In Ecological-Complexity-Lab/infomap_ecology_package: Community Detection using Infomap, Inspired by Ecological Networks

# Use this to add a copy-code buttong
klippy::klippy(position = c('top', 'right'), color = 'darkred')

knitr::opts_chunk$set(echo = TRUE)

1. Built in randomization capabilities for bipartite networks

The function run_infomap_monolayer can use shuffling algorithms built into vegan. To use this, we need to set signif=T and provide a shuffling method to shuff_method. The shuffling methods are the ones detailed in ?vegan::commsim.

network_object <- create_monolayer_network(memmott1999, bipartite = T, directed = F, group_names = c('A','P'))

# Run with shuffling
infomap_object <- run_infomap_monolayer(network_object, infomap_executable='Infomap',
                                        flow_model = 'undirected',
                                        silent=T, trials=20, two_level=T, seed=123, 
                                        signif = T, shuff_method = 'r00', nsim = 10) #nsim = 50
# Plot histograms
plots <- plot_signif(infomap_object, plotit = T)
plot_grid(
  plots$L_plot+
    theme_bw()+
    theme(legend.position='none', 
          legend.title = element_text(size=16),
          axis.text = element_text(size=16)),
  plots$m_plot+
    theme_bw()+
    theme(legend.position='none', 
          legend.title = element_text(size=16),
          axis.text = element_text(size=16))
)

Another way is to provide shuff_method with a list containing already shuffled networks. You can for example use the function shuffle_infomap to produce shuffled networks with vegan. This is necessary on the shuffling algorithm from ?vegan::commsim needs additional arguments such as burning.

# Or can shuffle like this, if additional arguments are needed for the shuffling algorithm
shuffled <- shuffle_infomap(network_object, shuff_method='curveball', nsim=10, burnin=2000) #nsim=50
infomap_object <- run_infomap_monolayer(network_object, infomap_executable='Infomap',
                                        flow_model = 'undirected',
                                        silent=T, trials=20, two_level=T, seed=123, 
                                        signif = T, shuff_method = shuffled, nsim = 10) #nsim = 50
plots <- plot_signif(infomap_object, plotit = F)
plot_grid(
  plots$L_plot+
  theme_bw()+
  theme(legend.position='none', 
        axis.text = element_text(size=16), #20
        legend.title = element_text(size=16),
        axis.title = element_text(size=16)), #20
  plots$m_plot+
    theme_bw()+
    theme(legend.position='none', 
          axis.text = element_text(size=16), #20
          legend.title = element_text(size=16),
          axis.title = element_text(size=16)) #20
)

2. Dedicated randomization algorithm

You can also create your own shuffling algorithm and a list of shuffled link lists. As in this example with the Tur et al. 2016 data set..

# Import data
data(tur2016)
tur2016_altitude2000 <- tur2016 %>% 
  filter(altitude==2000) %>% 
  select("donor", "receptor", "total") %>% 
  group_by(donor, receptor) %>% 
  summarise(n=mean(total)) %>% 
  rename(from = donor, to = receptor, weight = n) %>% 
  ungroup() %>%
  slice(c(-10,-13,-28)) %>%  # Remove singletons
  filter(from!=to) # Remove self loops

tur_network <- create_monolayer_network(tur2016_altitude2000, directed = T, bipartite = F)

# A dedicated function to shuffle the networks, considering the flow.
shuffle_tur_networks <- function(x){ # x is a network object
  m <- x$mat

  # Assign off-diagona values
  off_diagonal_lower <- m[lower.tri(m, diag = FALSE)]
  off_diagonal_upper <- m[upper.tri(m, diag = FALSE)]
  out <- matrix(0, nrow(m), ncol(m), dimnames = list(rownames(m), colnames(m)))
  out[lower.tri(out, diag = FALSE)] <- sample(off_diagonal_lower, size = length(off_diagonal_lower), replace = F)
  out[upper.tri(out, diag = FALSE)] <- sample(off_diagonal_upper, size = length(off_diagonal_upper), replace = F)

  # Re-assign the diagonal (left intact)
  diag(out) <- diag(m)


  # Sanity checks
  t1 <- setequal(out[upper.tri(out, diag = FALSE)], m[upper.tri(m, diag = FALSE)]) #Should be TRUE
  t2 <- setequal(out[lower.tri(out, diag = FALSE)], m[lower.tri(m, diag = FALSE)]) #Should be TRUE
  t3 <- identical(out[upper.tri(out, diag = FALSE)], m[upper.tri(m, diag = FALSE)]) #Should be FALSE
  t4 <- identical(out[lower.tri(out, diag = FALSE)], m[lower.tri(m, diag = FALSE)]) #Should be FALSE
  t5 <- all(table(m)==table(out))# Should be TRUE because it includes all the values, including diagonal
  if (any(c(t1,t2,!t3,!t4,t5)==F)){stop('One or more sanity checks failed')}
  return(out)
}

# Create a list with shuffled link lists
nsim <- 100 #nsim <- 1000
shuffled <- NULL
for (i in 1:nsim){
  print(i)
  x <- shuffle_tur_networks(tur_network) #Shuffle the network
  x <- create_monolayer_network(x,directed = T,bipartite = F) # Create a monolayer object
  shuffled[[i]] <- create_infomap_linklist(x)$edge_list_infomap #Create a link-list
}  

# Use the shuffled link lists. 
tur_signif <- run_infomap_monolayer(tur_network, infomap_executable='Infomap',
                      flow_model = 'rawdir',
                      silent=T,
                      trials=100, two_level=T, seed=200952,
                      signif = T, shuff_method = shuffled)

print(tur_signif$pvalue)

plots <- plot_signif(tur_signif, plotit = F)

plots

Ecological-Complexity-Lab/infomap_ecology_package documentation built on June 6, 2024, 5:28 a.m.