How is that possible? What does 0 mean? Is that right? Then my friend who said "since there is no variation, using ID as random effect is unnecessary" is correct? So, then would I use it as a fixed effect? But wouldn't the fact that there is so little variation suggest it isn't going to tell us much anyway? You see that my variance and SD from the individual ID as the random effect equals 0. Linear mixed model fit by maximum likelihoodįormula: Velocity ~ D.CPC.min + FD.CPC + (1 | ID) In my model with the random effect I also was trying to look at the output to see what kind of evidence or significance the RE has: lmer(Velocity ~ D.CPC.min + FD.CPC + (1|ID), REML=FALSE, family=gaussian, data=tv) But I wouldn't really know what I should compare between them anyway. Glmer/glm the log-likelihoods are not commensurate (i.e., theyĪnd here I assume this means you can't compare between a model with random effect or without. I ran the model with the individual as a random effect and without, but then I read Ben Bolker's GLMM FAQ where they state:ĭo not compare lmer models with the corresponding lm fits, or Maybe an initial question is: What test/diagnostic can I do to figure out if Individual is a good explanatory variable and should it be a fixed effect - qq plots? histograms? scatter plots? And what would I look for in those patterns.
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What I can't figure out is how to test if there really is something being accounted for when setting individual as a random effect. However, I am now being told that there is no need to include the individual as a random effect because there is not a lot of variation in their response.
So, I thought I would set the individual as a random effect. I want to know if they behave differently when they are in higher risk landscapes than not. Some of those moose were experimented on 2 or 3 times for a total of 29 trials. Ive been told a rule of thumb is if you have 4 or more groups/individuals which I do (15 individual moose).
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That is also testable against the data, if you compare the model above (with a LRT) to one in which the loadings are free to differ across latent indicators (as they do across manifest indicators of first-order factors).I am trying to understand when to use a random effect and when it is unnecessary. Note that you are fixing all higher-order loadings to 1, postulating the latent indicators are essentially tau-equivalent. Write your equations on separate lines, i.e., each operator can have multiple variables on the lefthand or righthand side, but there can only be one operator (e.g., =~) per line. In addition, how will you specify the formula for this model using lavaan in R? Is this the right way to write out the measurement model? You can compare the nested models using a likelihood ratio test (with the lavTestLRT() function) of the $H_0$ that the higher-order factor model is sufficient to explain the correlations among the 4 lower-order factors.īecause you used the data to decide on the 4-factor structure, you should gather an independent sample to validate your original findings.
The 4-factor CFA with simple structure will be less constrained than a hierarchical CFA because you replace 4*3/2=6 factor covariances with 4 higher-order factor loadings. Is that the right model to be specifying for my purposes? Perhaps I should also specify another model where there are only four factors? Now, I'm thinking of performing a confirmatory factor analysis that specifies a one-factor hierarchical structure with 4 facets.
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