R4eco - Eco-Analysis Made Easy in R

R4eco is a small R package designed to simplify ecological analyses by providing a range of functions for diverse tasks. While the package is continuously expanding, it currently includes functions for Renkonen's similarity index (S) calculation, bootstrap implementation, and convenient plotting of UPGMA dendrograms.

Installation

To install R4eco, use the following code:

# Install R4eco
remotes::install_github("wilsonfrantine/R4eco")

# Load the package
library(R4eco)

Some functionalities:

1. Plot Linear Mixed Models (lme4)

# Plot linear prediction
lmerPredictionPlot(model = modelX)

modelX is a lme4 model object. If you have any, take a look a ?lmerPredictionPlot

Example Data and Model

d <- data.frame(
  Type = rep(c("Forest", "Regeneration", "Restoration"), each = 12),
  Landscape = rep(paste0("L", 1:12), times = 3),
  Mean_NDVI_SD_500 = rnorm(36, mean = 0.2, sd = 0.02),
  hill_q0 = sample(5:14, 36, replace = TRUE),
  Abundance = sample(5:50, 36, replace = TRUE)
)

modelX <- lme4::lmer(formula = hill_q0 ~ Mean_NDVI_SD_500 * Type + (1|Landscape), data = d)

Some customization tips

You can edit the output of the function as any ggplot2 graph. Find below some suggestions

# Customizing plot with color and fill scales
lmerPredictionPlot(model = modelX) +
  scico::scale_color_scico_d(palette = "batlow", begin = 0.1, end = 0.7) +
  scico::scale_fill_scico_d(palette = "batlow", begin = 0.1, end = 0.7)

# Adding facet_wrap to the plot
lmerPredictionPlot(model = modelX) +
  scico::scale_color_scico_d(palette = "batlow", begin = 0.1, end = 0.7) +
  scico::scale_fill_scico_d(palette = "batlow", begin = 0.1, end = 0.7) +
  ggplot2::facet_wrap(~Type)

2. Renkonen Similarity Calculation

To begin, you can generate sample data using the simulate_data() function: simulate_data().

# Generate sample data
simulate_data()

Calculate Renkonen similarity with or without bootstrapping:

# Calculate Renkonen similarity
df <- simulate_data(5, 9)
S <- renkonen(df)
head(S)

# Calculate Renkonen similarity with bootstrap
df <- simulate_data()
S <- renkonen(df, boot = 1000)
head(S)

3. UPGMA Dendrogram Plotting

Visualize Renkonen similarity as a UPGMA dendrogram:

# Plot UPGMA dendrogram
df <- simulate_data()
S <- renkonen(df, boot = 1000)
trees <- upgma(S)
plot_upgma(trees)

Consensus Dendrogram

Compute a consensus dendrogram for saving as Newick format or using in other software:

# Compute consensus dendrogram
df <- simulate_data()
S <- renkonen(df, boot = 1000)
trees <- upgma(S)
tree_consensus <- tree_consensus(trees, type = "mcc")

4. GLM and LM surface plots

ggsurface can handle some glm and lm models to generate 2D surface plots:

data(mtcars)
m <- glm(mpg ~ wt + hp, data=mtcars, family = "gaussian")
ggsurface(m, x.var = "wt", y.var = "hp",
   legend.title = "milles per galon", high.col = "darkred",
   round.legend = 0)

Contact

For assistance or inquiries, please reach out to: Wilson Frantine-Silva wilsonfrantine@gmail.com

Enjoy using R4eco to enhance your ecological analyses!