Refers generally to statistical procedures that utilize the logistic function, most commonly various forms of logistic regression
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Difference between logit and probit models
Can anybody please tell me the difference between the logit and the probit model? I'm more interested here in knowing when to use logistic regression, and when to use probit. If there's any literature ...
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7answers
19k views
Which pseudo-$R^2$ measure is the one to report for logistic regression (Cox & Snell or Nagelkerke)?
I have a SPSS Output for a logistic regression. This output reports two measure for the model fit, Cox & Snell and ...
9
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2answers
1k views
Simulation of Logistic Regression Power Analysis - Designed Experiments
This question is in response to an answer given by @Greg Snow in regards to a question I asked concerning power analysis with logistic regression and SAS Proc GLMPOWER.
If I am designing an ...
13
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2answers
4k views
Does an unbalanced sample matter when doing logistic regression?
Okay, so I think I have a decent enough sample, taking into account the 20:1 rule of thumb: a fairly large sample (N=374) for a total of 7 candidate predictor variables.
My problem is the following: ...
6
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1answer
679 views
Interpretation of simple predictions to odds ratios in logistic regression
I'm somewhat new to using logistic regression, and a bit confused by a discrepancy between my interpretations of the following values which I thought would be the same:
exponentiated beta values
...
14
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1answer
437 views
Logistic regression in R resulted in Hauck Donner phenomenon. Now what?
I'm trying to predict a binary outcome using 50 continuous explanatory variables (the range of most of the variables is -ā to ā). My data set has almost 24,000 rows. When I run glm in R, I get:
...
8
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1answer
2k views
What is the difference between generalized estimating equations and GLMM?
I'm running a GEE on 3-level unbalanced data, using a logit link. How does this differ (in terms of the conclusions I can draw and the meaning of the coefficients) from a GLM with mixed effects ...
7
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2answers
2k views
How to deal with perfect separation in logistic regression?
If you have variable which perfecly separates zeroes and ones in target variable, R will yield the following "perfect or quasi perfect separation" warning message:
...
3
votes
3answers
943 views
Model Selection: Logistic Regression
Suppose we have $n$ covariates $x_1, \dots, x_n$ and a binary outcome variable $y$. Some of these covariates are categorical with multiple levels. Others are continuous. How would you choose the ...
4
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1answer
632 views
Graphing a Probability Curve for a Logit Model With Multiple Predictors
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 + ...
7
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4answers
4k views
How to carry out multiple post-hoc chi-square tests on a 2 X 3 table?
My data set is comprised of either total mortality or survival of an organism at three site types, inshore, midchannel and offshore. The numbers in the table below represent the number of sites.
...
7
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3answers
7k views
Logistic Regression in R (Odds Ratio)
I'm trying to undertake a logistic regression analysis in R. I have attended courses covering this material using STATA. I am finding it very difficult to replicate ...
5
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1answer
746 views
Power analysis for ordinal logistic regression
I am looking for a program (in R or SAS or standalone, if free or low cost) that will do power analysis for ordinal logistic regression.
12
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2answers
1k views
When is logistic regression solved in closed form?
Take $x \in \{0,1\}^d$ and $y \in \{0,1\}$ and suppose we model the task of predicting y given x using logistic regression. When can logistic regression coefficients be written in closed form?
One ...
6
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3answers
4k views
Multiple Chi-Squared Tests
I have cross classified data in a 2 x 2 x 6 table. Let's call the dimensions response, A and ...
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4answers
5k views
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2answers
2k views
Interpretation of log transformed predictors in logistic regression
One of the predictors in my logistic model has been log transformed. How do you interpret the estimated coefficient of the log transformed predictor and how do you calculate the impact of that ...
11
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1answer
522 views
Diagnostics for Logistic Regression?
For linear regression, we can check the diagnostic plots (residuals plots, Normal QQ plots, etc) to check if the assumptions of linear regression are violated.
For logistic regression, I am having ...
12
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1answer
2k views
Poisson regression to estimate relative risk for binary outcomes
Brief Summary
Why is it more common for logistic regression (with odds ratios) to be used in cohort studies with binary outcomes, as opposed to poisson regression (with relative risks)?
Background
...
10
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5answers
2k views
Stepwise logistic regression and sampling
I am fitting a stepwise logistic regression on a set of data in SPSS. In the procedure, I am fitting my model to a random subset that is approx. 60% of the total sample, which is about 330 cases.
...
6
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1answer
1k views
Hosmer-Lemeshow vs AIC for logistic regression
If the Hosmer-Lemeshow indicates a lack of fit but the AIC is the lowest among all the models....should you still use the model?
If I delete a variable, the Hosmer-Lemeshow statistic is not ...
8
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1answer
367 views
Properties of logistic regressions
We're working with some logistic regressions and we have realized that the average estimated probability always equals the proportion of ones in the sample; that is, the average of fitted values ...
5
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0answers
143 views
Updating classification probability in logistic regression through time
I am building a predictive model that forecasts a student's probability of success at the end of a term. Iām specifically interested in whether the student succeeds or fails, where success is usually ...
8
votes
3answers
5k views
Logistic regression model does not converge
I've got some data about airline flights (in a data frame called flights) and I would like to see if the flight time has any effect on the probability of a ...
8
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1answer
3k views
Logistic Regression - Multicollinearity Concerns/Pitfalls
In Logistic Regression, is there a need to be as concerned about multicollinearity as you would be in straight up OLS regression?
For example, with a logistic regression, where multicollinearity ...
6
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5answers
2k views
Better Classification of default in logistic regression
Full Disclosure: This is homework. I've included a link to the dataset ( http://www.bertelsen.ca/R/logistic-regression.sav )
My goal is to maximize the prediction of loan defaulters in this data set. ...
4
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2answers
2k views
R package for fixed-effect logistic regression
I'm looking for an R package for estimating the coefficients of logit models with individual fixed-effect (individual intercept) using Chamberlain's 1980 estimator. It is often known as Chamberlain's ...
3
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1answer
596 views
How to fit BradleyāTerryāLuce model in R, without complicated formula?
The BradleyāTerryāLuce(BTL) model states that $p_{ji} = logit^{-1}(\delta_j - \delta_i)$, where $p_{ij}$ is the probability that object $j$ is judged to be "better", heavier, etc, than object $i$, ...
3
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2answers
569 views
How do you predict a response category given an ordinal logistic regression model?
I want to predict a health problem. I have 3 outcome categories that are ordered: 'normal', 'mild', and 'severe'. I wish to predict this from two predictor variables, a test result (a continuous, ...
3
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2answers
2k views
Logistic Regression: Classification Tables a la SPSS in R
In SPSS output there is a pretty little classification table available when you perform a logistic regression, is the same possible with R? If so, how?
3
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2answers
441 views
Consequences of an improper link function in N alternative forced choice procedures (e.g. 2AFC)?
Background: In some cognitive psychology research areas N-alternative forced choice tasks are common. The most common of these is a two alternative forced choice (2AFC). This usually takes the form ...
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1answer
162 views
Enormous coefficients in logistic regression - what does it mean and what to do?
I get enormous coefficients during logistic regression, see coefficients with krajULKV:
...
2
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1answer
452 views
Logistic vs linear regression
Let's say I run a linear regression model with a binary dependent variable. If I ran logistic regression on the same data would the results be comparable or exactly similar? By results I mean both the ...
2
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2answers
1k views
Case weighted logistic regression
I'm looking at a few logistic regression issues. ("regular" and "conditional").
Ideally, I'd like to weight each of the input cases so that the glm will focus more on predicting the higher weighted ...
8
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2answers
519 views
Building a linear model for a ratio vs. percentage?
Suppose I want to build a model to predict some kind of ratio or percentage. For example, let's say I want to predict the number of boys vs. girls who will attend a party, and features of the party I ...
4
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3answers
250 views
$\chi^2$ tests to compare the fit of large samples logistic models
Does anyone know of any $\chi^2$ tests to compare the fit of logistic models which factor out the sample size? I'm dealing with a very large sample and I fear the significant $\chi^2$ test I get when ...
2
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3answers
2k views
Stepwise model selection, Hosmer-Lemeshow statistics and prediction success of model in nested logistic regression in R
is it possible to do stepwise (direction = both) model selection in nested binary logistic regression in R? I would also appreciate if you can teach me how to get:
Hosmer-Lemeshow statitistic,
Odds ...
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2answers
182 views
Logistic regression sub-group size parameters
Someone in my lab has a sample of 500 older kids and he wants to investigate what factors are related to the probability that they will bully. Groups:
...
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1answer
632 views
More than one outcome (dependent) variables in ordinal logistic regression
I want to run ordinal logistic regression (OLR) in SPSS. My data include 6 predictor variable (two continuous and 4 categorical ) but my outcome variables are also 6 (categorical-likert scale).
e.g my ...
0
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0answers
225 views
Binary classification and ROC curve area less than .5
Both my independent and dependent variables are binary. My result for classification table is 72% for predicted, and my ROC curve area is 0.389. Since <0.5 for ROC area is the worst for accuracy ...
0
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0answers
271 views
Using Rasch model to explain relationships between a set of dependent and independent variables
My research study is in development economics. My data consist of more than one independent variables (continuous and categorical) as well as more than one dependent variables (categorical 5-point ...
26
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5answers
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Alternatives to logistic regression in R
I would like as many algorithms that perform the same task as logistic regression. That is algorithms/models that can give a prediction to a binary response (Y) with some explanatory variable (X).
...
9
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1answer
3k views
Obtaining predicted values (Y=1 or 0) from a logistic regression model fit
Let's say that I have an object of class glm (corresponding to a logistic regression model) and I'd like to turn the predicted probabilities given by ...
18
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5answers
7k views
What is the significance of logistic regression coefficients?
I am currently reading a paper concerning voting location and voting preference in the 2000 and 2004 election. In it, there is a chart which displays logistic regression coefficients. From courses ...
10
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2answers
1k views
How does the power of a logistic regression and a t-test compare?
Is the power of a logistic regression and a t-test equivalent? If so, they should be "data density equivalent" by which I mean that the same number of underlying observations yields the same power ...
7
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1answer
902 views
Output of logistic model in R
I'm trying to interpret the following type of logistic model:
mdl <- glm(c(suc,fail) ~ fac1 + fac2, data=df, family=binomial)
Is the output of ...
10
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2answers
3k views
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?
10
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4answers
3k views
Sample size calculation for univariate logistic regression
How does one calculate the sample size needed for a study in which a cohort of subjects will have a single continuous variable measured at the time of a surgery and then two years later they will be ...
4
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2answers
2k views
Why use Platt's scaling?
In order to calibrate a confidence level to a probability in supervised learning (say to map the confidence from an SVM or a decision tree using oversampled data) one method is to use Platt's Scaling ...
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1answer
396 views
Are robust methods really any better?
I have two groups of subjects, A, and B, each with a size of approximately 400, and about 300 predictors. My goal is to build a prediction model for a binary response variable. My customer wants to ...
