Questions tagged [logistic]
Refers generally to statistical procedures that utilize the logistic function, most commonly various forms of logistic regression
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Estimating logistic regression coefficients in a case-control design when the outcome variable is not case/control status
Consider sampling data from a population of size $N$ in the following way: For $k=1, ..., N$
Observe individual $k$'s "disease" status
If they have the disease, include them in the sample with ...
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How to fit a logistic regression for 1 dependent variable and 1 qualitative variable measured twice
I am struggling to fit a simple logistic regression for one dependent value (group) by one independent qualitative variable (dilat) measured twice independently (rater).
I try many solutions and ...
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Is there overfitting in my modelling approach despite cross-vaidation?
My model is predicting a binomial dependent variable with a rich feature space of 20,000 independent variables. I am using the penalized logistic regression from the glmnet package, which works for ...
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What does a huge gap between P values of the same predictor computed in bootstrapped and non-bootstrapped versions of the same regression mean?
My binary logistic regression gives P values significant at 0.05 level for some of the modeled independent variables. However, bootstrapping the same regression model gives P values significant at 0....
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Number of levels in categorical predictors/combining categories in logistic regression
I have an independent variable “State in the US” and it has around 16 different levels (PA, NY, NJ, etc). Now I have combined the states which appear infrequently into "others" category. But I still ...
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Logistic Regression/Naive Bayes with highly correlated data
Background: We work with data from sports event, more accurately with data about the spectators of sports events: how many people are being violent, what kind of event is this, etc. We have quite a ...
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Interpreting Intercept when doing logistic regression with categorical data in R
In R I have a categorical variable that I performed logistic regression on and got the following result:
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Inclusion of significant interaction term in logistic regression table versus stratification for data presentation
This is a general question on logistic regression result reporting for a publication.
We have an example where two well correlated ($r=0.4, p=0.001$) blood parameters (...
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Overlap between robust glm and weighted glm in R
I sometimes have to vectorise the Huber weights from a robust regression and use them in a lm.
Recently I've had to do something similar for a logistic model but I'm slightly worried because I don't ...
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Odds and odds ratios in logistic regression
I am having difficulties understanding one logistic regression explanation. The logistic regression is between temperature and fish which die or do not die.
The slope of a logistic regression is 1....
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Relationship between McNemar's test and conditional logistic regression
I am interested in the modeling of binary response data in paired observations. We aim to make inference about the effectiveness of a pre-post intervention in a group, potentially adjusting for ...
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Counterpart to regression equivariance in logistic regression?
Let $T(y_i,\pmb x_i)$ be a regression estimator (of the scalar $y_i$ unto
the $p$-vector $\pmb x_i$). When $T$ is the usual LS estimator and $\nu\in\mathbb{R}^p$, we have that:
$$T(y_i+\pmb x_i'\pmb\...
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Appropriate regression model when dependent variable is between 0 and 1?
I am performing a regression where my dependent variable is the value of a group's Simpson Diversity Index. This index value is constrained by $1/k$ and $1$ (where $k$ is the number of classes), ...
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Statistical Analysis on mostly boolean values
So I have a large dataset, and I was wondering what the best way to conduct statistical analysis of it is. I'm very green in terms of statistical methods, but I learn quickly. Basically, each item has ...
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Investigating robustness of logistic regression against violation of linearity of logit
I am conducting a logistic regression with a binary outcome (start and not start). My mix of predictors are all either continuous or dichotomous variables.
Using the Box-Tidwell approach, one of my ...
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Stepwise versus L2 regularized logistic regression: dataset-specific performance
I have two data sets from different collections. The second data set is smaller. They were both analyzed with the same methods in order to derive feature sets of 10-30 features each. Each feature set ...
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Calculating ICC for random-effects logistic regression
I'm running a logistic regression model in the form:
lmer(response~1+(1|site), family=binomial, REML = FALSE)
Normally I would calculate the ICC from the ...
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Correlation between discrete and continuous variables
I am using a scale which consists of discrete values 0 (normal), 1 (mild), 2 (moderate), 3 (severe). I have used this scale for 200 patients.
I am going to find the correlation of this scale with ...
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Is it technically "valid" to fit a logistic regression with a dependent variable that is a proportion?
Several posts (here and here) suggest that beta regression is more appropriate when the dependent variable is naturally bounded between 0 and 1. My question is, leaving appropriateness aside, is it ...
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Do the commands logit and glm (Binomial family) in Stata use different fitting/estimation algorithm?
In my mind, the commands logit and glm (Binomial family) in Stata both use maximum ...
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Multiple testing and logistic regression
I want to perform a number of univariate regressions with different symptoms (e.g. fever, cough, sneezing) as the response variable and one categorical variable (which is always the same each time) as ...
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Adding variables to the model one by one, or at the same time
What are the benefits of adding the variables into a model one by one, as compared to adding them all at the same time?
As I see in most research, the first model that is tested is consisted of all ...
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Fractional Polynomial interpreting interaction terms
Background: I have developed a logistic regression model where I am trying to analyze the effect of socio-economic data of a family on their probability of receiving a home loan. I have used ...
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Less significant findings using conditional logistic regression
I used regular logistic regression on my dataset and got a few significant hits. However, since the data is 1:1 case-control matched data I decided to try using conditional logistic regression (...
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(Why) should bootstrap sampling distribution for logistic regression slope be conditional on $S=\sum Y_j$?
I am working my way through Chapter 4 (Tests) in Davison & Hinkley's (D&H's) book "Bootstrap Methods and their Applications", and have a question about one of their examples.
Davison & ...
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Conditional logistic regression vs GLMM in R
I have paired data (GWAS case/control study) and I have heard using conditional logistic regression or generalized linear mixed models (GLMM) is appropriate. Which should I use in this case? Why would ...
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Simulate data for power analysis of logistic regression model - include covariance variance of variables?
I've tried to simulate data for a power analysis of a logistic regression. The results of the power analysis look reasonable: power=90% for a sample of 6000 persons. But I feel that the analysis lacks ...
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Separation in logistic regression in a complex survey?
Firth's penalized maximum likelihood estimates, exact logistic regression and Bayesian logistic regression (e.g. bayesglm) can account for separation in logistic regression. But how to account for ...
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Goodness of fit vs. significance
If you had two models - one with a better fit, and one with predictors of higher significance - which one would you choose?
Keep in mind that both models are considered a good fit. One has a 3% ...
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Nested Logit model in r
I am trying to run a nested logit using mlogit in R to analyze data from choices people made. There are 4 possible alternatives they could choose from, but in any given choice situation a person had ...
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Multinomial Logit with mlogit and Yogurt Data
I am using the mlogit package in R to estimate a choice model, I started with the famous Yogurt dataset.
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When to use ridge estimator / naive Bayes
I used the Logistic function in weka, to predict a binary class. I have used SimpleLogistic before, but Logistic also seem to give me good results. I did want to clarify if I understand some things ...
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Large scale Cox regression with R (Big Data)
I am trying to run a Cox regression on a sample 2,000,000 row dataset as follows using only R. This is a direct translation of a PHREG in SAS. The sample is representative of the structure of the ...
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Which is applicable, ordinal or multinomial regression model?
I have done a job satisfaction survey where the DV is a 7 point Likert type scale and 5 IVs with 6 point Likert-type scale and 6 IVs with 5 point Likert type scale, all ordinal.
Which type of ...
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Singularity issues in multinomial logit model with differing choice sets
I am estimating a discrete choice model in which individuals choose which schools to attend.
I have a large amount of data on individuals and schools. However, each particular school only appears in ...
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What value is conditional logistic regression if two cohorts are already matched on everything of interest?
My situation is this:
I am comparing two cohorts
I have matched the two cohorts on all of the factors that I am interested in
The two cohorts are already balanced on all of the factors that I would ...
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Given the below dependent variable description, should I choose either Ordered or Multinomial logistic regressions?
My dependent variable looks like a range of ranks. It actually might be considered that way. But the ranking is based on subjective non-quantifiable cutoffs. We assessed the behaviors of a group of ...
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Looking for a regression model or other statistics models for competitions
We have botanical data and need to analyze this sort of scenario, in terms of students for the simplicity of explanation:
We have a series of competitions or trials, each one involves two individuals ...
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explaining an extremely large coefficient in a rare events logistic regression
I am running a rare events logistic regression on a binary dependent variable. I have 538 observations and only 10 events (so 528 values of 0 and 10 of 1), which is why I chose to use a rare events ...
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Power analysis for minimum sample size for 6 continuous predictors and 1 dichotomous criterion
My study has 6 continuous predictor variables and a dichotomous criterion variable. The IRB wants me to provide a power analysis, which I take to mean how I decided on number of participants to ...
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Which machine learning algorithm should I use?
I have one dependent binary categorical variable, and one independent continuous variable. There is a lot of randomness deciding the result of the dependent variable.
The relationship between the ...
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Why do Minitab and SPSS give opposite results in Ordinal Logistic Regression?
I run an ordinal logistic regression model using both SPSS and Minitab. The dataset is exactly the same. The results are exactly the same, but in opposite directions. I have not manipulated default ...
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Transforming a continuous covariate into a discrete one in logistic regression [duplicate]
I am largely inexperienced in the area of logistic regression. I was wondering if there were a good way to transform a continuous covariate in a logistic regression into a discrete one by subdividing ...
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Getting the bootstrap-validated AUC in R
In a paper by Faraklas et al, the researchers create a Necrotizing Soft-Tissue Infection Mortality Risk Calculator. They use logistic regression to create a model with mortality from necrotizing soft-...
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Logistic regression, "sum" confidence interval
I have an existing fitted logit regression model.
Model:
$\hat{p}(x)=\frac{1}{1+e^{-\hat{\beta}x}}$
With parameter estimates $\hat{\beta}$, and observation $x$
Given a new set of datapoints $x_1,\...
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Base category and conditional logit coefficients in the multinomial logit model
Why do we need to use a base category (normalised to zero) when working with multinomial logit models?
Why do we need to report conditional logit coefficients?
Wouldn't marginal effects give us a ...
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How to do 4-parametric regression for ELISA data in R
I am a biology student. We do many Enzyme Linked Immunosorbent Assay (ELISA) experiments and Bradford detection. A 4-parametric logistic regression (reference) is often used for regression these data ...
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Multinomial logistic regression and marginal effects
I am trying to calculate the marginal effects of a multinomial logistic regression. To do this I use the mlogit package and the ...
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