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2. R: Calculate and interpret odds ratio in logistic regression

   stackoverflow.com/questions/41384075

   However, in logistic regression an odds ratio is more like a ratio between two odds values (which happen to already be ratios). How would probability be defined using the above formula? Instead, it may be more correct to minus 1 from the odds ratio to find a percent value and then interpret the percentage as the odds of the outcome increase ...

3. r - How to calculate interaction term as odds ratio in logistic...

   stackoverflow.com/questions/24357393

   I've done some reading about interpreting interaction terms in generalized linear models. When using the log odds, the model is linear and the interaction term(s) can be interpreted in the same way as OLS regression. When the coefficients are exponentiated into odds ratios, this is no longer the case.

4. Odd's Ratio Plot Produced by the plot_model() function in the...

   stackoverflow.com/questions/64089673

   I have produced an odds ratio plot (below) from the data frame called FID (below) using the plot_model() function in the sjPlot package, but the figure is too large for the plotting window. I have two models involving: (1) FID_Model_1 (see figure 1); and (2) Mixed_FID_Model_1 (see figure 2). The mixed model contains the lowest AIC value, and ...

5. How do you interpret thresholds in CLMM (intercepts and odds...

   stackoverflow.com/questions/67534381

   Now im in the process of interpreting the results of the CLMM. I would like to use sjplot and analyze results based on odds ratio, but I'm having issues understanding the thresholds. Eg: 1|2 comes out with an estimate of -1.0375 and an odds ratio of 0.35. The sjplot also signifies that the odds ratio has a significant p-value (figure of sjplot ...

6. Interpreting odds ratios in log linear model output for 3 x 3...

   stackoverflow.com/questions/49197958

   Then the odds ratio such that I can state that the odds of being blue, given the object is high are ?? times higher/lower (plus 95% CI) than being green (given high) I would like these figures for each combination of colour/size. I am not sure which model parameters I need to exponentiate to get these odds

7. How can I convert logs to odds ratio and logs to probabilities...

   stackoverflow.com/questions/56810063

   From here: R: Calculate and interpret odds ratio in logistic regression After running logistic regression model with glm with logit link to convert logits to odds ratio, you can exponentiate it: ...

8. calculating odds ratio in python - Stack Overflow

   stackoverflow.com/questions/61023380

   First approach return odds ratio=9 and second approach returns odds ratio=1.9. I am relatively new to the concept of odds ratio and I am not sure how fisher test and logistic regression could be used to obtain the same value, what is the difference and which method is correct approach to get the odds ratio in this case.

9. lme4 - R: Generalized linear mixed models (GLMM):STANDARDIZED ...

   stackoverflow.com/questions/59100298

   And I can answer some your questions, try to avoid asking too many questions in one post. You don't need to convert your coefficients back to odds or log odds. It is your response variables that are transformed using logit. So you interpret your coefficients as change in log odds ratio per standard deviation of predictor –

10. r - Calculating odds ratio from glm output - Stack Overflow

    stackoverflow.com/questions/48140473

    Therefore, the odds of detection if the animal spends 0 minutes on site is e(-1.49644) or 0.2239. The odds ratio of detection if an animal is on site for X minutes is calculated as follows. We'll model odds ratios for minutes 0 through 10, and calculate the associated probability of detection.

11. R - lrm logistic regression coefficients / odds ratio?

    stackoverflow.com/questions/58752412

    The model coefficients will be in the form of log-odds (still on the log scale) # Log-odds. coef(fit) Intercept X1 X2 X3. 0.03419513 0.92890297 0.48097414 1.86036897. If you want to move to odds then we need to use exponentiation to transfer from the log scale. # Odds.