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lin.R
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lin.R
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# =============================================================================
# Lin Concordance Correlation Coefficient (Lin CCC)
# Supporting Information for Early-Capistrán et al. (2020), PeerJ
# [email protected] - February 2020
# =============================================================================
# =============================================================================
# Load libraries
# =============================================================================
# Check if required libraries are installed and install
# if necessary
packages <- c("DescTools", "ggplot2", "ggthemes", "dplyr")
if (length(setdiff(packages, rownames(installed.packages()))) > 0) {
install.packages(setdiff(packages, rownames(installed.packages())))
}
library(DescTools)
library(ggplot2)
library(ggthemes)
library(dplyr)
# .............................................................................
# NOTE:
# If you did not open this script directly from ".../quantifying_lek_data_code",
# please define the working directory to ".../quantifying_lek_data_code":
#
# setwd(".../quantifying_lek_data_code")
#
# .............................................................................
# =============================================================================
# Load and prepare data
# =============================================================================
# Load standardised CPUE and fisheries landing data ...........................
data = read.csv("data/comp_dataset.csv", header=TRUE)
# Remove rows with missing values .............................................
comp_data = data[complete.cases(data),]
#attach(comp_data)
# Standardise values to z-scores ..............................................
# Define variables
cpue <- comp_data$stCpue
landing <- comp_data$totalLanding
# Define z-scores and add to data frame
comp_data[,'zStCpue']<-(cpue-mean(cpue))/sd(cpue)
comp_data[,'zLanding']<-(landing-mean(landing))/sd(landing)
# =============================================================================
# Plot z-scores for exploratory visual evaluation
# =============================================================================
# Plot z-scores for standardised CPUE
p1 <- ggplot(comp_data, aes(x = yearSerial, y = zStCpue)) +
geom_point(size=2.5, colour = "steelblue") +
labs(x="Year", y="Standardised CPUE (z-score)") +
theme_hc()+ scale_colour_hc()
#Plot z-scores of CPUE and total annual landings
p2 <- p1 + geom_point(aes(x = yearSerial, y = zLanding),
size=2.5, color="tomato", shape=1) +
labs(x="Year (serialised)", y="z-scores for CPUE and annual landings")
p2
# =============================================================================
# Run Lin CCC
# =============================================================================
# Define variables ............................................................
zStCpue = comp_data$zStCpue
zLanding = comp_data$zLanding
# Run Lin CCC .................................................................
lin.ccc <- CCC(zStCpue, zLanding, ci = "z-transform",
conf.level = 0.95, na.rm = TRUE)
lin.ccc