Description Usage Arguments Value Examples

View source: R/singleNode.getNodeRanksMovie.r

Make a movie of the fixed, single-node walk the diffusion probability method makes in search of a given patient's perturbed variables.

1 2 | ```
singleNode.getNodeRanksMovie(subset.nodes, ig, output_filepath,
num.misses = NULL, zoomIn = FALSE)
``` |

`subset.nodes` |
- The subset of variables, S, in a background graph, G. |

`ig` |
- The igraph object associated with the background knowledge graph. |

`output_filepath` |
- The local directory at which you want still images to be saved. |

`zoomIn` |
- Boolean. Delete nodes outside of node subset's order 1 neighborhood?. Default is FALSE. |

`movie` |
- If you want to make a movie, set to TRUE. This will produce a set of still images that you can stream together to make a movie. Default is TRUE. Alternatively (movie=FALSE), you could use this function to get the node labels returned for each node ranking starting with a perturbed variable. |

ranksByStartNode - a list object of node rankings Each element is based on a different startNode. Images are also generated in the output_directory specified.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 | ```
# Read in any network via its adjacency matrix
tmp = matrix(1, nrow=100, ncol=100)
for (i in 1:100) {
for (j in 1:100) {
tmp[i, j] = rnorm(1, mean=0, sd=1)
}
}
colnames(tmp) = sprintf("Compound%d", 1:100)
ig = graph.adjacency(tmp, mode="undirected", weighted=TRUE, add.colnames="name")
V(ig)$name = tolower(V(ig)$name)
adjacency_matrix = list(tmp) # MUST BE GLOBAL VARIABLE
# Set other tuning parameters
p0=0.1 # 10% of probability distributed uniformly
p1=0.9 # 90% of probability diffused based on edge weights in networks
thresholdDiff=0.01
G = vector(mode="list", length=length(V(ig)$name))
names(G) = V(ig)$name
subset.nodes = names(G)[sample(1:length(G), 3)]
singleNode.getNodeRanksMovie(subset.nodes, ig, output_filepath = getwd())
``` |

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