models-amplitude_allsites.Rmd

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

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


```{r}
df <- read.csv('cooked-data/amplitudeAndClimate-allsites.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, varwidth  = T)
```


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

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