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

learn more… | top users | synonyms (1)

1
vote
0answers
10 views

How to Rank the Effects (Coefficients) of a Categorical Variable

I have a categorical variable, 'Ethnicity' with 12 levels, and an output variable 'has_condition' stating whether a particular subject had a particular genetic condition (1 means favorable, 0 means ...
1
vote
2answers
42 views

Analyzing residuals in logistic regression

Greetings statistics experts, I am having a try with the kaggle titanic dataset and am wondering what to do with the residuals after fitting models. In the case of linear regression you can look at a ...
0
votes
2answers
32 views

Logistic regression : non exclusive predictors

I am doing a logistic regression . My outcome is a categorical (yes/ no) pain after surgery. The predictors i wish to model for includes the type of anaesthesia , among other predictors. The problem ...
0
votes
0answers
15 views

How do I report the weights of the most influential features for logistic regression?

I am currently using logistic regression to compute the probability of some event. I randomly split my training/test data and perform cross-validation on the training data, getting a "best model" for ...
0
votes
0answers
22 views

Deforestation Scenarios using Logistic Regression (Stata)

I used Logistic Regression to model the contribution of a range of explanatory variables on deforestation processes (being my dependant variable - Deforested=1, No Deforestation=0) in the Brazilian ...
0
votes
0answers
5 views

Test for a Difference in Two Odds Ratios [migrated]

I run a logistic model for young people and another logistic model with the same prognostic factors for old people. I would like to compare the two ORs for each prognostic factor between the two ...
0
votes
0answers
20 views

Obtaining adjusted proportions with logistic regression

Can I obtain adjusted proportions of a binary variable by using logistic regression? I have a binary variable (normal/abnormal), which I'd like to obtain adjusted prevalence for (i.e the proportion ...
0
votes
0answers
11 views

How to predict the predict upper bound of a learning algorithm?

I am selecting features for a Logistic regression classifier. I have tested a lot of feature selection algorithms. however, it seems that there exist a fixed upper bound AUC value for a fix feature ...
0
votes
1answer
41 views

pruning Neural Network

Since a feedforward NN with a logistic function as activation function is not linear, does it make sense to reduce variables first with principal components or discriminant analysis? Because ...
1
vote
2answers
47 views

How can I model a binary outcomes in time series using logistic regression?

My data has a binary outcome (attack or not attack), day (20 day in repeated measured design) and some covariates (nestling’s movement). The objectives of my experiment are testing the effect of time ...
0
votes
0answers
12 views

test of correlation among binary variables prior to running logistic regression analysis

I am running a logistic regression analysis with binary variables on SPSS: dependent variable: preterm birth (Y/N) independent variables: hypertension (Y/N), diabetes (Y/N), C section (Y/N), ...
1
vote
0answers
10 views

prediction with string similarity

Assume we have an input of an email and we want to predict if it is spam or not spam. Without being a statistician, i would think one of the predictors takes the subject of the input email and ...
0
votes
1answer
65 views

Logistic regression with +1/-1 labels

I am trying to implement logistic regression where the label space is {-1,+1} instead of the usual {0,1}. I know that I can model this as a 0-1 problem but nevertheless I wanted to see if I can derive ...
0
votes
0answers
8 views

Does it make sense to report equally-fit, more complex, model, if it fits better a theory?

I have two (logistic) regression models for which the deviance is not significantly distinct (p = 0.7). One of them has education, gender and age explaining variable Y. In the other, I have added a ...
1
vote
0answers
20 views

Logistic regression and IV that depends on another IV value

I am modeling the effect of aspects of house change and marital status change on a (binomial) DV. Each observation in my data is a 3-year period in someone's life. Thus, for family change, I have a ...
0
votes
1answer
23 views

Logistic regression: should I remove observations with IV combinations that cannot lead to DV=1?

I am studying the effect of changes in household state on transport mode changes in people's life trajectories. My model is such that each observation I have is a 2-year period in a person's life. My ...
0
votes
0answers
23 views

Can anyone explain this error message in R? [duplicate]

I am running a mixed effect logistic regression, with two categorical predictors each of which are dichotomous. This is my formula, with variable names changed: ...
0
votes
0answers
19 views

Question about interactions in logistic regression, and reporting them [duplicate]

I am running a logistic regression with two dichotomous categorical predictors, and of course a dichotomous outcome variable. My questions are: How do I interpret a significant interaction? Do I ...
2
votes
0answers
21 views

Comparing two correlated dependent variables

I have two dependent variables that I want to predict. One is a normally-distributed continuous variable, and the other is a binary categorical variable (0 or 1). They're moderately correlated so that ...
0
votes
0answers
9 views

Number of parameters in multinomial logistic regression

In Chapter 10 (Directed graphical models) of Murphy's Machine Learning text, the author claims that multinomial logistic regression has $O(K^2 V^2)$ parameters, where $K$ is the number of discrete ...
0
votes
0answers
14 views

Function predict in glm applied to new data [migrated]

This question could have been discussed before, but I failed to find it. The question is: let's suggest, I'm fitting a logistic regression and I would like to train it on training set and then apply ...
0
votes
1answer
29 views

Python - SkLearn Logistic Regression: One-by-one train instance

Here is my question, I have a huge train set so I can't load it in memory and apply this code. ...
1
vote
0answers
14 views

Interpretation of logged term in logit (generalizing previous answers)

I have a logistic regression model, where one of my variables is logged. It is of the following form: $\ln(\frac{p}{1-p}) = B_1\ln(X) + B_2Y + ... + \epsilon$ , where $\epsilon$ is an error term. I ...
2
votes
2answers
97 views
+50

Determine Maximum Likelihood Estimate (MLE) of loglogistic distribution

I am given two data sets containing dates and losses (in some currency). I have to determine the maximum likelihood estimates of the parameters of loglogistic distribution. I googled and found a ...
1
vote
0answers
10 views

Gllamm, gllapred and correct way of plotting results? [migrated]

I am trying to run a random intercept, random coefficient (usually referred to as random slope) multilevel logit model for cross-sectional data with cross-level interactions in Stata with the command ...
1
vote
1answer
45 views

Interpretation of variance in multilevel logistic regression

Please help me to interpret the findings of my model. The specifications of the model are: Dependent variable: treatment (1) or no-treatment (0). Independent variables: age, number of drugs used, ...
0
votes
1answer
54 views

Why doesn't Mahout logistic regression give a good AUC when the model is tested on training data?

0 down vote favorite I'm using the logistic regression of Mahout (version 0.9) but when I check the created model on the same data set it was trained for, I do not see a high value for AUC. I would ...
0
votes
2answers
28 views

Multiple test correction for categorical variables with many levels

I am creating a binary logistic regression model with the inputs as a single categorical variable with 100 levels. My goal is to find which level of the categorical variable is most likely to result ...
1
vote
0answers
15 views

Does collinearity of one-hot encoded features matter for SVM and LogReg?

Sometimes I encode categorical features as binary values - one feature per possible category value indicating whether that feature name matches the original category value (i.e. one-of-K scheme). Now ...
9
votes
1answer
484 views

How fair is it to use the word “predict” for (logistic) regression?

My understanding is that even regression does not give causality. It can only give association between y variable and x variables and possibly a direction. Am I correct? I've often found phrases ...
3
votes
1answer
94 views

Why including some observations twice changes the coefficients of logistic regression?

When I simply duplicate a subset of observations and build the same logistic regression model with the extended data, the coefficients of covariates change. If I duplicate the whole dataset, they ...
0
votes
2answers
27 views

Emphasize a link between two predictor variables (Machine Learning)

I am creating a machine learning application which will utilize logistic regression (though I haven't ruled out bayesian regression). I have multiple predictor variables that I believe to be ...
1
vote
0answers
11 views

How to summarize a series of cumulative variables and extract their contributions to the variation explained by the summarized variable?

I want to use a mixed logistic regression. My explanatory variables are ten variables corresponding to cumulative variables measuring the same value over the 10 years preceding the year of interest, ...
4
votes
1answer
105 views

What does a very high odds ratio in binary logistic regression indicate?

I am developing a predictive model to apply to raster layers for land cover classification. So, I have a thematic category that is classified as agriculture but an accuracy assessment indicates ...
1
vote
1answer
35 views

Is it possible to compare probabilities of 2 logistic different models?

Is it possible to compare probabilities of 2 logistic different models? For example if I have one model that returns the probability that someone answer a phone call on Mondays, and then I have ...
1
vote
0answers
14 views

Weighting particular cases in logistic regression

I am new to logistic regression and I am trying to determine whether it makes sense to weight a particular case in my data which is oversampled in order to model my data better. I'm not even sure if ...
6
votes
1answer
66 views

Help me understand adjusted odds ratio in logistic regression

I've been having a hard time trying to understand the use of logistic regression in a paper. The paper available here uses logistic regression to predict probability of complications during cataract ...
1
vote
0answers
29 views

Test of Independence Between Two Nominal Variables With Many Levels Each

I am looking to test whether there is a significant relationship between two nominal variables, one which has 100 levels and the other with 10 levels. I initially considered doing a $\chi^2$ test of ...
2
votes
0answers
11 views

Half-Normal Plot of Coefficients from Binary Factorial Experiments

After I wrote this all up I debated whether or not I should post it because I think I know the answer to this question (after looking at the two models I'd end up with), but since I don't really know ...
0
votes
0answers
15 views

logistic regression test error rate in intercept-only model

I'm using logistic regression with LOOCV and am balancing the classes for the two responses. I noticed that with my model, the test error rate is decent (0.22) and the predictor variable is ...
1
vote
1answer
20 views

How useful is estimate of accuracy for cross-validation in case of imbalance in class distribution

I have about 4000 instances of one class and 38000 instances of another. I used the DAAG library and I got the following result: ...
1
vote
1answer
18 views

How to select a threshold for logistic regression in case of imbalance in class distribution

Consider the model fit2 <- glm(y~x+z,data=records,family=binomial) I have about 42000 records, of which close to 38000 belong to class y=0 and the remaining 4000 belong to class y=1. In order for ...
2
votes
1answer
40 views

Using proportional data with a binomial error structure in R… a worked example needing answers!

I am trying to test if the proportion of herbivores in spider's diets is related to the proportion of herbivores in their grassland, but am struggling to understand if I should be using a binomial ...
0
votes
1answer
80 views

Logistic regression: class probabilities

I am using logistic regression to solve the classification problem. g = glm(target ~ ., data=trainData, family = binomial("logit")) There are two classes ...
2
votes
0answers
12 views

Difference between spatially autocorrelated logit methods

I am seeking advice on different methods to account for spatial autocorrelation in logit models. I've seen a lot of different models attempt to address all of the issues with spatial logit models ...
7
votes
2answers
261 views

What are some reasons iteratively reweighted least squares would not converge when used for logistic regression?

I've been using the glm.fit function in R to fit parameters to a logistic regression model. By default, glm.fit uses iteratively reweighted least squares to fit the parameters. What are some reasons ...
0
votes
0answers
16 views

glm function in R uses conditional or unconditional likelihood? [migrated]

I read from Logistic Regression:A Self-Learning Text by Kleinbaum that the parameter estimates of logistic regression is unbiased when conditional likelihood is used and biased when unconditional ...
1
vote
0answers
23 views

Bias the classification in logistic regression

I want to make my classifier prioritise finding true cases (1) even if that means that a lot of the false cases (0) are also classified as true. Specifically I wish to find the weights to my features ...
1
vote
1answer
49 views

intercept bias in logistic case-control regression: which is the reason?

I don't understand the reason why if I use case-control sampling in a logistic regression then the intercept is biased. The book Agresti 2007 (An introduction to categorical data analysis) says: ...