scrumpy posted on Thursday, October 27, 2005 - 2:26 pm
Hi- I am running a latent variable mixture model that yields 2 classes from 8 indicators. The output provides the odds ratio comparing the classes on each indicator.
How would I calculate the confidence intervals to know which odds ratios are significantly different from one another? Is this in the output somewhere? Or is there a formula to calculate CI from SE in odds ratios?
what is the formula to calculate the confidence interval for an odds ratio? which distribution does this follow?? And is an 'adjusted odds rato' different from odds ratio?
Thank you very much.
BMuthen posted on Saturday, November 12, 2005 - 6:15 pm
These are given in the Hosmer and Lemeshow logistic regression book cited on the website or other logistic regression books. Essentially, these build on the lower and upper confidence interval limits for the logistic regression coefficients(log odds) which are then exponentiated to give you the corresponding odds. An adjusted odds ratio is an odds ratio for a binary x variable where you have other x variables in the logistic regression.
How would I calculate the odd ratios confidence intervals in MULTINATIONAL LOGISTIC REGRESSION whereby imputation is used. without imputation CINTERVALS in the OUTPUT command yields the CI. why MPLus dont give the CI with imputation.
I'm running an LCA with 2 classes and I'm interested in obtaining 95% confidence intervals for the item-response probabilities and latent class probabilities. I know that I can add the CINTERVAL option to the output. However, can you describe how to obtain 95% confidence intervals for the latent class probabilities, since these are means rather than thresholds? I know only one class has an estimated parameter and the other class is the complement, so I'm unsure how to obtain the confidence intervals for both classes. Any references/resources that you could share would be helpful too. Thanks!
I am having difficulty calculating the CI for the ORs of DCAT output. I am using the formula as noted in the document in the FAQ section 'logOR ± 1.96*SE(logOR). Then exponentiate those two limits to get the OR limits'
The problem is either that the CI does not contain the OR and\or that it is indicating a significant result when the p value is highly insignificant or vice versa. Here is an example.
Comorbidities - Est= 0.029 SE=0.068 p=0.673 OR= log(OR) of 0.029 = 1.03 CI = 0.029+\- 1.96*0.068 = (0.16, 0.10) exp CI (1.11, 1.18)
So as you can see the OR is not within the CI and the CI is in conflict with the p value. Am I doing something incorrectly or have I missed something? thank you
aurora posted on Tuesday, April 05, 2016 - 9:46 pm
Hello Drs. Muthen,
I am running a discrete time survival analysis with time-varying/independent covariates. In your board, adjusted odds ratio is explained as an odds ratio for a binary x variable where there are other x variables in the logistic regression. Then how could I obtain an adjusted odd ratio?
Hi there I am running a multinomial logistic regression (4 levels on DV, 4 continuous IVs, 2 binary. TYPE=COMPLEX.
1) How do I determine the overall 'fit' of the model (e.g. in SPSS I get a log likelihood and Chi square of the final model compared with the intercept)?
2) How do I get confidence intervals? tried CINTERVALS in my output, but can't bootstrap with clusters
3) If the model runs without warnings can I assume it is identified/reliable (even with only 6 in one group of DV)? I screened for too small expected cell frequencies, linearity of the logit, multicollinearity, etc
5) How do I get a pseudo R2?
6) How do I interpret the intercept given for each level of my DV. Is it the probability of being in that level of the DV vs the reference group given all the predictors in the model (e.g. U1 vs U4)? Or not accounting for any predictors? Or something else?
1) Run 2 models where one has all the slopes fixed at zero.
2) You should get CIs. If not, send output to Support along with license number.
3) Identified, yes, but the question is how good the estimates and SEs are - a Monte Carlo simulation would be needed.
5) You get McFadden's R-square by the runs in 1).
6) See books on multinomial regression, including our new book.
Amber Fahey posted on Friday, April 20, 2018 - 10:48 pm
In growth modeling, does the p-value for log odds provided in the output mean anything? I have several instances where the p-value was > .05 for my logistic regression coefficient but the calculated odds fell within the lower and upper bounds of CI 95%.
In the same model, I also calculated very large log values e.g., 111.05 and 39.65. I'm wondering if this is an indication that there is a problem with my model. Any feedback is greatly appreciated! Example: Log Odds = 0.19, p=.06 SE = (0.10) Odds =1.21 CI 95% = (0.99, 2.70)