graph4lg (version 1.8.0)

scatter_dist: Plot scatterplots of genetic distance vs landscape distance

Description

The function enables to plot scatterplots to visualize the relationship between genetic distance (or differentiation) and landscape distance (Euclidean distance, cost-distance, etc.)between populations or sample sites.

Usage

scatter_dist(
  mat_gd,
  mat_ld,
  method = "loess",
  thr_gd = NULL,
  thr_ld = NULL,
  se = TRUE,
  smooth_col = "black",
  pts_col = "#999999"
)

Value

A ggplot2 object to plot

Arguments

mat_gd

A symmetric matrix or dist object with pairwise genetic distances between populations or sample sites.

mat_ld

A symmetric matrix or dist object with pairwise landscape distances between populations or sample sites. These distances can be Euclidean distances, cost-distances or resistance distances, among others.

method

A character string indicating the smoothing method used to fit a line on the scatterplot. Possible values are the same as with function 'geom_smooth()' from ggplot2 : 'lm', 'glm', 'gam', 'loess' (default).

thr_gd

(optional) A numeric or integer value used to remove values from the data before to plot. All genetic distances values above thr_gd are removed from the data.

thr_ld

(optional) A numeric or integer value used to remove values from the data before to plot. All landscape distances values above thr_ld are removed from the data.

se

Logical (optional, default = TRUE) indicating whether the confidence interval around the smooth line is displayed.

smooth_col

(optional) A character string indicating the color used to plot the smoothing line (default: "blue"). It must be a hexadecimal color code or a color used by default in R.

pts_col

(optional) Character string indicating the color used to plot the points (default: "#999999"). It must be a hexadecimal color code or a color used by default in R.

Author

P. Savary

Details

IDs in mat_gd and mat_ld must be the same and refer to the same sampling sites or populations, and both matrices must be ordered in the same way. Matrix of genetic distance mat_gd can be computed using mat_gen_dist. Matrix of landscape distance mat_ld can be computed using mat_geo_dist when the landscape distance needed is a Euclidean geographical distance.

Examples

data(data_tuto)
mat_dps <- data_tuto[[1]]
mat_dist <- suppressWarnings(mat_geo_dist(data = pts_pop_simul,
      ID = "ID",
      x = "x",
      y = "y"))
mat_dist <- mat_dist[order(as.character(row.names(mat_dist))),
                      order(as.character(colnames(mat_dist)))]
scatterplot_ex <- scatter_dist(mat_gd = mat_dps,
                              mat_ld = mat_dist)