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plot_ml.R
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###############################################################
########## plot after ML
###############################################################
plot_ml <- function(data, comp, index, algo, pdf = F, taxo_group) {
#combi1 <- inf_$indices[,"NSI"]
#combi2 <- COMP[,"NSI"]
#index <- "NSI"
#algo <- "Inference assigned metazoan - V9"
combi1 <- data
combi2 <- comp[,index]
## agregate results by grab
# predictions
combined1 <- aggregate(combi1, by=list(comp$Grab, comp$Station,comp$Locality), FUN=mean)[,"x"]
combined1_sd <- aggregate(combi1, by=list(comp$Grab, comp$Station,comp$Locality), FUN=sd)[,"x"]
combined1_sd[is.na(combined1_sd)] <- 0
# know values
combined2 <- aggregate(combi2, by=list(comp$Grab, comp$Station,comp$Locality), FUN=mean)[,"x"]
# keep farm_name vector
farm_nam <- aggregate(combi1, by=list(comp$Grab, comp$Station,comp$Locality), FUN=mean)[,"Group.3"]
## colors
col <- aggregate(comp$col_plot, by=list(comp$Grab, comp$Station,comp$Locality), FUN=mean)[,"x"]
# for storage
comb1_disc <- c(rep(0, length(combined1)))
comb2_disc <- c(rep(0, length(combined2)))
### if AMBI
if (index == "AMBI")
{
for (i in 1:length(combined1))
{
if (combined1[i] < 1.2) comb1_disc[i] <- 1
else if (combined1[i] >= 1.2 && combined1[i] < 3.3) comb1_disc[i] <- 2
else if (combined1[i] >= 3.3 && combined1[i] < 4.3) comb1_disc[i] <- 3
else if (combined1[i] >= 4.3 && combined1[i] < 5.5) comb1_disc[i] <- 4
else if (combined1[i] >= 5.5 && combined1[i] <= 6) comb1_disc[i] <- 5
else if (combined1[i] >= 6) comb1_disc[i] <- 5 # if above 6 (it can happens with MXnet for instance)
if (combined2[i] < 1.2) comb2_disc[i] <- 1
else if (combined2[i] >= 1.2 && combined2[i] < 3.3) comb2_disc[i] <- 2
else if (combined2[i] >= 3.3 && combined2[i] < 4.3) comb2_disc[i] <- 3
else if (combined2[i] >= 4.3 && combined2[i] < 5.5) comb2_disc[i] <- 4
else if (combined2[i] >= 5.5 && combined2[i] <= 6) comb2_disc[i] <- 5
else if (combined2[i] >= 6) comb2_disc[i] <- 5 # just in case here
}
## regression
diff <- comb1_disc-comb2_disc
good_ass <- length(subset(diff, diff ==0))
bad_ass <- length(subset(diff, diff !=0))
}
### if microAMBI
if (index == "microAMBI")
{
for (i in 1:length(combined1))
{
if (combined1[i] < 1.2) comb1_disc[i] <- 1
else if (combined1[i] >= 1.2 && combined1[i] < 3.3) comb1_disc[i] <- 2
else if (combined1[i] >= 3.3 && combined1[i] < 4.3) comb1_disc[i] <- 3
else if (combined1[i] >= 4.3 && combined1[i] < 5.5) comb1_disc[i] <- 4
else if (combined1[i] >= 5.5 && combined1[i] <= 6) comb1_disc[i] <- 5
else if (combined1[i] > 6) comb1_disc[i] <- 5
if (combined2[i] < 1.2) comb2_disc[i] <- 1
else if (combined2[i] >= 1.2 && combined2[i] < 3.3) comb2_disc[i] <- 2
else if (combined2[i] >= 3.3 && combined2[i] < 4.3) comb2_disc[i] <- 3
else if (combined2[i] >= 4.3 && combined2[i] < 5.5) comb2_disc[i] <- 4
else if (combined2[i] >= 5.5 && combined2[i] <= 6) comb2_disc[i] <- 5
else if (combined2[i] > 6) comb2_disc[i] <- 5
}
## regression
diff <- comb1_disc-comb2_disc
good_ass <- length(subset(diff, diff ==0))
bad_ass <- length(subset(diff, diff !=0))
}
### if NSI
if (index == "NSI")
{
for (i in 1:length(combined1))
{
if (combined1[i] < 10) comb1_disc[i] <- 1
else if (combined1[i] >= 10 && combined1[i] < 15) comb1_disc[i] <- 2
else if (combined1[i] >= 15 && combined1[i] < 20) comb1_disc[i] <- 3
else if (combined1[i] >= 20 && combined1[i] < 25) comb1_disc[i] <- 4
else if (combined1[i] >= 25 && combined1[i] <= 31) comb1_disc[i] <- 5
else if (combined1[i] > 31) comb1_disc[i] <- 5
if (combined2[i] < 10) comb2_disc[i] <- 1
else if (combined2[i] >= 10 && combined2[i] < 15) comb2_disc[i] <- 2
else if (combined2[i] >= 15 && combined2[i] < 20) comb2_disc[i] <- 3
else if (combined2[i] >= 20 && combined2[i] < 25) comb2_disc[i] <- 4
else if (combined2[i] >= 25 && combined2[i] <= 31) comb2_disc[i] <- 5
else if (combined2[i] > 31) comb2_disc[i] <- 5
}
## regression
diff <- comb1_disc-comb2_disc
good_ass <- length(subset(diff, diff ==0))
bad_ass <- length(subset(diff, diff !=0))
}
### if NQI1
if (index == "NQI1")
{
for (i in 1:length(combined1))
{
if (combined1[i] < 0.31) comb1_disc[i] <- 1
else if (combined1[i] >= 0.31 && combined1[i] < 0.49) comb1_disc[i] <- 2
else if (combined1[i] >= 0.49 && combined1[i] < 0.63) comb1_disc[i] <- 3
else if (combined1[i] >= 0.63 && combined1[i] < 0.82) comb1_disc[i] <- 4
else if (combined1[i] >= 0.82 && combined1[i] <= 1) comb1_disc[i] <- 5
else if (combined1[i] > 1) comb1_disc[i] <- 5
if (combined2[i] < 0.31) comb2_disc[i] <- 1
else if (combined2[i] >= 0.31 && combined2[i] < 0.49) comb2_disc[i] <- 2
else if (combined2[i] >= 0.49 && combined2[i] < 0.63) comb2_disc[i] <- 3
else if (combined2[i] >= 0.63 && combined2[i] < 0.82) comb2_disc[i] <- 4
else if (combined2[i] >= 0.82 && combined2[i] <= 1) comb2_disc[i] <- 5
else if (combined2[i] > 1) comb2_disc[i] <- 5
}
## regression
diff <- comb1_disc-comb2_disc
good_ass <- length(subset(diff, diff ==0))
bad_ass <- length(subset(diff, diff !=0))
}
### if ISI
if (index == "ISI")
{
for (i in 1:length(combined1))
{
if (combined1[i] < 4.5) comb1_disc[i] <- 1
else if (combined1[i] >= 4.5 && combined1[i] < 6.1) comb1_disc[i] <- 2
else if (combined1[i] >= 6.1 && combined1[i] < 7.5) comb1_disc[i] <- 3
else if (combined1[i] >= 7.5 && combined1[i] < 9.6) comb1_disc[i] <- 4
else if (combined1[i] >= 9.6 && combined1[i] <= 13) comb1_disc[i] <- 5
else if (combined1[i] > 13) comb1_disc[i] <- 5
if (combined2[i] < 4.5) comb2_disc[i] <- 1
else if (combined2[i] >= 4.5 && combined2[i] < 6.1) comb2_disc[i] <- 2
else if (combined2[i] >= 6.1 && combined2[i] < 7.5) comb2_disc[i] <- 3
else if (combined2[i] >= 7.5 && combined2[i] < 9.6) comb2_disc[i] <- 4
else if (combined2[i] >= 9.6 && combined2[i] <= 13) comb2_disc[i] <- 5
else if (combined2[i] > 13) comb2_disc[i] <- 5
}
## regression
diff <- comb1_disc-comb2_disc
good_ass <- length(subset(diff, diff ==0))
bad_ass <- length(subset(diff, diff !=0))
}
if (index == "Shannon")
{
for (i in 1:length(combined1))
{
if (combined1[i] < 0.9) comb1_disc[i] <- 1
else if (combined1[i] >= 0.9 && combined1[i] < 1.9) comb1_disc[i] <- 2
else if (combined1[i] >= 1.9 && combined1[i] < 3) comb1_disc[i] <- 3
else if (combined1[i] >= 3 && combined1[i] < 4.8) comb1_disc[i] <- 4
else if (combined1[i] >= 4.8 && combined1[i] <= 5.7) comb1_disc[i] <- 5
else if (combined1[i] > 5.7) comb1_disc[i] <- 5
if (combined2[i] < 0.9) comb2_disc[i] <- 1
else if (combined2[i] >= 0.9 && combined2[i] < 1.9) comb2_disc[i] <- 2
else if (combined2[i] >= 1.9 && combined2[i] < 3) comb2_disc[i] <- 3
else if (combined2[i] >= 3 && combined2[i] < 4.8) comb2_disc[i] <- 4
else if (combined2[i] >= 4.8 && combined2[i] <= 5.7) comb2_disc[i] <- 5
else if (combined2[i] > 5.7) comb2_disc[i] <- 5
}
## regression
diff <- comb1_disc-comb2_disc
good_ass <- length(subset(diff, diff ==0))
bad_ass <- length(subset(diff, diff !=0))
}
## classif
# diff <- combined1_rf-combined2_rf
# good_ass <- length(subset(diff, diff ==0))
# bad_ass <- length(subset(diff, diff !=0))
##### PLOT
# AMBI
if (index == "AMBI")
{
if(pdf)
{
dir.create(file.path(getwd(), taxo_group), showWarnings = F)
pdf(file = paste(taxo_group, "/", "AMBI.pdf", sep=""), width = 6.6, height=4, useDingbats=F)
} else quartz(width = 6.6, height=4)
#par(mfrow = c(1, 2))
mat <- rbind(c(1,2,3), c(1,2,4))
layout(mat, widths=c(2.5,1,1))
mod <- lm (combined1 ~ combined2)
if (anova(mod)[["Pr(>F)"]][1] >= 0.05) sig <- "ns"
if (anova(mod)[["Pr(>F)"]][1] < 0.05) sig <- "*"
if (anova(mod)[["Pr(>F)"]][1] < 0.01) sig <- "**"
if (anova(mod)[["Pr(>F)"]][1] < 0.001) sig <- "***"
# kappa
kap <- kappa2(cbind(comb1_disc,comb2_disc), "squared", sort.levels=TRUE)
if (is.na(kap$p.value) == T) kap$p.value <- 1
if (kap$p.value >= 0.05) sigk <- "ns"
if (kap$p.value < 0.05) sigk <- "*"
if (kap$p.value < 0.01) sigk <- "**"
if (kap$p.value < 0.001) sigk <- "***"
#col2 <- col[as.integer(as.factor(farm_nam))]
#col <- comp_foram$Col_plot
plot(combined1 ~ combined2, xlim=c(0,6), ylim=c(0,6), pch =col, col= col, xaxs="i",yaxs="i",cex=1, main = paste("AMBI prediction /", algo), xlab = "Morphology", ylab = "Molecular")
arrows(combined2, combined1-combined1_sd, combined2, combined1+combined1_sd, length=0.01, angle=90, code=3, col=col)
abline(mod, col="blue")
rect(0,0,1.2,1.2,border="blue")
rect(1.2,1.2,3.3,3.3,border="green")
rect(3.3,3.3,4.3,4.3,border="yellow")
rect(4.3,4.3,5.5,5.5,border="orange")
rect(5.5,5.5,6,6,border="red")
text(6,0.5, pos=2,paste("R²=", round(summary(mod)$adj.r.squared, 3), sig, sep=""))
text(6,0.2, pos=2,paste("Kappa=", round(kap$value, 3), sigk, sep=""))
# second plot for legend
par(mar=c(0,0,0,0), xpd=TRUE)
plot(0,type="n", axes=F, xlab="", ylab="")
legend("topleft", as.vector(unique(farm_nam)), col= unique(col), pch=unique(col), box.lty=0, inset=c(0,0.11))
par(mar=c(3,1,5,3))
bplt <- barplot(prop.table(table(diff))*100, ylim=c(0,100), xlab="Status mismatch", ylab="Percentage", cex.names = 0.7, cex.axis=0.7,cex.lab=0.5)
text(x= bplt, y= prop.table(table(diff))*100 + 5, labels=paste("n=", table(diff), sep=""), xpd=TRUE, cex=0.7)
par(mar=c(5,1,2,3))
bxplt <- boxplot(combined1 - combined2, cex.names = 0.7, cex.axis=0.7,cex.lab=0.5)
text(x= 0.65, y= bxplt$stats, labels=paste(round(bxplt$stats, 2)), xpd=TRUE, cex=0.7)
points(1, mean(combined1 - combined2), col="red", pch=3)
text(x= 1.35, y= mean(combined1 - combined2), labels=paste(round(mean(combined1 - combined2), 2)), xpd=TRUE, cex=0.7, col="red")
if(pdf) dev.off()
}
# AMBI
if (index == "microAMBI")
{
if(pdf)
{
dir.create(file.path(getwd(), taxo_group), showWarnings = F)
pdf(file = paste(taxo_group, "/", "microAMBI.pdf", sep=""), width = 6.6, height=4, useDingbats=F)
} else quartz(width = 6.6, height=4)
#par(mfrow = c(1, 2))
mat <- rbind(c(1,2,3), c(1,2,4))
layout(mat, widths=c(2.5,1,1))
mod <- lm (combined1 ~ combined2)
if (anova(mod)[["Pr(>F)"]][1] >= 0.05) sig <- "ns"
if (anova(mod)[["Pr(>F)"]][1] < 0.05) sig <- "*"
if (anova(mod)[["Pr(>F)"]][1] < 0.01) sig <- "**"
if (anova(mod)[["Pr(>F)"]][1] < 0.001) sig <- "***"
# kappa
kap <- kappa2(cbind(comb1_disc,comb2_disc), "squared", sort.levels=TRUE)
if (is.na(kap$p.value) == T) kap$p.value <- 1
if (kap$p.value >= 0.05) sigk <- "ns"
if (kap$p.value < 0.05) sigk <- "*"
if (kap$p.value < 0.01) sigk <- "**"
if (kap$p.value < 0.001) sigk <- "***"
#col2 <- col[as.integer(as.factor(farm_nam))]
#col <- comp_foram$Col_plot
plot(combined1 ~ combined2, xlim=c(0,6), ylim=c(0,6), pch =col, col= col, xaxs="i",yaxs="i",cex=1, main = paste("microAMBI prediction /", algo), xlab = "Morphology", ylab = "Molecular")
arrows(combined2, combined1-combined1_sd, combined2, combined1+combined1_sd, length=0.01, angle=90, code=3, col=col)
abline(mod, col="blue")
rect(0,0,1.2,1.2,border="blue")
rect(1.2,1.2,3.3,3.3,border="green")
rect(3.3,3.3,4.3,4.3,border="yellow")
rect(4.3,4.3,5.5,5.5,border="orange")
rect(5.5,5.5,6,6,border="red")
text(6,0.5, pos=2,paste("R²=", round(summary(mod)$adj.r.squared, 3), sig, sep=""))
text(6,0.2, pos=2,paste("Kappa=", round(kap$value, 3), sigk, sep=""))
# second plot for legend
par(mar=c(0,0,0,0), xpd=TRUE)
plot(0,type="n", axes=F, xlab="", ylab="")
legend("topleft", as.vector(unique(farm_nam)), col= unique(col), pch=unique(col), box.lty=0, inset=c(0,0.11))
par(mar=c(3,1,5,3))
bplt <- barplot(prop.table(table(diff))*100, ylim=c(0,100), xlab="Status mismatch", ylab="Percentage", cex.names = 0.7, cex.axis=0.7,cex.lab=0.5)
text(x= bplt, y= prop.table(table(diff))*100 + 5, labels=paste("n=", table(diff), sep=""), xpd=TRUE, cex=0.7)
par(mar=c(5,1,2,3))
bxplt <- boxplot(combined1 - combined2, cex.names = 0.7, cex.axis=0.7,cex.lab=0.5)
text(x= 0.65, y= bxplt$stats, labels=paste(round(bxplt$stats, 2)), xpd=TRUE, cex=0.7)
points(1, mean(combined1 - combined2), col="red", pch=3)
text(x= 1.35, y= mean(combined1 - combined2), labels=paste(round(mean(combined1 - combined2), 2)), xpd=TRUE, cex=0.7, col="red")
if(pdf) dev.off()
}
##NSI
if (index == "NSI")
{
if(pdf)
{
dir.create(file.path(getwd(), taxo_group), showWarnings = F)
pdf(file = paste(taxo_group, "/", "NSI.pdf", sep=""), width = 6.6, height=4, useDingbats=F)
} else quartz(width = 6.6, height=4)
#par(mfrow = c(1, 2))
mat <- rbind(c(1,2,3), c(1,2,4))
layout(mat, widths=c(2.5,1,1))
mod <- lm (combined1 ~ combined2)
if (anova(mod)[["Pr(>F)"]][1] >= 0.05) sig <- "ns"
if (anova(mod)[["Pr(>F)"]][1] < 0.05) sig <- "*"
if (anova(mod)[["Pr(>F)"]][1] < 0.01) sig <- "**"
if (anova(mod)[["Pr(>F)"]][1] < 0.001) sig <- "***"
# kappa
kap <- kappa2(cbind(comb1_disc,comb2_disc), "squared", sort.levels=TRUE)
if (is.na(kap$p.value) == T) kap$p.value <- 1
if (kap$p.value >= 0.05) sigk <- "ns"
if (kap$p.value < 0.05) sigk <- "*"
if (kap$p.value < 0.01) sigk <- "**"
if (kap$p.value < 0.001) sigk <- "***"
plot(combined1 ~ combined2, xlim=c(0,31), ylim=c(0,31), pch =col, col= col, xaxs="i",yaxs="i",cex=1, main = paste("NSI prediction /", algo), xlab = "Morphology", ylab = "Molecular")
arrows(combined2, combined1-combined1_sd, combined2, combined1+combined1_sd, length=0.01, angle=90, code=3, col=col)
abline(mod, col="blue")
rect(0,0,10,10,border="red")
rect(10,10,15,15,border="orange")
rect(15,15,20,20,border="yellow")
rect(20,20,25,25,border="green")
rect(25,25,31,31,border="blue")
text(31,2.6, pos=2,paste("R²=", round(summary(mod)$adj.r.squared, 3), sig, sep=""))
text(31,1, pos=2,paste("Kappa=", round(kap$value, 3), sigk, sep=""))
# second plot for legend
par(mar=c(0,0,0,0), xpd=TRUE)
plot(0,type="n", axes=F, xlab="", ylab="")
legend("topleft", as.vector(unique(farm_nam)), col= unique(col), pch=unique(col), box.lty=0, inset=c(0,0.11))
par(mar=c(3,1,5,3))
bplt <- barplot(prop.table(table(diff))*100, ylim=c(0,100), xlab="Status mismatch", ylab="Percentage", cex.names = 0.7, cex.axis=0.7,cex.lab=0.5)
text(x= bplt, y= prop.table(table(diff))*100 + 5, labels=paste("n=", table(diff), sep=""), xpd=TRUE, cex=0.7)
par(mar=c(5,1,2,3))
bxplt <- boxplot(combined1 - combined2, cex.names = 0.7, cex.axis=0.7,cex.lab=0.5)
text(x= 0.65, y= bxplt$stats, labels=paste(round(bxplt$stats, 2)), xpd=TRUE, cex=0.7)
points(1, mean(combined1 - combined2), col="red", pch=3)
text(x= 1.35, y= mean(combined1 - combined2), labels=paste(round(mean(combined1 - combined2), 2)), xpd=TRUE, cex=0.7, col="red")
if(pdf) dev.off()
}
### NQI1
if (index == "NQI1")
{
if(pdf)
{
dir.create(file.path(getwd(), taxo_group), showWarnings = F)
pdf(file = paste(taxo_group, "/", "NQI1.pdf", sep=""), width = 6.6, height=4, useDingbats=F)
} else quartz(width = 6.6, height=4)
#par(mfrow = c(1, 2))
mat <- rbind(c(1,2,3), c(1,2,4))
layout(mat, widths=c(2.5,1,1))
mod <- lm (combined1 ~ combined2)
if (anova(mod)[["Pr(>F)"]][1] >= 0.05) sig <- "ns"
if (anova(mod)[["Pr(>F)"]][1] < 0.05) sig <- "*"
if (anova(mod)[["Pr(>F)"]][1] < 0.01) sig <- "**"
if (anova(mod)[["Pr(>F)"]][1] < 0.001) sig <- "***"
# kappa
kap <- kappa2(cbind(comb1_disc,comb2_disc), "squared", sort.levels=TRUE)
if (is.na(kap$p.value) == T) kap$p.value <- 1
if (kap$p.value >= 0.05) sigk <- "ns"
if (kap$p.value < 0.05) sigk <- "*"
if (kap$p.value < 0.01) sigk <- "**"
if (kap$p.value < 0.001) sigk <- "***"
plot(combined1 ~ combined2, xlim=c(0,1), ylim=c(0,1), pch =col, col= col, xaxs="i",yaxs="i",cex=1, main = paste("NQI1 prediction /", algo), xlab = "Morphology", ylab = "Molecular")
arrows(combined2, combined1-combined1_sd, combined2, combined1+combined1_sd, length=0.01, angle=90, code=3, col=col)
abline(mod, col="blue")
rect(0,0,.31,.31,border="red")
rect(.31,.31,.49,.49,border="orange")
rect(.49,.49,.63,.63,border="yellow")
rect(.63,.63,.82,.82,border="green")
rect(.82,.82,1,1,border="blue")
text(1,.1, pos=2,paste("R²=", round(summary(mod)$adj.r.squared, 3), sig, sep=""))
text(1,.05, pos=2,paste("Kappa=", round(kap$value, 3), sigk, sep=""))
# second plot for legend
par(mar=c(0,0,0,0), xpd=TRUE)
plot(0,type="n", axes=F, xlab="", ylab="")
legend("topleft", as.vector(unique(farm_nam)), col= unique(col), pch=unique(col), box.lty=0, inset=c(0,0.11))
par(mar=c(3,1,5,3))
bplt <- barplot(prop.table(table(diff))*100, ylim=c(0,100), xlab="Status mismatch", ylab="Percentage", cex.names = 0.7, cex.axis=0.7,cex.lab=0.5)
text(x= bplt, y= prop.table(table(diff))*100 + 5, labels=paste("n=", table(diff), sep=""), xpd=TRUE, cex=0.7)
par(mar=c(5,1,2,3))
bxplt <- boxplot(combined1 - combined2, cex.names = 0.7, cex.axis=0.7,cex.lab=0.5)
text(x= 0.65, y= bxplt$stats, labels=paste(round(bxplt$stats, 2)), xpd=TRUE, cex=0.7)
points(1, mean(combined1 - combined2), col="red", pch=3)
text(x= 1.35, y= mean(combined1 - combined2), labels=paste(round(mean(combined1 - combined2), 2)), xpd=TRUE, cex=0.7, col="red")
if(pdf) dev.off()
}
#### ISI
if (index == "ISI")
{
if(pdf)
{
dir.create(file.path(getwd(), taxo_group), showWarnings = F)
pdf(file = paste(taxo_group, "/", "ISI.pdf", sep=""), width = 6.6, height=4, useDingbats=F)
} else quartz(width = 6.6, height=4)
#par(mfrow = c(1, 2))
mat <- rbind(c(1,2,3), c(1,2,4))
layout(mat, widths=c(2.5,1,1))
mod <- lm (combined1 ~ combined2)
if (anova(mod)[["Pr(>F)"]][1] >= 0.05) sig <- "ns"
if (anova(mod)[["Pr(>F)"]][1] < 0.05) sig <- "*"
if (anova(mod)[["Pr(>F)"]][1] < 0.01) sig <- "**"
if (anova(mod)[["Pr(>F)"]][1] < 0.001) sig <- "***"
# kappa
kap <- kappa2(cbind(comb1_disc,comb2_disc), "squared", sort.levels=TRUE)
if (is.na(kap$p.value) == T) kap$p.value <- 1
if (kap$p.value >= 0.05) sigk <- "ns"
if (kap$p.value < 0.05) sigk <- "*"
if (kap$p.value < 0.01) sigk <- "**"
if (kap$p.value < 0.001) sigk <- "***"
plot(combined1 ~ combined2, xlim=c(0,13), ylim=c(0,13), pch =col, col= col, xaxs="i",yaxs="i",cex=1, main = paste("ISI prediction /", algo), xlab = "Morphology", ylab = "Molecular")
arrows(combined2, combined1-combined1_sd, combined2, combined1+combined1_sd, length=0.01, angle=90, code=3, col=col)
abline(mod, col="blue")
rect(0,0,4.5,4.5,border="red")
rect(4.5,4.5,6.1,6.1,border="orange")
rect(6.1,6.1,7.5,7.5,border="yellow")
rect(7.5,7.5,9.6,9.6,border="green")
rect(9.6,9.6,13,13,border="blue")
text(13,1.1, pos=2,paste("R²=", round(summary(mod)$adj.r.squared, 3), sig, sep=""))
text(13,.4, pos=2,paste("Kappa=", round(kap$value, 3), sigk, sep=""))
# second plot for legend
par(mar=c(0,0,0,0), xpd=TRUE)
plot(0,type="n", axes=F, xlab="", ylab="")
legend("topleft", as.vector(unique(farm_nam)), col= unique(col), pch=unique(col), box.lty=0, inset=c(0,0.11))
par(mar=c(3,1,5,3))
bplt <- barplot(prop.table(table(diff))*100, ylim=c(0,100), xlab="Status mismatch", ylab="Percentage", cex.names = 0.7, cex.axis=0.7,cex.lab=0.5)
text(x= bplt, y= prop.table(table(diff))*100 + 5, labels=paste("n=", table(diff), sep=""), xpd=TRUE, cex=0.7)
par(mar=c(5,1,2,3))
bxplt <- boxplot(combined1 - combined2, cex.names = 0.7, cex.axis=0.7,cex.lab=0.5)
text(x= 0.65, y= bxplt$stats, labels=paste(round(bxplt$stats, 2)), xpd=TRUE, cex=0.7)
points(1, mean(combined1 - combined2), col="red", pch=3)
text(x= 1.35, y= mean(combined1 - combined2), labels=paste(round(mean(combined1 - combined2), 2)), xpd=TRUE, cex=0.7, col="red")
if(pdf) dev.off()
}
#### Shannon
if (index == "Shannon")
{
if(pdf)
{
dir.create(file.path(getwd(), taxo_group), showWarnings = F)
pdf(file = paste(taxo_group, "/", "Shannon.pdf", sep=""), width = 6.6, height=4, useDingbats=F)
} else quartz(width = 6.6, height=4)
#par(mfrow = c(1, 2))
mat <- rbind(c(1,2,3), c(1,2,4))
layout(mat, widths=c(2.5,1,1))
mod <- lm (combined1 ~ combined2)
if (anova(mod)[["Pr(>F)"]][1] >= 0.05) sig <- "ns"
if (anova(mod)[["Pr(>F)"]][1] < 0.05) sig <- "*"
if (anova(mod)[["Pr(>F)"]][1] < 0.01) sig <- "**"
if (anova(mod)[["Pr(>F)"]][1] < 0.001) sig <- "***"
# kappa
kap <- kappa2(cbind(comb1_disc,comb2_disc), "squared", sort.levels=TRUE)
if (is.na(kap$p.value) == T) kap$p.value <- 1
if (kap$p.value >= 0.05) sigk <- "ns"
if (kap$p.value < 0.05) sigk <- "*"
if (kap$p.value < 0.01) sigk <- "**"
if (kap$p.value < 0.001) sigk <- "***"
plot(combined1 ~ combined2, xlim=c(0,5.7), ylim=c(0,5.7), pch =col, col= col, xaxs="i",yaxs="i",cex=1, main = paste("Shannon prediction /", algo), xlab = "Morphology", ylab = "Molecular")
arrows(combined2, combined1-combined1_sd, combined2, combined1+combined1_sd, length=0.01, angle=90, code=3, col=col)
abline(mod, col="blue")
rect(0,0,0.9,0.9,border="red")
rect(0.9,0.9,1.9,1.9,border="orange")
rect(1.9,1.9,3,3,border="yellow")
rect(3,3,4.8,4.8,border="green")
rect(4.8,4.8,5.7,5.7,border="blue")
text(5.7,0.5, pos=2,paste("R²=", round(summary(mod)$adj.r.squared, 3), sig, sep=""))
text(5.7,0.2, pos=2,paste("Kappa=", round(kap$value, 3), sigk, sep=""))
# second plot for legend
par(mar=c(0,0,0,0), xpd=TRUE)
plot(0,type="n", axes=F, xlab="", ylab="")
legend("topleft", as.vector(unique(farm_nam)), col= unique(col), pch=unique(col), box.lty=0, inset=c(0,0.11))
par(mar=c(3,1,5,3))
bplt <- barplot(prop.table(table(diff))*100, ylim=c(0,100), xlab="Status mismatch", ylab="Percentage", cex.names = 0.7, cex.axis=0.7,cex.lab=0.5)
text(x= bplt, y= prop.table(table(diff))*100 + 5, labels=paste("n=", table(diff), sep=""), xpd=TRUE, cex=0.7)
par(mar=c(5,1,2,3))
bxplt <- boxplot(combined1 - combined2, cex.names = 0.7, cex.axis=0.7,cex.lab=0.5)
text(x= 0.65, y= bxplt$stats, labels=paste(round(bxplt$stats, 2)), xpd=TRUE, cex=0.7)
points(1, mean(combined1 - combined2), col="red", pch=3)
text(x= 1.35, y= mean(combined1 - combined2), labels=paste(round(mean(combined1 - combined2), 2)), xpd=TRUE, cex=0.7, col="red")
if(pdf) dev.off()
}
return(list("preds_cont" = combined1, "bi_values"= combined2, "preds_cat" = comb1_disc, "labels" = comb2_disc, "R2" = paste(round(summary(mod)$adj.r.squared, 3), sig, sep=""), "KAP" = paste(round(kap$value, 3), sigk, sep="")))
}