What is the best model code for R longitudinal: 19 time points with 6 groups (1 factor with 2 levels, 1 factor with 3 levels), two response variables

Data from a longitudinal biology study. Is there a difference between groups over time? 3 independant factorial fixed effects (solution type with 2 levels, temperature with 3 levels, time with 19 levels) = 6 groups. There are 2 response variables (continuous variable: weight(mg)) and (percentage: (above last weight)). There will be a correlation between time readings as the same groups are measured over 19 time points.

This perhaps wants two models, one for the continuous response, one for percent.

Plot, model, assumptions, re-plot. Thank you!

plotted with

ggplot(ab, aes(x = time, y = response, colour = type, group = type)) +
  geom_point() +
  geom_line() +
  facet_wrap(ab$temp) +
  scale_colour_manual(values = c(DS = "blue", MT = "green"), labels = c("Control", "Treatment")) +
  labs(colour='type') +
  xlab("") +
  ylab("") +
  scale_y_continuous(limits = c(0, 50)) +
  labs(title = "") +
  theme_bw() +
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))

Best code for the model & assumptions? Cheers in advance



Read more here: https://stackoverflow.com/questions/66279109/what-is-the-best-model-code-for-r-longitudinal-19-time-points-with-6-groups-1

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