Nothing
## ----setup, include = FALSE---------------------------------------------------
knitr::opts_chunk$set(
fig.align = "center", fig.width = 7, fig.height = 5
)
library(simmr)
## ---- eval = FALSE------------------------------------------------------------
# install.packages("simmr")
# library(simmr)
## ---- message=FALSE, results='hide'-------------------------------------------
# Load in example data
data(geese_data_day1)
# Load into simmr
simmr_in <- with(
geese_data_day1,
simmr_load(
mixtures = mixtures,
source_names = source_names,
source_means = source_means,
source_sds = source_sds,
correction_means = correction_means,
correction_sds = correction_sds,
concentration_means = concentration_means
)
)
# MCMC run
simmr_out <- simmr_mcmc(simmr_in)
## -----------------------------------------------------------------------------
p <- plot(simmr_in,
xlab = expression(paste(delta^13, "C (per mille)", sep = "")),
ylab = expression(paste(delta^15, "N (per mille)", sep = "")),
title = "Isospace plot of Inger et al Geese data",
mix_name = "Organism"
)
## -----------------------------------------------------------------------------
p + xlim(-50, 50) + labs(subtitle = "A subtitle goes here")
## -----------------------------------------------------------------------------
p <- plot(simmr_out,
type = "boxplot",
title = "simmr"
)
## -----------------------------------------------------------------------------
p + ylim(0, 0.5) +
labs(subtitle = "Something else") +
ylab("A new ylab")
## -----------------------------------------------------------------------------
# First extract the dietary proportions
simmr_out2 <- simmr_out$output[[1]]$BUGSoutput$sims.list$p
colnames(simmr_out2) <- simmr_out$input$source_names
# Now turn into a proper data frame
library(reshape2)
df <- reshape2::melt(simmr_out2)
colnames(df) <- c("Num", "Source", "Proportion")
# Finally create the new variable that you want to colour by
df$new_colour <- "Type 2"
df$new_colour[df$Source == "Zostera"] <- "Type 1"
# And create the plot
ggplot(df, aes_string(
y = "Proportion", x = "Source",
fill = "new_colour", alpha = 0, 5
)) +
geom_boxplot(notch = TRUE, outlier.size = 0) +
theme_bw() +
ggtitle("simmr output boxplot with changed colours") +
theme(legend.position = "none") +
coord_flip()
## -----------------------------------------------------------------------------
p <- plot(simmr_in)
## -----------------------------------------------------------------------------
library(ggnewscale)
new_df <- data.frame(
x = geese_data_day1$source_means[, "meand13CPl"] + geese_data_day1$correction_means[, "meand13CPl"],
y = geese_data_day1$source_means[, "meand15NPl"] + geese_data_day1$correction_means[, "meand15NPl"],
Source = "Mixtures"
)
## -----------------------------------------------------------------------------
p +
new_scale_color() +
geom_polygon(data = new_df, aes(x = x, y = y), fill = "orange", alpha = 0.2)
## -----------------------------------------------------------------------------
chull_vals <- chull(new_df[, 1], new_df[, 2])
new_df2 <- new_df[chull_vals, ]
p +
new_scale_color() +
geom_polygon(data = new_df2, aes(x = x, y = y), fill = "orange", alpha = 0.2)
## -----------------------------------------------------------------------------
# Create the new data frame
new_mix <- data.frame(
x = geese_data_day1$mixtures[, "d13C_Pl"],
y = geese_data_day1$mixtures[, "d15N_Pl"],
Source = "Mixtures"
)
# Find the convex hull
chull_mix_vals <- chull(new_mix[, 1], new_mix[, 2])
new_mix2 <- new_mix[chull_mix_vals, ]
# Plot using new_scale_color
p +
new_scale_color() +
geom_polygon(data = new_mix2, aes(x = x, y = y), fill = "purple", alpha = 0.2)
## -----------------------------------------------------------------------------
source_means_c <- geese_data_day1$source_means + geese_data_day1$correction_means
source_sds_c <- sqrt(geese_data_day1$source_sds^2 + geese_data_day1$correction_sds^2)
mix <- geese_data_day1$mixtures
x <- c(
source_means_c[, "meand13CPl"] - source_sds_c[, "SDd13C"],
source_means_c[, "meand13CPl"] - source_sds_c[, "SDd13C"],
source_means_c[, "meand13CPl"] + source_sds_c[, "SDd13C"],
source_means_c[, "meand13CPl"] + source_sds_c[, "SDd13C"]
)
y <- c(
source_means_c[, "meand15NPl"] - source_sds_c[, "SDd15N"],
source_means_c[, "meand15NPl"] + source_sds_c[, "SDd15N"],
source_means_c[, "meand15NPl"] - source_sds_c[, "SDd15N"],
source_means_c[, "meand15NPl"] + source_sds_c[, "SDd15N"]
)
new_df3 <- data.frame(
x = x,
y = y,
Source = "Mixtures"
)
chull_vals <- chull(new_df3[, 1], new_df3[, 2])
new_df4 <- new_df3[chull_vals, ]
p + new_scale_color() +
geom_polygon(data = new_df2, aes(x = x, y = y), fill = "orange", alpha = 0.3) +
new_scale_color() +
geom_polygon(data = new_df4, aes(x = x, y = y), fill = "orange", alpha = 0.1)
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