Refers generally to statistical procedures that utilize the logistic function, most commonly various forms of logistic regression
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26 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 ...
2
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0answers
29 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
43 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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20 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
50 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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0answers
26 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
59 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 ...
2
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0answers
24 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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0answers
23 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:
...
2
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2answers
45 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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0answers
36 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 ...
5
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2answers
205 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 ...
2
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0answers
24 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.
...
5
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0answers
115 views
+50
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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0answers
22 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 ...
3
votes
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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1answer
45 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
...
2
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0answers
14 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 ...
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0answers
17 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 ...
2
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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 ...
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0answers
40 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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0answers
34 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 ...
0
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0answers
24 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
40 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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0answers
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
30 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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0answers
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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0answers
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
54 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 ...
-1
votes
1answer
44 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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0answers
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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0answers
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. ...
0
votes
0answers
37 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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0answers
29 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
67 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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0answers
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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0answers
27 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 ...
1
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0answers
24 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 ...
0
votes
2answers
105 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
votes
1answer
103 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. ...
3
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0answers
52 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 ...
1
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1answer
41 views
What to do With Curvilinear Relationships?
If I have determined a curvilinear relationship between my dichotomous y and continuous x, what should I do before running a logit regression?
Should I log transform my x variable, etc?
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0answers
50 views
Using R in Java Project
I hope this question is not off topic on CV:
I am currently fitting some different models (naive bayes, logistic regression...) in R which I up to now thought of as prototypes for a later Java ...
1
vote
1answer
48 views
How do I conduct a simulation using a logistic model with multiple covariates using R?
I'm investigating the asymptotic normality of the estimators of the logistic model. I wish to do a simulation to show that the standard error decreases as sample size n increases. Assume i have ...
3
votes
1answer
46 views
Recreate logistic regression equation from table of odds data
I'm reading the technical manual for a linking study between two assessments. It's pretty clear that the table is model output from a fitted logistic regression equation. Here's what pass odds look ...
1
vote
1answer
59 views
What method should I use to identify which variables differentiate between objects of two different classes?
To illustrate the problem posed by the question: Consider the problem of differentiating between consumers who belong to two different segments. I could use a naive or a sophisticated approach as ...
1
vote
2answers
89 views
Fit a logistic regression code in R
If I have 10 Variables (Q,W,E,R,T,Y,U,I,P,A) and I want Q to be my response variable and other 9 to be my predictors variable. Do I write it in R like this
...
2
votes
1answer
28 views
Nested Models for Likelihood Test
If I have a logistic regression model with 3 predictors, $x_1$, $x_2$, $x_3$, and then I remove $x_3$ from my model (left with only $x_1$ and $x_2$), are those models nested? And therefore I can use ...
4
votes
1answer
187 views
Meaning of p-Value of logistic regression model variables
So I'm working with logistic regression models in R. Though I'm still new to statistics I feel like I got a bit of an understanding for regression models by now, but there's still something that ...
0
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1answer
67 views
Obtaining base level with margeff & gologit2 in STATA
I would be extremely glad if someone could help me with this.
I'm using the gologit2 generalized logistic regression/ partial proportional odds model for ordinal dependent variables. The dependent ...
