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!

```
head(ab)
temp type ID dry_weight_mg time weight.mg pc.dryweight
<fct> <fct> <fct> <dbl> <fct> <dbl> <dbl>
1 25 DS DS25 1820 0m 1820 0
2 25 DS DS25 1820 5m 1921 5.55
3 25 DS DS25 1820 20m 1998 9.78
4 25 DS DS25 1820 30m 1999 9.84
5 25 DS DS25 1820 12.5h 2169 19.2
6 25 DS DS25 1820 24h 2241 23.1
```

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