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4spec_model_identical_cost.Rmd
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4spec_model_identical_cost.Rmd
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---
title: "ident cost runs"
author: "Kat Leigh"
date: "2/26/2021"
output: html_document
params:
species_c1: 30
#species_c2: 30
species_c3: 30
species_c4: 20
species_c5: 20
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
library(tidyverse)
library(ggplot2)
library(here)
library(quadprog)
library(future)
library(gtools)
library(future.apply)
source("C:/Users/kleigh/OneDrive/Documents/UCSB/quotabasket_model/working_model/functions/matrixCombn.R")
source("C:/Users/kleigh/OneDrive/Documents/UCSB/quotabasket_model/working_model/functions/every_basket_all_res.R")
source("C:/Users/kleigh/OneDrive/Documents/UCSB/quotabasket_model/working_model/functions/qb_stock_m.R")
source("C:/Users/kleigh/OneDrive/Documents/UCSB/quotabasket_model/working_model/functions/qb_stock_for_optim.R")
source("C:/Users/kleigh/OneDrive/Documents/UCSB/quotabasket_model/working_model/functions/matrix_nodup.R")
```
### Preparing the inputs and outputs for `qb_stock.r`:
For each species, we need:
* r - growth rate
* K - carrying capacity
* X - starting stock
* p - price
For each technology we need:
* Catchability per species
* Cost per unit effort
# Inputs
run this entire document for each scenario you want to test
## Step 1: Input parameters
```{r}
# Species:
# s = species, r, K, X, p
s1 <- c(s=1, r=0.2, K=100, X=50, p=20)
#s2 <- c(s=2, r=0.2, K=100, X=50, p=20)
s3 <- c(s=3, r=0.2, K=100, X=50, p=20)
s4 <- c(s=4, r=0.2, K=100, X=50, p=20)
s5 <- c(s=5, r=0.2, K=100, X=50, p=20)
# t = tech, q1 = catchability species 1, q2 = catchability species 2...
t1 <- c(t=1, q1=0.04#, q2=0.04
, q3=0.04, q4=0.04, q5 = 0.04)
#t2 <- c(t=2, q1=0.04#, q2=0.04, q3=0.04, q4=0.04, q5 = 0.04)
t3 <- c(t=2, q1=0.04#, q2=0.04
, q3=0.04, q4=0.04, q5 = 0.04)
t4 <- c(t=2, q1=0.04#, q2=0.04
, q3=0.04, q4=0.04, q5 = 0.04)
t5 <- c(t=2, q1=0.04#, q2=0.04
, q3=0.04, q4=0.04, q5 = 0.04)
# Tech cost: t = tech, cost = cost per unit effort
c1 <- c(t=1, cost=params$species_c1)
#c2 <- c(t=2, cost=params$species_c2)
c3 <- c(t=3, cost=params$species_c3)
c4 <- c(t=4, cost=params$species_c4)
c5 <- c(t=5, cost=params$species_c5)
# Baskets: binary matrix of # species x # baskets, where 1 means that species is in that basket
# b1 <- c(s1 = 1, s2 = 1, s3 = 0, s4 = 0, s5 = 0)
# b2 <- c(s1 = 0, s2 = 0, s3 = 1, s4 = 1, s5 = 1)
# b3 <- c(s1 = 0, s2 = 0, s3 = 1, s4 = 0, s5 = 0)
# b4 <- c(s1 = 0, s2 = 0, s3 = 0, s4 = 1, s5 = 0)
# b5 <- c(s1 = 0, s2 = 0, s3 = 0, s4 = 0, s5 = 1)
# Quotas: position = basket, number is the harvest limit.
# We want this to be 0.1 of the preceding stock under constant mortality, so this will have to change to just input constant m.
# This is a guess of optimal QB caps for each bakset. A good starting point is r/2.
cap_guess = c(0.2, 0.2)
# Years
years = 30
```
# Step 2: make a list of the inputs
```{r}
# Change these to reflect the number of species and technologies above:
species = list(s1, s3, s4, s5)
tech = list(t1, t3, t4, t5)
cost = list(c1, c3, c4, c5)
n_baskets = 2
```
# Step 3: get all possible basket arrangements (for 2 QBs) and their associated respective optimal QB caps
# Run this!
```{r}
# get every basket combo for your context, with the optimal mortality set up
output <- every_basket(species, tech, cost, n_baskets, cap_guess, years, cores = 2)
# get the data out of the list and into a workable list
extract <- plyr::laply(output[[1]], identity)
# create dataframes for each basket arrangement,
# add a column for total profits per year and total biomass per year,
# create objects for the total NPV, average profits, and average biomass for each basket arrangement
for(i in 1:length(extract)){
dfoutput <- cbind(as.data.frame(c(extract[[i]]$stock)),
as.data.frame(c(extract[[i]]$harvest)),
as.data.frame(c(extract[[i]]$effort)),
as.data.frame(c(extract[[i]]$profit_per_t)),
as.data.frame(c(extract[[i]]$mortality1)),
as.data.frame(c(extract[[i]]$mortality2)))
dfoutput <- dfoutput %>%
mutate(tot_profit = t_1+t_2.1+t_2.2#+t_2.3
,
tot_biomass = s_1+s_3+s_4+s_5)
assign(paste("basket",i, sep = "."),dfoutput)
assign(paste("NPV_basket",i, sep = "."),sum(dfoutput$tot_profit, na.rm = TRUE))
assign(paste("av_pft_basket",i, sep = "."),mean(dfoutput$tot_profit, na.rm = TRUE))
assign(paste("av_biomass",i, sep = "."),mean(dfoutput$tot_biomass))
}
# stick all the NVPs, profits, and biomasses into a df, arrange from highest to lowest to find the optimal arrangement for eac of these 3 criterion
NPVs <- mget(ls(pattern="NPV_basket")) %>%
unlist() %>%
as.data.frame() %>%
tibble::rownames_to_column("basket") %>%
arrange(-.)
av_pfts <- mget(ls(pattern="av_pft_basket")) %>%
unlist() %>%
as.data.frame() %>%
tibble::rownames_to_column("basket") %>%
arrange(-.)
av_stocks <- mget(ls(pattern="av_biomass[.]")) %>%
unlist() %>%
as.data.frame() %>%
tibble::rownames_to_column("basket") %>%
arrange(-.)
# identify the optimal basket arrangements
optimal_basket_NPV <-
head(NPVs,1)
optimal_basket_av_pft <-
head(av_pfts,1)
optimal_basket_stock <-
head(av_stocks,1)
optimals <- rbind(optimal_basket_av_pft, optimal_basket_NPV, optimal_basket_stock)
```
# Step 5: save results of the optimal basket arrangements and the summaries
```{r}
# this is designed to automatically extract the 3 optimal basket arrangements as well as their summary info. just change the file name for each run!
path_store <- "C:/Users/kleigh/OneDrive/Documents/UCSB/quotabasket_model/working_model/price and cost explore/"
csvname <- paste(c1[2],#".",s2[2],
".",c3[2],".",c4[2],".",c5[2],".csv",sep="")
csvfile <- paste(path_store, "4spec_opt_NPV_ident_c", csvname, sep="")
write.table(get(str_split(optimal_basket_NPV$basket, pattern = "_")[[1]][2]),csvfile, row.names=FALSE, sep=",")
# path_store <- "C:/Users/kleigh/OneDrive/Documents/UCSB/quotabasket_model/working_model/price and cost explore/"
# csvname <- paste(s1[5],".",s2[5],".",s3[5],".",s4[5],".",s5[5],".csv",sep="")
# csvfile <- paste(path_store, "4spec_opt_av_pft_p", csvname, sep="")
# write.table(get(str_split(optimal_basket_av_pft$basket, pattern = "_")[[1]][3]),csvfile, row.names=FALSE, sep=",")
#
# path_store <- "C:/Users/kleigh/OneDrive/Documents/UCSB/quotabasket_model/working_model/price and cost explore/"
# csvname <- paste(s1[5],".",s2[5],".",s3[5],".",s4[5],".",s5[5],".csv",sep="")
# csvfile <- paste(path_store, "4spec_opt_stock_p", csvname, sep="")
# write.table(get(paste("basket",str_split(optimal_basket_stock$basket, pattern = "[.]")[[1]][2], sep = '.')),csvfile, row.names=FALSE, sep=",")
#
# path_store <- "C:/Users/kleigh/OneDrive/Documents/UCSB/quotabasket_model/working_model/price and cost explore/"
# csvname <- paste(s1[5],".",s2[5],".",s3[5],".",s4[5],".",s5[5],".csv",sep="")
# csvfile <- paste(path_store, "4spec_opt_summaries_p", csvname, sep="")
# write.table(optimals,csvfile, row.names=FALSE, sep=",")
```