# R/LCC_Histogram.R In NetSci: Calculates Basic Network Measures Commonly Used in Network Medicine

#### Documented in Histogram_LCC

```#' @title Histogram_LCC
#' @description Plots the histogram to evaluate the significance of the Largest Connected Component (LCC).
#' @param LCC_L an output from the function LCC_Significance or LCC_Bipartide
#' @param Name title of the plot
#' @importFrom graphics abline hist title axis
#' @importFrom stats ecdf sd
#' @return An Histogram of the simulated LCC, and a red line of the actual LCC.
#' @export
#'
#' @examples
#' set.seed(666)
#' net  = data.frame(
#' Node.1 = sample(LETTERS[1:15], 15, replace = TRUE),
#' Node.2 = sample(LETTERS[1:10], 15, replace = TRUE))
#' net\$value = 1
#' net =  CoDiNA::OrderNames(net)
#' net = unique(net)
#'
#' g <- igraph::graph_from_data_frame(net, directed = FALSE )
#' targets = c("N", "A", "I", "F")
#' LCC_Out = LCC_Significance(N = 1000,
#'                  Targets = targets,
#'                                   G = g,
#'                                   bins = 5,
#'                                   min_per_bin = 2)
#'                                   # in a real interactome, please use the default
#'
#' Histogram_LCC(LCC_Out, "Example")

Histogram_LCC = function(LCC_L, Name = NULL){

lim = c(LCC_L\$LCC, LCC_L\$LCCZ)
LCC_L[[1]]%>%
hist(., las = 1,
main = "",
xlim = c(min(lim -10), max(lim + 10)),
col = 'gray75', ylab = "", axes = FALSE)
abline(v = LCC_L\$LCC, col = "red")
axis(1, las = 1)
axis(2, las = 1)
title(main = Name, sub = paste0("LCC: ",
round(LCC_L\$LCC,0),
" (",
round(LCC_L\$mean,2),
" +- ",
round(LCC_L\$sd,2),"; ",
"p: " ,round(LCC_L\$emp_p,4),
")"))

}
```

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NetSci documentation built on Dec. 11, 2021, 9:21 a.m.