hope all of you are safe during these pandemic scenario.
I would describe my case so you can get a better understatement of my question.
I work with a wild population of a bird species in which we can find two different types of individuals: those with a subordinate status called floaters (birds with no territory which is secretively moving inside the population) and those with a high status called territorials (birds that defend a territory, a partner and a nest). Our fieldwork procedure let us distinguish through their behavior between these two types of individuals and our currently question is if there are any morphological differences between floaters and territorial individuals.
For this aim, we have 7 cohorts of 1-year adults and its morphological data (weight, tarsus length (both to measure condition), length of ornamental feathers, amount spotted plumage... etc) to compare between floaters and territorials (F vs. T).
And I've built the following model after exploring and clearing up my data set: a logistic regression using lme4 with year as a random factor. After getting the results on the summary, I've checked the residuals using the package DHARMa and everything is ok.
logistic <- glmer(status ~ body_condition + plumage + feather_length + (1|year) data = data, family = "binomial") summary(logistic) Generalized linear mixed model fit by maximum likelihood (Laplace Approximation) ['glmerMod'] Family: binomial ( logit ) Formula: status ~ body_condition + mot_suma + feather_length + (1 | capture_year) Data: data2 AIC BIC logLik deviance df.resid 239.4 255.2 -114.7 229.4 171 Scaled residuals: Min 1Q Median 3Q Max -1.8357 -0.7930 -0.5998 1.0149 2.2089 Random effects: Groups Name Variance Std.Dev. capture_year (Intercept) 0.1558 0.3947 Number of obs: 176, groups: capture_year, 6 Fixed effects: Estimate Std. Error z value Pr(>|z|) (Intercept) -1.89970 2.01301 -0.944 0.34532 body_condition -0.51050 0.18749 -2.723 0.00647 ** mot_suma -0.46016 1.10950 -0.415 0.67833 feather_length 0.04145 0.05569 0.744 0.45668 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Correlation of Fixed Effects: (Intr) condtn mot_sm body_condi 0.179 mot_suma -0.081 -0.057 fethr_lngth -0.991 -0.177 0.064
I am new to GLMMs and I was wondering if after checking the model residuals it would be necessary to do any other type of validation like K-fold cross validation to demonstrate that the built model is actually right.
I've spent the last days searching for ways to perform k-fold CV in R for GLMM but I've only found how to perform it with a glm, not a glmm. I could use some help right now because I feel quite lost!