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)
#> Continuous matrix... using summary function 'mean(x, na.rm = TRUE)' for coursegraining.
rp_plot(RMc2)


# Binary
RMb1 <- rp(cumsum(rnorm(200)), emRad = NA)
rp_plot(RMb1, plotMeasures = TRUE)
#> Auto-RQA, not including diagonal, theiler set to 1...

# Reported RQA measures in rp_plot will be based on the full matrix
rp_plot(RMb1, maxSize = 100^2, plotMeasures = TRUE)
#> Auto-RQA, not including diagonal, theiler set to 1...
#> NOTE: To speed up the plotting process, the RP will represent a coursegrained matrix. Set argument 'courseGrain = FALSE' to see the full matrix.
#> Auto-RQA, not including diagonal, theiler set to 1...
#> Binary matrix... using summary function 'ifelse(mean(x, na.rm = TRUE)>recurrence_threshold,1,0)' for coursegraining.

# Plotting the coursegrained matrix itself will yield different values
RMb2 <- mat_coursegrain(RMb1)
#> Auto-RQA, not including diagonal, theiler set to 1...
#> Binary matrix... using summary function 'ifelse(mean(x, na.rm = TRUE)>recurrence_threshold,1,0)' for coursegraining.
rp_plot(RMb2, plotMeasures = TRUE)
#> Auto-RQA, not including diagonal, theiler set to 1...


# Categorical
RMl1 <- rp(y1 = round(runif(100, min = 1, max = 3)), chromatic = TRUE)
rp_plot(RMl1)
#> Warning: Multiple components found; returning the first one. To return all, use `return_all = TRUE`.

RMl2 <- mat_coursegrain(RMl1, categorical = TRUE)
#> Categorical matrix... using summary function 'attributes(ftable(x))$col.vars[[which.max(ftable(x))]]' for coursegraining.
rp_plot(RMl2)
#> Warning: Multiple components found; returning the first one. To return all, use `return_all = TRUE`.