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The interpretation is the same as for a generalised linear model, except that the estimates of the fixed effects are conditional on the random effects.
Since this is a generalized linear mixed model, the coefficient estimates are not interpreted in the same way as for a linear model. In this case you have a binary outcome with a logit link, so the raw ...
5
Assuming that you have multiple or repeated measures/observations per state, then yes, it makes sense to fit random intercepts for state. You are correct that random effects are used when the observed levels of a factor are taken from a wider population, however that is not the only justification. If you have a large number of levels and there is correlation ...
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A couple of extra notes on top of what @RobertLong already answered:
As Robert also noted, the interpretation of the coefficients from generalized linear mixed models are conditional on the random effects. Most often this is not the interpretation you are looking for. For more info on this check here.
You have fitted the model with the default Laplace ...
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In models with nonlinear link functions in general there is indeed a difference in the interpretation of the regression coefficients in GEEs and mixed-effects models. In short,
GEEs give you the more usual interpretation of comparing groups of subjects. E.g., for dichotomous outcomes and the logit link you get the log-odds ratio between the group of males ...
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The Penalized Quasi Likelihood (PQL) method has been proposed to fit generalized linear mixed-effects models. The way it works is by doing a kind of a Laplace approximation in a quasi-likelihood formulation of the model. This approximation results in a transformation of the original outcome variable. The aim of the transformation is to make the transformed ...
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You can do this in the R package mcp. Although your actual full model may be outside the scope of mcp, this is a way to do "random effects" change points.
The mcp package contains a demo dataset called ex_varying:
> library(mcp)
> head(ex_varying)
id x id_numeric y
1 John 1 5 30.792018
2 John 5 5 1.027091
3 John 9 ...
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You have too few observations to include in your initial model that many predictors. Also, note that for binary data the effective sample size is determined by the minimum of the frequencies of the zeros and the ones. Hence, you have very little information in your data to obtain any meaningfully stable results.
Finally, as noted in the comments by EdM, ...
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This seems to be a design with repeated measures within subjects. This means that measurements within a particular subject are likely to be more similar to each other than to other subjects. That is, there will be correlation within subjects. One way to proceed with this is to use a mixed effects model and fit random intercepts for subject.
In the common ...
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Typically, you'd use something like corCAR1(form = ~ date | site) or corAR1(form = ~ months_since_start_of_timeseries | site). This specifies time as an auto-correlation co-variate and groups by site. If your time series are perfectly regular and the data sorted by time, you could use corAR1(form = ~ 1 | site).
However, I suggest that you do not use a ...
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It would be helpful to know why you would choose to model the data in this way. However in terms of the multilevel model, this is technically possible to do. I would suggest group mean centering the within-unit sum by subtracting each unit's mean from their time-specific value on the sum. In R's dplyr:
df <- df %>% group_by(unit) %>% mutate(...
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In the two-stage approach, in the first step, you summarize the repeated measurements per peptide_Id into a single number. This inevitably leads to some information loss. To give another example, say that you measure the blood pressure of a patient ten times. Even though these measurements are correlated, they contain more information than their average. The ...
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Both an ANOVA and mixed modeling approach will work for your data. I am more familiar with mixed models, and can speak better to that. However, you are correct that you can run your analysis as a mixed model and then get an ANOVA table afterward. If you are using R, then you can use lmer to first estimate the mixed model:
require(lme4)
m <- lmer(outcome ~...
1
The lcmm() function fit a latent class linear mixed-effects model. This postulates that there are some underlying sub-populations in your data that you wish to recover. The model is estimated using maximum likelihood, and therefore it will provide you with correct inferences provides that any missing data in your outcome variable are missing at random and ...
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Normalizing the dependent variable as you have does not make sense. It would be one thing to take a regular z-score of it (based on the sample mean and standard deviation). Sometimes people do that to their predictors and outcome to get their coefficients into an effect size metric - 1 standard deviation increase in predictor is associated with XX standard ...
1
A couple of notes:
In glmmTMB() and for a normally distributed outcome, specifying the random-effects structure as (1 + factor | ID) will be equivalent to us(0 + factor | ID) provided that dispformula = ~ 0.
However, this is not equivalent to compound symmetry. The compound symmetry structure typically assumes that the covariance is the for all pairs of ...
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As noted in the comments of the other responders you have a quite small dataset, which makes fitting a mixed model tricky.
In general, you could give a try to different optimization algorithms, and altering the defaults. For example, the simple random intercepts model seems to converge with GLMMadaptive when you increase the number of EM iterations, i.e.,
...
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