Linked Questions
94 questions linked to/from Omitted variable bias in logistic regression vs. omitted variable bias in ordinary least squares regression
21
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3
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When to remove insignificant variables?
I'm working on logistic regression model. I checked the summary of the model which is built on 5 independent variables out which one is not significant with a P-value of 0.74.I wish to know that do we ...
AKSHIT SINGH
10
votes
3
answers
7k
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Problem understanding the logistic regression link function
I am trying to learn the logistic regression model. I came to know that there is no linear relationship between predictor variables and response variables since response variables are binary (...
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4
votes
2
answers
2k
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Why significant variable doesn't improve model performance?
I have a binary classification problem with 5K records and 60+ features/columns/variables. dataset is slightly imbalanced (or not) with 33:67 class proportion
What I did was
1st) Run a logistic ...
- 3,322
14
votes
2
answers
4k
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Why is logistic regression well calibrated, and how to ruin its calibration?
In the scikit learn documents on probability calibration they compare logistic regression with other methods and remark that random forest is less well calibrated than logistic regression.
Why is ...
- 5,772
10
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3
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How should two cross-validated logistic regression models be compared?
I'm using 100 times 10-fold repeated cross-validation to assess the ROC-AUC performance improvement of adding a biomarker to an existing model:
Model_A : pred1 + pred2
Model_B :pred1 + pred2 + pred3
I'...
- 85
1
vote
2
answers
7k
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R - Model selection in Glmer
Having troubles to perform a model selection for glmer in R. I'm using the package lme4 with the following structure:
...
3
votes
3
answers
1k
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Probability threshold in ROC curve analyses
When conducting a logistic regression analysis in SPSS, a default threshold of 0.5 is used for the classification table. Consequently, individuals with a predicted probability < 0.5 are assigned to ...
- 159
7
votes
1
answer
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If ϵ is uniformly distributed, then a linear probability model is appropriate? Can I find any Literature?
A latent variable model involving a binomial observed variable $Y$ can be constructed such that $Y$ is related to the latent variable $Y^*$ via
$
Y = \begin{cases}
0, & \mbox{if }Y^*...
- 71
2
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3
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3k
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How to test for main effects in 2x2 factorial design with categorical outcome
The problem I have is as follows: I have a clinical trial based on 2x2 factorial design (four treatment combinations, balanced design)comparing treatment A with treatment B, where the primary outcome ...
- 173
2
votes
1
answer
7k
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Intercept significant in glmer and lmer
I'm quite new on this with binomial data tests, confusing when doing the analysis in R using glmer and lmer
I am doing an ...
- 21
2
votes
1
answer
2k
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What is difference between mediation analysis and mendelian randomization analysis?
I would like to know what the difference between mediation analysis and mendelian randomization (MR) analysis is.
As I know, instrumental variable (IV) in mediation analysis is almost similar to IV (=...
- 43
2
votes
1
answer
6k
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Do control variables need to have a correlation to both the independent variable and the dependent variable?
I have been wondering about this: Do control variables need to have a correlation to both the independent variable and the dependent variable?
E.g. I want to check the effect of Education (...
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3
votes
1
answer
5k
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Bias and variance of coefficient estimation of logistic regression
For a linear regression problem $y=X\beta + \epsilon$, I think we know very well that the estimated $\hat{\beta} = \dfrac{X^Ty}{X^TX}$ is unbiased, and has the variance introduced by $\epsilon$.
It ...
user42004
2
votes
1
answer
2k
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Importance of C statistic in logistic regression
I am working on a dataset with 1500 cancer patients. The research question is impact of various patient and surgery related variables on disease specific mortality. So the outcome/dependent variable ...
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3
votes
2
answers
2k
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Intercept in Multiple logistic regression
I've run a multivariable logistic regression on 8 variables and my results are a bit puzzling.
The intercept (that is the log odds when the other covariates = 0) is significant (p<0.001), but the ...
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