R plot 去除虛線

Question

我試圖按照本教程 ( https://popgen.nescent.org/2018-03-27_RDA_GEA.html ) 和 plot RDA，但我想刪除兩條虛線（x=0 和 y=0） . 有誰知道如何擺脫它們？

這是我正在談論的圖表

Answer 1

根據這篇文章，如果您自己從頭開始構建 plot，則可以更改plot.rda()以刪除虛線，但這是一項復雜/具有挑戰性的任務。 在我看來，最簡單/最好的解決方案是用abline(h = 0, v = 0, col = "white", lwd = 2)在虛線上繪制白線，然后在 plot 之前用box()重繪 plot 邊框點/線。 有關示例，請參見下面的## PLOTTING ##部分：

## OBTAIN & LOAD THE DATA ##
#install.packages(c("psych","vegan"), dependencies=TRUE)
library(psych)    # Used to investigate correlations among predictors
library(vegan)    # Used to run RDA
temp <- tempfile()
download.file("https://github.com/NESCent/popgenInfo/blob/master/data/wolf_geno_samp_10000.zip?raw=true",
              temp)
gen <- read.csv(unzip(temp, "wolf_geno_samp_10000.csv"), row.names=1)
dim(gen)
sum(is.na(gen))
gen.imp <- apply(gen,
                 2,
                 function(x) replace(x,
                 is.na(x),
                 as.numeric(names(which.max(table(x))))))
sum(is.na(gen.imp)) # No NAs
env <- read.csv(url("https://raw.githubusercontent.com/NESCent/popgenInfo/master/data/wolf_env.csv"))
str(env)
env$individual <- as.character(env$individual)
env$land_cover <- as.factor(env$land_cover)
identical(rownames(gen.imp), env[,1]) 
pairs.panels(env[,5:16], scale=T)
pred <- subset(env, select=-c(precip_coldest_quarter, max_temp_warmest_month, min_temp_coldest_month))
table(pred$land_cover)
pred <- subset(pred, select=-c(land_cover))
pred <- pred[,5:12]
colnames(pred) <- c("AMT","MDR","sdT","AP","cvP","NDVI","Elev","Tree")
pairs.panels(pred, scale=T)
wolf.rda <- rda(gen.imp ~ ., data=pred, scale=T)
wolf.rda
RsquareAdj(wolf.rda)
summary(eigenvals(wolf.rda, model = "constrained"))
screeplot(wolf.rda)
signif.full <- anova.cca(wolf.rda, parallel=getOption("mc.cores"))
signif.full
signif.axis <- anova.cca(wolf.rda, by="axis", parallel=getOption("mc.cores"))
signif.axis
vif.cca(wolf.rda)
plot(wolf.rda, scaling=3)
plot(wolf.rda, choices = c(1, 3), scaling=3)
levels(env$ecotype) <- c("Western Forest","Boreal Forest","Arctic","High Arctic","British Columbia","Atlantic Forest")
eco <- env$ecotype
bg <- c("#ff7f00","#1f78b4","#ffff33","#a6cee3","#33a02c","#e31a1c")

## PLOTTING ##
plot(wolf.rda, type="n", scaling=3)
abline(h = 0, v = 0, col = "white", lwd = 2)
box()
points(wolf.rda, display="species", pch=20, cex=0.7, col="gray32", scaling=3)           # the SNPs
points(wolf.rda, display="sites", pch=21, cex=1.3, col="gray32", scaling=3, bg=bg[eco]) # the wolves
text(wolf.rda, scaling=3, display="bp", col="#0868ac", cex=1)                           # the predictors
legend("bottomright", legend=levels(eco), bty="n", col="gray32", pch=21, cex=1, pt.bg=bg)

plot(wolf.rda, type="n", scaling=3, choices=c(1,3))
abline(h = 0, v = 0, col = "white", lwd = 2)
box()
points(wolf.rda, display="species", pch=20, cex=0.7, col="gray32", scaling=3, choices=c(1,3))
points(wolf.rda, display="sites", pch=21, cex=1.3, col="gray32", scaling=3, bg=bg[eco], choices=c(1,3))
text(wolf.rda, scaling=3, display="bp", col="#0868ac", cex=1, choices=c(1,3))
legend("topleft", legend=levels(eco), bty="n", col="gray32", pch=21, cex=1, pt.bg=bg)

此外，在未來，如果您可以發布獲取和加載數據所需的代碼或一個最小的、可重現的示例，這將使這個問題更容易回答； 請參閱如何制作出色的 R 可重現示例