Hi everyone,
I have a pretty simple question regarding the use of LMMs. I have data representing number of vesicles for individual animals counted, each having one of three possible genotypes. There are three animals per genotype and about 90 repeated measurements for each animal. Each repeated measurement represents an individual axon. Can I use an LMM to analyze the effect of Genotype on the number of vesicles, while specifying that the repeated measurements of each animal are not independent?
Vesicle ~ Genotype + (1 | animal)
Or does this now lead to an overestimation of the significance? Usually I would have averaged the repeated measurements for each animal and then done a one-way ANOVA, but I wondered whether I can take advantage of the large number of repeated measurements.
Thanks in advance.
PS: Because the data was count data and a Poisson distribution resulted in overdispersion, I used a negative-binomial for the model. In R this means I used glmer.nb() to create the model.
I have a pretty simple question regarding the use of LMMs. I have data representing number of vesicles for individual animals counted, each having one of three possible genotypes. There are three animals per genotype and about 90 repeated measurements for each animal. Each repeated measurement represents an individual axon. Can I use an LMM to analyze the effect of Genotype on the number of vesicles, while specifying that the repeated measurements of each animal are not independent?
Vesicle ~ Genotype + (1 | animal)
Or does this now lead to an overestimation of the significance? Usually I would have averaged the repeated measurements for each animal and then done a one-way ANOVA, but I wondered whether I can take advantage of the large number of repeated measurements.
Thanks in advance.
PS: Because the data was count data and a Poisson distribution resulted in overdispersion, I used a negative-binomial for the model. In R this means I used glmer.nb() to create the model.