# Is the linear mixed-effects model the right choice for analysing my data?

I'm in desperate need of advice in terms of the choice of statistical test for my analysis.

Briefly to explain what I am analyzing in an animal model: I want to see the effect of 2 categorical variables ("genetic_profile" and "sex") and "age" variable (recorded in exact days which vary between subjects but the data is generally split into 3 and 9 months-old) on the continuous variable - "measurement". There are 52 measurements (24 come from paired animals - i.e. measure was taken twice in 12 out of 38 animals). The measurement was taken using a program that displays processed brain images and two points were selected manually to measure the thickness of a small anatomical feature.

Outcome variable: measurement

Predictor variables: genetic_profile (mutant/wild-type), sex (female/male), age (in days),

This is the model in R (also accounting for a possible interaction between genetic_profile and age). The random effect comes from animal.

model = lmer(measuremnet ~ Genetic_profile*Age + Gender +
(1 | animal), data = data_set)


I attached a picture of a plot of residals vs fitted (plot(model)) and it definitely does not look like the right fit (residuals form 3 parallel stripes across the plot).

I believe it's important to mention that measurement has presumably a large degree of error since the precision measure was to just 1 decimal place and essentially all measurements that were recorded were between 0.3 and 0.5 (differed by 0.1, 0.2 or 0.3 mm at most).

Should I try and find a way to apply this model or was the suggestion wrong? I presume that a t-test should be applied if the linear mixed model is incorrect?

• Your measurement variable is some sort of scale, isn't it? These sorts of residuals are observed when you do a linear regression on say likert data. Can you tell us more about the outcome? What is it, what is it measuring, how is it performed? Apr 22, 2021 at 23:32
• Thank you for your reply! What I'm measuring is the thickness of a small anatomical feature on images. Almost all collected data fits between 0.3 and 0.5 mm (and precision is to just 1 decimal place). This may be a reason why it appears as a scale although it actually isn't. Apr 22, 2021 at 23:42
• If your outcome consists of 0.3, 0.4, and 0.5 then the noise in the data is decidedly not normal. You'll need to think of a different approach. Can edit your question with as much detail on how the outcome is measured as you can? Are you rounding the outcome, or is a machine rounding the outcome? Apr 22, 2021 at 23:58
• Thank you! I've edited the post and I hope it explains it a bit better. So I'm taking the measurement by manually selecting 2 points in a program that displays the image and the program itself is responsible for rounding it. Unfortunately, I couldn't find a way to increase the precision and I have to complete the analysis with this dataset. Apr 23, 2021 at 0:13
• 0. Welcome to CV.SE. 1. You might want to consider consider your response being ordinal rather than continuous. Apr 23, 2021 at 1:27