mat_coursegrain: Course grain a matrix for plotting

In FredHasselman/casnet: A Toolbox for Studying Complex Adaptive Systems and Networks

Course grain a matrix for plotting

Description

Course grain a matrix for plotting

Usage

mat_coursegrain(
  RM,
  target_height = round(NROW(RM)/2),
  target_width = round(NCOL(RM)/2),
  summary_func = NA,
  recurrence_threshold = NA,
  categorical = NA,
  output_type = 0,
  n_core = 1,
  silent = FALSE
)

Arguments

RM	A (recurrence) matrix
target_height	How many rows? (default = NROW(RM)/2)
target_width	How many columns? (default = NCOL(RM)/2)
summary_func	How to summarise the values in subset X of RM. If set to NA, the function will try to pick a summary function based on the cell values: If RM is a distance matrix, mean(X, na.rm = TRUE) will be used; If it is a binary matrix ifelse(mean(X, na.rm = TRUE)>recurrence_threshold,1,0), a categorical matrix (categorical = TRUE, or, matrix attribute chromatic = TRUE) will pick the most frequent category in the subset attributes(ftable(X))$col.vars$x[[which.max(ftable(X))]]. (default = NA)
recurrence_threshold	For a binary matrix the mean of the cells to be summarised will vary between 0 and 1, which essentially represents the recurrence rate for that subset of the matrix. If NA the threshold will be set to a value that in most cases should return a plot with a similar RR as the original plot. (default = NA)
categorical	If set to TRUE, will force summary_func to select the most frequent value. If NA the matrix attribute chromatic will be used. If chromatic is not present, all values in the matrix have to be whole numbers as determined by plyr::is.discrete(). (default = NA)
output_type	The output format for plyr::vapply(). (default = 0.0)
n_core	Number of cores for parallel processing. Set to NA to automatically choose cores. (default = 1)
silent	Silt-ish mode (default = FALSE)

Value

A coursegrained matrix of size target_width by target_height.

Note

This code was inspired by code published in a blog post by Guillaume Devailly on 29-04-2020 (https://gdevailly.netlify.app/post/plotting-big-matrices-in-r/)

Examples

# Continuous
RMc1 <- rp(cumsum(rnorm(200)))
rp_plot(RMc1)
RMc2 <- mat_coursegrain(RMc1)
rp_plot(RMc2)

# Binary
RMb1 <- rp(cumsum(rnorm(200)), emRad = NA)
rp_plot(RMb1, plotMeasures = TRUE)
# Reported RQA measures in rp_plot will be based on the full matrix
rp_plot(RMb1, maxSize = 100^2, plotMeasures = TRUE)
# Plotting the coursegrained matrix itself will yield different values
RMb2 <- mat_coursegrain(RMb1)
rp_plot(RMb2, plotMeasures = TRUE)

# Categorical
RMl1 <- rp(y1 = round(runif(100, min = 1, max = 3)), chromatic = TRUE)
rp_plot(RMl1)
RMl2 <- mat_coursegrain(RMl1, categorical = TRUE)
rp_plot(RMl2)

FredHasselman/casnet documentation built on April 20, 2024, 3:05 p.m.