Refers generally to statistical procedures that utilize the logistic function, most commonly various forms of logistic regression
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365 views
How can I assess GEE/logistic model fit when covariates have some missing data?
I have fit two generalized estimating equation (GEE) models to my data:
1) Model 1: Outcome is longitudinal Yes/No variable (A) (year 1,2,3,4,5) with longitudinal continuous predictor (B) for years ...
5
votes
0answers
117 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
...
5
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0answers
154 views
How to test for mediation when working with binary data?
I want to assess if a single variable is mediating the effects of a set of IVs on a single DV. All variables in the model (IV, DV and the mediator variable) are dichotomous (0, 1) and observed. What ...
5
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0answers
300 views
AUC in ordinal logistic regression
I'm using 2 kind of logistic regression - one is the simple type, for binary classification, and the other is ordinal logistic regression. For calculating the accuracy of the first, I used ...
5
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0answers
141 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. I’m specifically interested in whether the student succeeds or fails, where success is usually ...
4
votes
0answers
122 views
Is standardization needed before fitting logistic regression?
My question is do we need to standardize the data set to make sure all variables have the same scale, between [0,1], before fitting logistic regression. The formula is ...
4
votes
0answers
115 views
Logistic regression model for analysis of many IVs with a relatively small sample size
I'm trying to determine the influence (direction and relative strength) of certain attributes of incoming students to an academic program on their successful completion of the program. My sample size ...
4
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0answers
579 views
How to assess mediation effect in multinomial logistic regression?
I wonder if it possible to include a mediation effect in multinomial logistic regression. I have a categorical (3 categories) outcome variable and four predictors (all continuous). I expect one of the ...
3
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0answers
38 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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0answers
54 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 ...
3
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0answers
38 views
Modeling pass rates for departments and courses within a school
Suppose I have a regression model, for example a logistic regression model, which provides a score between 0 and 1 reflecting whether or not that a student will pass a course given certain variables:
...
3
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0answers
85 views
Using priors to detect an effect? logistic Bayesian regression
I have designed an idea and am looking for similar approaches in other literature/areas or if I have applied the Bayesian concepts wrongly. Here is a statement of my problem:
I am modeling the ...
3
votes
0answers
127 views
Assumptions of generalized linear models
On page 232 of "An R companion to applied regression" Fox and Weisberg note
Only the Gaussian family has constant variance, and in all other GLMs the conditional variance of y at $\bf{x}$ ...
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195 views
Logistic Regression Cost Function issue in Matlab
I'm trying to implement a logistic regression function in matlab. I calculated the theta values, linear regression cost function is converging and then I use those parameters in logistic regression ...
3
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0answers
112 views
Sensible to include ratio as a variable in logistic regression?
I'm creating a generalised linear regression using a binomial link function for
two variables A and B. From looking at the data it appears that A/B may have
discriminatory effect. Is it sensible to ...
3
votes
0answers
93 views
Shifted intercepts in logistic regression
I have a question about the effects of shifting the intercept in a logistic fit on the mean of a particular transformation of the scores.
Here is the notation I will be using for the question. The ...
3
votes
0answers
76 views
Validating a logistic regression for a specific $x$
I have a logistic regression model for 0/1 binary response data that is built from samples $(x_1,Y_1),\ldots,(x_m,Y_m)$, where $x_1,\ldots,x_m$ are, fixed, nonrandom, real values and $Y_1,\ldots,Y_m$ ...
3
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0answers
193 views
Logistic regression: fixed effects for firms, countries & years
I am trying to use logistic regression on a sample of 20,000+ firms across 50+ countries, from 2000-2010. Do I need to use logistic regression with fixed effects for year and firm + dummy variables ...
3
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0answers
213 views
Bayesian model averaging in R
I have a logistic model that I've built with the nls function in R. I want to use Bayesian model averaging for variable selection, but I can't find a package for ...
3
votes
0answers
68 views
Dependent variable maximum value contingent on independent variable
I am trying to create a model for debt collections. In the past I have used logistic regression to predict pay/no-pay. This has worked well but has a few unfortunate consequences. People are more ...
3
votes
0answers
93 views
Making new variable instead of correcting for temporal autocorrelation in a GLMM. Is it a valid alternative?
I am doing some forest disturbance research, in which the aim is to predict the probabilities of wind damage occurrence in forest stands of different site (altitude, slope steepness) and stand ...
3
votes
0answers
99 views
Using ordinal regression to evaluate predictor “importance”?
We've got a construct-likert-scale with an internal (8 items) and an external dimension (6 item) and there is also a 5-point item y assessing the "subjective" perception (How skilled do you think you ...
3
votes
0answers
351 views
Convergence failure with step-by-step parameter estimates in SAS proc logistic using the OFFSET option
I'm trying to fit a logistic model of the following form:
$$ y \sim \frac{1}{1 + \exp(-\beta_X)} $$
where
$$ \beta_X = a_0 + a_1x_1 + a_2x_2 + a_3x_3 + a_4x_4 + a_5x_5 $$
In my case, there is ...
3
votes
0answers
146 views
Crossed random effects and unbalanced data
I am modeling some data where I think I have two crossed random effects. But the data set is not balanced, and I'm not sure what needs to be done to account for it.
My data is a set of events. An ...
3
votes
0answers
160 views
Softmax regression bias and prior probabilities for unequal classes
I'm using Softmax regression for a multi-class classification problem. I don't have equal prior probabilities for each of the classes.
I know from Logistic Regression (softmax regression with 2 ...
3
votes
0answers
384 views
Computing platform for R: System76 Linux laptop with dual core i5 or quad core i7?
Even though this is not directly a question related to statistical analyses, I thought this group might have some useful insights. My school windows-xp based workstation takes several hours to run ...
2
votes
0answers
49 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 ...
2
votes
0answers
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 ...
2
votes
0answers
26 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.
...
2
votes
0answers
15 views
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 ...
2
votes
0answers
32 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 ...
2
votes
0answers
127 views
Is the Mundlak fixed effects procedure applicable for logistic regression with dummies?
I have a dataset with 8000 clusters and 4 million observations. Unfortunately my statistical software, Stata, runs rather slowly when using its panel data function for logistic regression: ...
2
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0answers
53 views
How to get SGD to reach global optimal point in logistic regression?
I am trying to write a tool which involves implementing logistic regression. With the batch gradient descent method, the convergence is guaranteed as it is a convex problem. However, I find that with ...
2
votes
0answers
40 views
Nested outcome categories in a multinomial logistic
I’m modeling a target-shooting video game. A player can shoot and hit the target, shoot and miss, or switch weapons. For simplicity, the outcomes are HIT, ...
2
votes
0answers
91 views
Variable selection with restricted cubic splines
Is there any function in R for doing variable selection (backward elimination) in a multiple logistic regression using restricted cubic splines like mvrs procedure for STATA?
2
votes
0answers
311 views
Measure of goodness-of-fit in Ordinal Logistic Regression with continuous independent variable
In case of the ordinal logistic regression, both of the goodness-of-fit statistics, Pearson and Deviance goodness-of-fit measures, should be used only for models that have reasonably large expected ...
2
votes
0answers
45 views
Dealing with dependant data when estimating probability of an event happening
I have 10 year worth of data from 1970 to 1980 (40 quarters).
For each quarter I have five measurements M1, M2, M3, M4 and M5.
TWIST: Although the data I have is on individual patient level the ...
2
votes
0answers
83 views
Adjusting Logistic Regression Coefficients
I am wondering whether there is ever justification in adjusting your logistic regression coefficients. For example, I have a logistic regression model that predicts that 4% of farmers will go out of ...
2
votes
0answers
113 views
Logistic Regression failing in some cases
I am working on a website where I collect the results of chess games that people have played. Looking at the ratings of the player and the difference between their rating and that of their opponent, ...
2
votes
0answers
38 views
Binary logit with time series
I intend to run a Binary Logit with Wholesale Price Index of different commodities. I have converted these scale variables into categorical variables with the following criterion:
1=Price has ...
2
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0answers
136 views
Logistic regression discrimination threshold with cross validation
I'm using logistic regression to perform binary classification with training, CV, and test sets. When is the most appropriate time to pick a discrimination threshold to balance positive and negative ...
2
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0answers
63 views
Logistic Regression - Predicting an event with a couple time-related issues
I'm using logistic regression to predict the occurrence of tree carcasses falling after a mortality event. Data include a variety of topographic and tree characteristic variables and also time since ...
2
votes
0answers
74 views
Reporting contrasts between binary probability parameters in Bayesian data analysis - odds ratios or difference in probability?
In a bayesian data analysis, if one is modeling differences in binomial/bernoulli probability parameter differences between populations, is it still standard to report the difference in the binary ...
2
votes
0answers
169 views
Marginal effects with logistic generalized additive model in R
I am currently working with a logistic semi-parametric model in R using the mgcv package. The output from the model gives the standard log-odds coefficients; however, reviewers have requested ...
2
votes
0answers
816 views
SAS PROC LOGISTIC: Hosmer and Lemeshow test is good but Gini is bad?
I am using PROC LOGISTIC along with Class statements to do binary logit model(default=1,non-default=0) on a bank loan dataset ...
2
votes
0answers
61 views
How can I test for significance of a treatment in an unbalanced, repeated-measures experiment using R?
CrossValidated Community,
I must mention that I am a first-time poster (and relatively new to both modelling and R), so please excuse any norms I may violate in my post and politely inform me.
I ...
2
votes
0answers
123 views
What is the effect of merging categories on logistic regression estimates?
I have a logistic regression with a number of predictors variables including a factor with, say, 5 categories. The estimates for that factor compare categories 2-5 in turn to the reference category ...
2
votes
0answers
122 views
Procedure for variable selection + logistic regression when n is small, p is large, and data are unbalanced?
I have data that have been collected using case-control procedures, in which the population of positive cases is collected with a random sample of negative cases. This yields 62 positive cases and 179 ...
2
votes
0answers
147 views
How do I predict performance for individuals who haven't taken any courses yet?
I'm trying to do a logistic regression on some data.
Here's a simplified version of the situation:
I'm trying to predict student success based on their history, etc. One of my predictors is the ...
2
votes
0answers
167 views
Using generalized method of moments (GMM) to calculate logistic regression parameter
I want to calculate coefficients to a regression that is very similar to logistic regression (Actually logistic regression with another coefficient:
$$ \frac{A}{1 + e^{- (b_0 + b_1 x_1 + b_2 x_2 + ...
