Creates heatmap visualizations for attribute mixing matrices from mixing_matrix().
The function creates a tile plot showing how different attribute categories interact in the network.
Usage
plot_mixing_matrix(
mixing_results,
which_matrix = 1,
show_values = TRUE,
value_digits = 2,
color_scale = c("#F18F01", "white", "#2E86AB"),
midpoint = NULL,
text_size = 4,
text_color = "black",
text_color_threshold = NULL,
tile_border_color = "white",
tile_border_size = 0.5,
reorder_categories = FALSE,
diagonal_emphasis = TRUE,
...
)Arguments
- mixing_results
Output from
mixing_matrix()containing mixing matrices and summary statistics.- which_matrix
Integer or character. Which matrix to plot if multiple are present. Default is 1 (first matrix).
- show_values
Logical. Whether to display values in each tile. Default TRUE.
- value_digits
Integer. Number of decimal places for displayed values. Default 2.
- color_scale
Character vector of three colors for low, mid, and high values. Default uses a diverging color scale.
- midpoint
Numeric. The midpoint for the diverging color scale. Default NULL automatically calculates based on data range.
- text_size
Numeric. Size of value labels in tiles. Default 4.
- text_color
Character. Color of text labels. Default "black".
- text_color_threshold
Numeric. If provided, values above this threshold (0-1 scale) will use white text, values below will use black text. Default NULL uses text_color for all.
- tile_border_color
Character. Color of tile borders. Default "white".
- tile_border_size
Numeric. Width of tile borders. Default 0.5.
- reorder_categories
Logical. Whether to reorder categories by similarity. Default FALSE.
- diagonal_emphasis
Logical. Whether to emphasize diagonal cells (within-group mixing). Default TRUE.
- ...
Additional arguments passed to ggplot2 functions.
Examples
if (FALSE) { # \dontrun{
# Create a network with categorical attributes
data(icews)
icews_10 <- icews[icews$year == 2010, ]
net <- netify(
icews_10,
actor1 = "i", actor2 = "j",
symmetric = FALSE,
weight = "verbCoop"
)
# Run mixing matrix analysis
mixing_result <- mixing_matrix(
net,
attribute = "i_polity2_cat"
)
# Create visualization
plot_mixing_matrix(mixing_result)
} # }