the latter one is more ...
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98 views

Polynomial term in logistic regression

I've made a logistic regression model that includes a polynomial term to degree 2. I'm aware that logistic regression models the response variable as a non-linear function of the predictors. Does it ...
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What algorithms are available for logistic regression?

I am trying to implement a logistic regression function in c++, and not sure what algorithm to use. So far I have heard of these: Newton-Raphson IRLS Gradient descent Are there other algorithms ...
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CDF and logistic regression

Is the probability calculated by a logistic regression model (the one that is logit transformed) the fit of cumulative distribution function of successes of original data (ordered by the X variable)? ...
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What is wrong with this implementation of logistic regression (using iterative reweighted least squares)?

I am trying to implement logistic regression using the following algorithm: fit a simple linear model $y \sim Xb_0$ calculate $W = \frac{e^{Xb_0}}{(1+e^{Xb_0})^2}$ calculate $z = Xb_0 + y \cdot ...
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Dealing with multicollinearity in logistic regresson model

I have 5 quantitative predictor variables in my logit model, and when I use the cor function in R on those 5 variables, I see that $x_{1}$ and $x_{2}$ have ...
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28 views

Explanatory variables in a Lin-log model

I have a data set and I want to fit a Lin-log model. Is it possible to apply the log transformation only to some of the explanatory variables or should the log ...
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Interpretation of coefficient sign change and order of terms

I have a logistic regression with data that are kind of like this: ...
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What is quasibinomial?

I'm hoping someone can provide an intuitive overview of what quasibinomial is and what it does. I'm particularly interested in these points: How quasibinomial differs to the binomial distribution ...