In mdlama/milkweed:

library(milkweed)
library(dplyr)
library(ggplot2)
ipm <- mwIPM()
fit <- ipm$pars$budlings.per.stem.fit$fit

Budlings-per-stem

Let's start with just the data points.

merged <- fit$merged

plotdata <- data.frame(herb_avg = merged$herb_mean, 
                       buds_per_stem = merged$bdlgs_per_stem,
                       site = merged$site)

p <- plotdata %>% ggplot(aes(x = herb_avg, 
                             y = buds_per_stem,
                             color = site)) + 
                  geom_point()

p

Let's add the fit.

attach(ipm$pars)
xpts <- seq(from=0, to=2, length.out = 1000)
ypts <- predict(budlings.per.stem.fit$fit, 
                new = data.frame(herb_mean = xpts), 
                level=0.95,
                interval="confidence",
                se.fit=TRUE)
plotdata <- data.frame(herb_avg = xpts, 
                       buds_per_stem = ypts$fit[,"fit"],
                       lwr = ypts$fit[,"lwr"],
                       upr = ypts$fit[,"upr"],
                       site = NA)

p <- p + geom_line(data = plotdata, 
                   color = "black", 
                   linetype = "solid") +
         geom_ribbon(data = plotdata, 
                     aes(ymin=lwr, 
                         ymax=upr), 
                     alpha = 0.2) + 
         scale_y_continuous(limits = c(0, 2.8))

p <- p + theme_bw() +
         xlab("Herbivory severity") +
         ylab("Per capita clonal reproduction\n (sprouts/stem)") +
         labs(color="Sites") + 
         scale_x_continuous(limits = c(0.0, 2.0)) + 
         theme(legend.background = element_rect(fill="lightgrey",
                                                size=0.1,
                                                linetype="solid"),
               legend.key.size =  unit(0.2, "in"),
               legend.position = c(0.875, 0.75))

p
detach(ipm$pars)

Save figure.

ggsave("Figure4_ClonalGrowthVsHerb.png", height=4, width=6, device = "png", p)

mdlama/milkweed documentation built on Sept. 15, 2022, 9:24 a.m.