models-amplitude_Miedes.Rmd

---
title: "R Notebook"
output: html_notebook
---

```{r}
library(tidyverse)
library(nlme)
library(MuMIn)
```


```{r}
df <- read.csv('cooked-data/amplitudeAndClimate-Miedes.csv')
df <- df %>% mutate_at (vars(site, class, series), \(x) {as.factor(x)}) %>% mutate(date = as.Date(date)) %>% dplyr::rename(id = series)
df <- df %>% mutate( year = year(date), doy_yearoffset = (year - 2022) * 365 + doy)
str(df)
summary(df)
```


```{r}
date_start2022 <- "2022-04-01" # from first of April
date_end2022 <-"2022-10-30" # to 30 October

date_start2023 <- "2023-04-01" # from first of April
date_end2023 <-"2023-10-30" # to 30 October

df2022 <- df[which(df$date>=date_start2022 & df$date<=date_end2022),]
df2023 <- df[which(df$date>=date_start2023 & df$date<=date_end2023),]
df <- rbind.data.frame(df2022, df2023)
summary(df)
str(df)
```


```{r}
ggplot() + geom_line( aes (x = date, y = ampl, col = id), data = df)
# library(plotly)
# plot_ly(data = df, y = ~ampl, x = ~ date, color = ~id)
# plot_ly(data = df, mapping = aes(x = date, y = ampl, col = id)) + geom_line()
```


```{r}
boxplot(log10(ampl+1) ~ class, data = df)
```


```{r}
pines <- df %>% filter(class == "D" | class == "ND") %>% mutate(class= factor(class), id = factor(id))
str(pines)
```

```{r}
model.lme.pines <- lme(log10(ampl+1) ~ class, random = ~ 1|id, correlation=corAR1(form = ~doy_yearoffset|id), data = pines,  method = "ML")
sel.pines <- dredge(model.lme.pines)
View(sel.pines)
# model.lme.clim.pines <- lme(log10(ampl+1) ~ class * max.vwc + class * mean.temp, random = ~ 1|id, correlation=corAR1(), data = pines,  method = "ML")
# sel.pines.clim <- dredge(model.lme.clim.pines)
# View(sel.pines.clim)
```

Adding specie column to see if there's difference between species:
```{r}
df <- df %>% mutate(sp = case_when(class == 'D' | class == 'ND' ~ factor('Pine'),
                                   class == 'Quercus' ~ factor('Quercus')
                                   ))
```


```{r}
model.lme.ar <- lme(log10(ampl+1) ~ sp, random = ~ 1|id, correlation=corAR1(form = ~doy_yearoffset|id), data = df,  method = "ML")
sel.sp <- dredge(model.lme.ar)
View(sel.sp)
```


Now, see if climate vars are relevant
```{r}
model.lme.clim.ar <- lme(log10(ampl+1) ~ max.vwc + mean.temp, random = ~ 1|id, correlation=corAR1(form = ~doy_yearoffset|id), data = df,  method = "ML")
sel.clim <- dredge(model.lme.clim.ar)
View(sel.clim)
```

Extra, try again using lme4:
# ```{r}
# library('lme4')
# options(na.action = "na.fail")
# mod_pines <- lmer(log10(ampl+1) ~ class+(1|id)+(1|doy),data=pines)
# sel.pines2 <- dredge(mod_pines)
# View(sel.pines2)
# mod_all <- lmer(log10(ampl+1) ~ class+max.vwc*mean.temp+(1|id)+(1|doy), data=df, REML="FALSE")
# sel.all <- dredge(mod_all)
# View(sel.all)
```