Jen Rose posted on Wednesday, March 12, 2014 - 8:34 am
I have a 3 class latent class analysis model with categorical observed variables, several categorical covariates and a categorical distal outcome.
When I look at the latent class odds ratios results comparing Class 1 to Class 2, I see that for a couple of my categorical observed variables, the odds ratios are not significant based on the p value. I'm interpreting this as meaning that there are no significant differences in the logits between the two classes for these variables. However, when I look at the 95% confidence intervals for the latent class odds ratio results (using CINTERVAL), the confidence intervals do not cross 1.0, which suggests to me that the difference between the 2 classes is significant.
Can you tell me what might be causing the discrepancy between the p values for the latent class odds ratio results and the corresponding confidence intervals?
I'm running an LCA with 3 classes and I'm interested in obtaining 95% confidence intervals for the item-response probabilities and latent class probabilities. I have added the CINTERVAL option to the output.
Are these the equations used to estimate the latent class probabilities for a 3-class model? class 1: EXP(C#1)/(1+EXP(C#1)+EXP(C#2)) class 2: EXP(C#2)/(1+EXP(C#1)+EXP(C#2)) class 3: 1/(1+EXP(C#1)+EXP(C#2))
If so, do I then just plug in the lower estimates for both C#1 and C#2 into each equation to obtain the lower confidence limit? And then plug in the upper estimates for the upper limit?
I have tried this, but I have found that these confidence limits are not symmetric and sometimes do not contain the actual estimate. Thanks in advance for your help!
Q2. I don't think that approach works when there isn't a 1-1 relation between the logit and probability. Try bootstrapping to capture any non-symmetry in the probability estimate distribution. Or, Bayes.
Even with these bootstrapped results, I still run into the problem of not being able to apply an equation to somehow exponentiate the upper and lower limits. Is there another equation I can use to exponentiate this output and obtain reportable confidence intervals?
Alternatively, is there another way to obtain confidence intervals for latent class probabilities for a 3-class LCA? Thanks again for your help!
Thanks! In the output, I do get the confidence intervals in probability scale for categorical variables. However, I donít get confidence intervals in the probability scale for the latent class probabilities because I have 3 latent classes - I only get confidence intervals of model results for the latent class probabilities. How can I these model results to obtain the confidence intervals in the probability scale for the latent class probabilities?
However, there are 3 errors and no indication of what is incorrect. The indicated errors immediately follow the second parentheses for each term - EXP(C#2) just before the division sign, EXP(C#1) just before the addition sign, and EXP(C#2) just before the second parentheses - in the formula.
How do I fix this error and is the code correct? Additionally, the output for the PROB estimate and its upper and lower limits is the same single estimate. I assume this is because of the error in the formula? If this error is fixed, would I obtain bcbootstrap cintervals?
Finally, C#1 and C#2 are the latent classes, so I haven't defined them in the input - do I need to somehow define them in order to obtain the cintervals? If so, how do I code that? Thanks again!!
What is considered large latent class odds ratios with ordinal variables? I'm running a LCA with four ordinal variables, each with 6 categories. I've heard that latent class ORs greater than 5 or less than .2 for binary variables are large. Is the same true for ordinal variables? Seems like the ORs should be expected to be smaller. Thanks!
I have a similar question to CB above (June 3 2015).
I would like confidence intervals around estimated class prevalences from a GMM.
When I apply the formulas given in CB's June 3rd post by hand, I am able to get from the logit estimates back to the prevalence estimates. But when I use the lower and upper limits of the logits (from the cinterval command) in those formulas, the limits do not always contain the prevalence estimate.
I cannot use cinterval(bootstrap) because my data are weighted so I am using MLR.
It sounds like you want a non-symmetric interval for the prevalence estimate (you get the symmetric one). If you have several logit estimates that are used to create the prevalence I don't think using their limits works. I'm not sure how to go about this given your weights.
Errors: *** ERROR EXP(CLASS#1) /(1 + EXP(CLASS#1) + EXP(CLASS#2) ) ^ ERROR (this is actually placed after the first closing paren) *** ERROR EXP(CLASS#1) /(1 + EXP(CLASS#1) + EXP(CLASS#2) ) ^ ERROR (this is placed after the second closing paren) *** ERROR EXP(CLASS#1) /(1 + EXP(CLASS#1) + EXP(CLASS#2) ) ^ ERROR (this is placed after the third closing paren)
In addition, the estimate I got did not match the estimated prevalence of class #1. Also, it listed that value for all of the cinterval results ( i.e. the lower .5%, lower 2.5%, etc. results were all the same).
I imagine I must have written the statement incorrectly. Any advice would be greatly appreciated.