Refers generally to statistical procedures that utilize the logistic function, most commonly various forms of logistic regression
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Combinatorics problem applied to GAM Logistic regression. R code
I'm doing biomedical research and I need to set a GAM Logistic Model which get the maximum AUC score as possible. I have 4 disease markers; $Y_1, Y_2, Y_3, Y_4$ with different data in each one, and ...
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1answer
28 views
Logistic regression vs MLE of conditional probability
I am trying to build a classification model from a large dataset (50M instances) with a categorical independent variable (pred) and a binary dependent variable ...
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12 views
Comparison with trial dependent chance level
I ran an experiment where each participant had to choose 1 image from a 4-image display and I measured whether the image they chose was from category A. I want to compare the average proportion of ...
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1answer
36 views
Logistic model, what is more important: Anova Chi-sq test or significance of coefficients
I have a logistic model with 8 variables. I ran a chi square test in R (anova(glm.model,test='Chisq')) and 2 of the variables turn out to be predictive when ordered at the top of the test and not so ...
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1answer
51 views
How would you explain logit coeficients to someone with no statistical background
Similar question here
The question I highlighted above provides an overview of how generalized linear models work. However, I find people often want more and ask about how the coeficients were ...
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1answer
40 views
Which bootstrapped regression model should I choose?
I have a binary logistic regression model with a DV (disease: yes/no) and 5 predictors (demographics [age, gender, tobacco smoking (yes/no)], a medical index (ordinal) and one random treatment ...
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18 views
Necessary amount of countries in the model for 1 macro level predictor?
Within my logistic regression models I use cross-national data in order to say something about individual soft drug tolerance. In total I have 29 countries with 37.000 individuals.
My supervisor ...
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15 views
OK to use residual sum of squares for cross-validation of binary outcome?
For an OLS model the mean squared error can be used to assess the fit of the trained model on the validation data.
What is the equivalent for a logistic regression model? Can I simply use the ...
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1answer
16 views
can EM algorithm be applied to my problem? Input data set is based on a function of parameter
I understand EM algorithm is often used for missing data/mixture problem. But can it be used to optimize a particular type of likelihood based on jointly fitting variables and transformations of those ...
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1answer
40 views
SPSS: Plot a multinomial logistic regression
Very simply, I'd like to plot a graph that compares what is predicted by the model to the real observations.
It seems easy for binomials, but for multinomial the graph that SPSS creates is pretty ...
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53 views
Variable selection / Dataset reduction for large datasets (in R)
I'm working on a behavoural scorecard modelling exercise, and many of the decisions taken to date have been based on the experience of a consulting credit analyst (whose experience software-wise is ...
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1answer
52 views
Online logistic regression?
Here is my problem: I am developing an embedded system for some classification task. I am using Logistic Regression as my classifier. Now I train my classifier, and download my model on to my machine. ...
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21 views
Aggregate yearly t-stats (logistic regression)
This question follows up my first one.
I ran logistic regressions on a period of a few years. The authors that created the model I'm using aggregated their yearly results, to provide the coefficients ...
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1answer
55 views
Logistic regression: controlling variables not significant, what should I conclude/further test? [closed]
I ran annual logisitic regression on time-series datas. The most important independant variable have coefficient that are significant in a lot of years, that's a relief. But the "controlling ...
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31 views
Confusion matrix with low incidence rate
I am trying to use a binomial regression to predict customer churn. A reproducible example is below. In the example, there is about 5% latent attrition and customers with a price above 200 have a 15% ...
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2answers
69 views
Measuring the performance of Logistic Regression
Being quite new to the field, it occurs to me that there are multiple and fundamentally different ways of assessing the quality of a logistic regression:
One can evaluate it by looking at the ...
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25 views
Generate predictors with fixed predictive validity in R
Let's say I have a dataset:
x=rnorm(1000, mean=0, sd=10)
I would like to create five variables (a,b,c,d,e) that I can use to ...
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26 views
Lognormal and Logit models in JAGS
I'm trying to run this two-part model in JAGS. I have two components, one logit and one lognormal regressions in there:
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2answers
48 views
Advice needed on auction system simulation
I am trying to simulate an auction system in which a number of competitors, $N$, independently offer a discount from a reference price previously published by the buyer.
The order is awarded to the ...
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38 views
Question about proof for luce choice axiom w.r.t. conditional probability
In Luce (1959) the choice axiom is definied, that for a finite subset $T$ of $U$ such that, for every $S\subset T, P_S$ is defined.
If $P(x,y)\ne 0,1$ for all $x,y\in T$, then for $R\subset S\subset ...
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310 views
Is logistic regression a non-parametric test?
I recently received the following question via email. I'll post an answer below, but I was interested to hear what others thought.
Would you call logistic regression a non-parametric test? My ...
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28 views
Correlated error term residual in logit regression what are my options?
I have estimated a model, with many interactions of both continuous and factor explanatory variables, which is to be used for prediction.
My model has performed reasonably in out of sample testing.
...
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118 views
Luce Choice Axiom, question about conditional probability
I'm reading Luce (1959). Then I found this statement:
When a person chooses among alternatives, very often their responses
appear to be governed by probabilities that are conditioned on the
...
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23 views
Nested vs. conditional logit regression
I am trying to estimate a logit regression model with travel mode choice (categorical) being the dependent variable; explanatory variables include age (categorical), income (categorical), gender ...
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39 views
Generalization of cumulative probability models for ordinal Y
There are many models in existence for ordinal $Y$, for example the proportional odds ordinal logistic model, the continuation ratio model, and the cumulative probit model. The first and third of ...
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1answer
47 views
How can I interpret binary logistic table?
I´m beginner with SPSS and I have on problem on interpreting binary logistic results. So I have this table:
Variables in the Equation
...
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Selecting priors for logistic functions
I have this confusion related to how to select priors for a logistic regression
By Bayes theorem
$P(\theta|D) = \frac{P(D|\theta) * P(\theta)}{P(D)}$.
Now my likelihood $P(D|\theta)$ is given by ...
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18 views
Why does increasing event weight in logistic regression increase c-statistic?
I have a logistic regression model that uses all events and a sample of non-events as the training and test population. The observation weights are chosen following King & Zeng. Events are ...
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33 views
How to report most important predictors using glmnet?
I want to find the most important predictors for a binomial dependent variable out of a set of more than 43,000 independent variables (These form the columns of my input dataset). The number of ...
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47 views
Meaning of warning messages in logistic regression using multiply imputed data set in SPSS
I had a data set containing 2 dependent variables (one binary and the other ordinal) and a set of 30 co-variates (both continuous and categorical) with a lots of missing values. I heard that multiple ...
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35 views
Logistic regression with random and with fixed intercept
I have made logistic regression with fixed intercept and with random intercept on the same data, and the results (the odds-ratios) are very different. Could somebody tell me what can be the reasons of ...
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1answer
36 views
Parameters for a Hierarchical Multinomial Regression
I am trying to fit a hierarchical multinomial regression to cross sectional data. I have around 2000 units with only one observation per unit. I have a binomial response variable and 14 dummy ...
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2answers
42 views
How to detect a quasi separation problem for a data set?
Suppose that we have a two-column data set. One column consists of a hundred x=0 and a hundred x=1, whereas the other one consists of y's (1 or 0 response). Besides, suppose that the P(Y=1|X=0) = ...
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17 views
Prepare data for generalized linear regression [migrated]
I want to perform glm for the dataset Titanic in R. I did the following steps to prepare the data and run glm
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1answer
33 views
spss: working with two binary/dummy variables
Am trying to set a few binary/dummy variables against each other, i.e. propensity_to_dance and gender.
I assume that it' ok to ...
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22 views
Modelling a skewed, 10-point Satisfaction variable
I am trying to replicate and hopefully improve on an analysis done in a study to find determinants of patient satisfaction after shoulder surgery. Satisfaction is heavily skewed (with over 60% of ...
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21 views
spss 20 for mac: how to get probabilities after running a binary logistic regression [duplicate]
I have a binary variable that I'm investigating in SPSS, inclination_to_dance. I have another linear variable as well, ...
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1answer
56 views
Expected value of latent utility in logistic regression
I'm looking for an analytical expression for the expected value of the latent utility in a logistic regression.
Setup:
There are two choices indexed by $i \in \{0, 1\}$ with associated utilities ...
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1answer
45 views
Methods For Estimating Trend in R [closed]
Do you know how i can get a code for estimating trend by Mitcherlich and logistic method in R?
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21 views
Measure the relation/association between the outcome and the independent variables
I am running a model (logistic regression) with 20 independent variables in R.
Before running the model I calculated the correlation between all the variables and finally selected my variables by ...
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6 views
Is it necessary to used clustered data analysis techniques on matched designs?
We have cancer outcomes from SEER data matched on registry and age where exposed and unexposed individuals were matched in a 3:1 ratio. There are still 1,000s of cases per registry in either group. ...
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39 views
Pre to Post Repeated measures for categorical dependent variable and more than one independent variables both continues and catgorical
I am doing Prenatal (before delivery) and Postnatal (after delivery) depression assessment (2 time points).
Dependent variable is Depressed/Non depressed mother on a measure of depression.
When as ...
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30 views
Restricting model parameters in logistic models in R
Is there any function in R that can solve the problem like this in SAS?
Thanks in advance!
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2answers
68 views
Is this a case for ordered logistic regression?
I have the following study setup:
Three groups of people were asked a question, and the answer was ordinal (likely, somewhat likely, somewhat unlikely, unlikely). In my data set, I have a contingency ...
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10 views
Debugging Multinomial Logistic Regression Implementation [migrated]
Hi for the University we are required to implement from scratch (using numpy / scipy) Multi-logit. So basically I have to implement the log-likelihood and the graditen of that function.
I have ...
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28 views
Zero logit coefficient
Multinomial regression output gave me zero logit coefficients for continuous independent variables of income and saving. What could be the reasons for this?
Secondly, zero cell in the multinomial ...
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25 views
account for spatial autocorrelation with a binomial regression model
I am using a binomial regression model for presence/absence, with 20 independent variables to test. The data has x and y coordinates and I would like to understand how can I take into account the ...
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2answers
127 views
Is there a simple rule for interpretation of Interactions (and their directions) in binary logistic regression? [duplicate]
I have a binary logistic regression with Y (a disease) and 5 independent variables (and some of their 2-sided interactions which did not cause multicollinearity). All of my single IVs significantly ...
3
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1answer
107 views
Logistic regression algorithm in Ruby
I have been using R to calculate logistic regression with many independent variables for a Ruby on Rails web application. However, I can no longer import data from the database to R using RPostgreSQL. ...
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57 views
Analysis of randomized experiments
I have data from an experiment in which participants were randomly assigned to one of two groups and asked a series of opinion questions. One group, the control, was not presented with any additional ...
