# Suggestion about morphometric analysis in R using lm or lme

Maybe this is an over-the-top question but I have many doubts regarding my recent analysis about deer skull measurements and how to proceed with the analysis. This is a sample of my dataset:

   Factor1 population manage foraging height biome abundance area  forest plough
-0.6033788 ADA_BEC   best    fields   plain  agS    1500    73154  61154 12000
0.3250981 ADA_BEC   best    fields   plain  agS    1500    73154  61154 12000
0.5577059 ADA_BEC   best    fields   plain  agS    1500    73154  61154 12000
-0.1596194 ADA_BEC   best    fields   plain  agS    1500    73154  61154 12000
-1.3089952 ADA_BEC   best    fields   plain  agS    1500    73154  61154 12000
-2.1693392 ADA_BEC   best    fields   plain  agS    1500    73154  61154 12000
-0.9669080 ADA_BEC   best    fields   plain  agS    1500    73154  61154 12000
-1.8857842 ADA_BEC   best    fields   plain  agS    1500    73154  61154 12000
0.7242678 ADA_BEC   best    fields   plain  agS    1500    73154  61154 12000
1.6815373 ADA_BEC   best    fields   plain  agS    1500    73154  61154 12000


Factor1 are factor scores of all individuals (567) divided into 12 populations (population column). Others are either factor (manage (4lvl), foraging (3lvl), height (2lvl) and biome (4lvl)) or continuous, different for every population, (abundance, area (in ha), which totals forest+ploughland (also in ha)). Now I tried with all traditional uni-variate statistics such as anova, ancova, lm but, off course, my design is unballanced. My question is are there any general modeling solutions to incoporating all of these in a maximal model, simplifying it further and decide what is the most influential factor. Could mixed-effects model be used? Basic data are in fact, 50 measurements on every individual skull.

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lmer(Factor1 ~ manage + foraging + height + biome + abundance + area + forest + plough + (1|population), data = your.dataset)