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# load packages 
library(nlsLoop)
library(tidyverse) # just need dplyr

# load in data
data(Chlorella_TRC)

# fit nlsLoop object
fits <- nlsLoop(ln.rate ~ schoolfield.high(ln.c, Ea, Eh, Th, temp = K, Tc = 20),
                data = Chlorella_TRC,

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# Test hypothesis that activation energy of community is determined from activation energies of its determinants

n = 3

community <- data.frame(expand.grid(temp = seq(275, 305,0.5), curve = c(1:3)))
params <- data.frame(ln.c = c(1.5, 1, 0.5), 
                    Ea = c(0.7, 0.5, 0.3), 
                    Eh = rep(rnorm(n, 3, sd = 0.5)), 
                    Th = c(rnorm(n, 300, sd = 2)), 
                    curve = c(1,2,3))

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# function to get confidence intervals
confint_nlsLoop <- function(x, params, data, id_col, formula, ...){
  dat <- data[data[,id_col] == x,]
  params2 <- params[params[,id_col] == x,]
  fit = NULL
  try(fit <- minpack.lm::nlsLM(formula,
                               data = dat,
                               start = params2[colnames(params2) %in% all.vars(formula[[3]])]), silent = TRUE)
  if(!is.null(fit)){
    confint <- nlstools::confint2(fit)

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structure(list(lat = c(50.16996, 50.16887, 50.16943, 50.17204, 
50.17247, 50.17227, 50.17302, 50.17354, 50.17365, 50.17249, 50.17205, 
50.17442, 50.17689, 50.1767, 50.17634, 50.17601, 50.17592, 50.17549, 
50.17402, 50.17379, 50.17314, 50.17323, 50.17566, 50.17714, 50.17919, 
50.18199, 50.18284, 50.1838, 50.18447, 50.18505, 50.1849, 50.18843, 
50.1909, 50.19203, 50.19184, 50.19247, 50.19406, 50.19381, 50.19416, 
50.1958, 50.19621, 50.19623, 50.19792, 50.19717, 50.1982, 50.19857, 
50.19901, 50.19888, 50.19992, 50.20095, 50.19956, 50.19857, 50.1974, 
50.19619, 50.19529, 50.19273, 50.19154, 50.1862, 50.18248, 50.18177, 
50.17728, 50.17513, 50.17383, 50.17108, 50.17116, 50.17228, 50.17224, 

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library(dplyr)
library(tidyr)

# create dummy data
Class <- rep(seq(1,3,1),2)
Count <- runif(length(Class),50,100)
Quad_ID <- c(rep("A",3),rep("B",3))

x <- data.frame(Class,Count,Quad_ID)

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# load package
library(nlsLoop)
library(nlme)
library(ggplot2)
library(dplyr)

# create dummy data
example <- data.frame(expand.grid(K = 280:330, rep = 1:3))

# create dummy params

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# good coding practice ####
# 1. #hashtag your code so you know what it does
# 2. clear workspace and load packages at the top to keep track of what you have loaded
# 3. make sure your working directory is in the right place
# 4. space things out in a way that makes your code readable to you
# 5. google things you do not understand. The answers are out there, go find them
# 6. do not get scared/angry when you get errors. It does get easier.... eventually

# clear workspace #### Good code practice to do first
rm(list = ls())

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# example of a VERY simple binomial regression ####

# good coding practice (in my opinion anyway!) ####
# 1. #hashtag your code so you know what it does
# 2. clear workspace and load packages at the top to keep track of what you have loaded
# 3. make sure your working directory is in the right place
# 4. space things out in a way that makes your code readable to you
# 5. google things you do not understand. The answers are out there, go find them
# 6. do not get scared/angry when you get errors. It does get easier.... eventually

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# good coding practice ####
# 1. #hashtag your code so you know what it does
# 2. clear workspace and load packages at the top to keep track of what you have loaded
# 3. make sure your working directory is in the right place
# 4. space things out in a way that makes your code readable to you
# 5. google things you do not understand. The answers are out there, go find them
# 6. do not get scared/angry when you get errors. It does get easier.... eventually

# clear workspace #### Good code practice to do first
rm(list = ls())

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# good coding practice ####
# 1. #hashtag your code so you know what it does
# 2. clear workspace and load packages at the top to keep track of what you have loaded
# 3. make sure your working directory is in the right place
# 4. space things out in a way that makes your code readable to you
# 5. google things you do not understand. The answers are out there, go find them
# 6. do not get scared/angry when you get errors. It does get easier.... eventually

# clear workspace #### Good code practice to do first
# need to install mise first!