ggplot_gaussian_2D: Plot a 2D-Gaussian via ggplot

In gaussplotR: Fit, Predict and Plot 2D Gaussians

Description

Plot a 2D-Gaussian via ggplot

Usage

ggplot_gaussian_2D(
  gauss_data,
  normalize = TRUE,
  contour_thickness = 0.04,
  contour_color = "black",
  bins = 15,
  viridis_dir = 1,
  viridis_opt = "B",
  x_lab = "X values",
  y_lab = "Y values",
  axis.text = element_text(size = 6),
  axis.title = element_text(size = 7),
  axis.ticks = element_line(size = 0.3),
  plot.margin = unit(c(0.1, 0.1, 0.1, 0.1), "cm"),
  ...
)

Arguments

gauss_data	Data.frame with X_values, Y_values, and predicted_values, e.g. exported from predict_gaussian_2D()
normalize	Default TRUE, should predicted_values be normalized on a 0 to 1 scale?
contour_thickness	Thickness of contour lines
contour_color	Color of the contour lines
bins	Number of bins for the contour plot
viridis_dir	See "direction" in scale_fill_viridis_c()
viridis_opt	See "option" in scale_fill_viridis_c()
x_lab	Arguments passed to xlab()
y_lab	Arguments passed to ylab()
axis.text	Arguments passed to axis.text
axis.title	Arguments passed to axis.title
axis.ticks	Arguments passed to axis.ticks
plot.margin	Arguments passed to plot.margin
...	Other arguments supplied to ggplot2::theme()

Value

A ggplot object that uses metR::geom_contour_fill() to display the 2D-Gaussian

Author(s)

Vikram B. Baliga

Examples

if (interactive()) {
  ## Load the sample data set
  data(gaussplot_sample_data)

  ## The raw data we'd like to use are in columns 1:3
  samp_dat <-
    gaussplot_sample_data[,1:3]


  #### Example 1: Unconstrained elliptical ####
  ## This fits an unconstrained elliptical by default
  gauss_fit <-
    fit_gaussian_2D(samp_dat)

  ## Generate a grid of x- and y- values on which to predict
  grid <-
    expand.grid(X_values = seq(from = -5, to = 0, by = 0.1),
                Y_values = seq(from = -1, to = 4, by = 0.1))

  ## Predict the values using predict_gaussian_2D
  gauss_data <-
    predict_gaussian_2D(
      fit_object = gauss_fit,
      X_values = grid$X_values,
      Y_values = grid$Y_values,
    )

  ## Plot via ggplot2 and metR
  library(ggplot2); library(metR)
  ggplot_gaussian_2D(gauss_data)

  ## Produce a 3D plot via rgl
  rgl_gaussian_2D(gauss_data)


  #### Example 2: Constrained elliptical_log ####
  ## This fits a constrained elliptical, as in Priebe et al. 2003
  gauss_fit <-
    fit_gaussian_2D(
      samp_dat,
      method = "elliptical_log",
      constrain_orientation = -1
    )

  ## Generate a grid of x- and y- values on which to predict
  grid <-
    expand.grid(X_values = seq(from = -5, to = 0, by = 0.1),
                Y_values = seq(from = -1, to = 4, by = 0.1))

  ## Predict the values using predict_gaussian_2D
  gauss_data <-
    predict_gaussian_2D(
      fit_object = gauss_fit,
      X_values = grid$X_values,
      Y_values = grid$Y_values,
    )

  ## Plot via ggplot2 and metR
  ggplot_gaussian_2D(gauss_data)

  ## Produce a 3D plot via rgl
  rgl_gaussian_2D(gauss_data)
}

Example output

gaussplotR documentation built on May 3, 2021, 1:07 a.m.