Description Usage Arguments Value Examples

An interactive tool to assist in the visualization of the results from INDEED functions patial_corr() or non_partial_corr(). The size and the color of each node can be adjusted by users to represent either the Node_Degree, Activity_Score, Z_Score, or P_Value. The color of the edge is based on the binary value of either 1 corresonding to a positive correlation dipicted as green or a negative correlation of -1 dipicted as red. The user also has the option of having the width of each edge be proportional to its weight value. The layout of the network can also be customized by choosing from the options: 'nice', 'sphere', 'grid', 'star', and circle'. Nodes can be moved and zoomed in on. Each node and edge will display extra information when clicked on. Secondary interactions will be highlighted as well when a node is clicked on.

1 2 | ```
networkVis(results = NULL, nodesize = "Node_Degree",
nodecolor = "Activity_Score", edgewidth = "NO", layout = "nice")
``` |

`results` |
This is the result from calling either partial_corr() or non_partial_corr() |

`nodesize` |
This parameter determines what the size of each node will represent. The options are 'Node_Degree', 'Activity_Score', 'Z_Score', and 'P_Value'. The title of the resulting network will identify which parameter was selected to represent the node size. The default is Node_Degree |

`nodecolor` |
This parameter determines what color each node will be based on a yellow to blue color gradient. The options are 'Node_Degree', 'Activity_Score', 'Z_Score', and 'P_Value'. A color bar will be created based on which parameter is chosen. |

`edgewidth` |
This is a 'YES' or 'NO' option as to if the edgewidth should be representative of the weight value corresponding to the correlation between two nodes. |

`layout` |
User can choose from a a handful of network visualization templates including:'nice', 'sphere', 'grid', 'star', and circle'. |

An interactive dipiction of the network resulting from INDEED functions patial_corr() or non_partial_corr()

1 2 3 4 | ```
result1 = non_partial_cor(data=Met_GU,class_label = Met_Group_GU,
id=Met_name_GU,method="spearman",permutation_thres = 0.05, permutation = 1000)
networkVis(results = result1, layout= 'nice', nodesize= 'Node_Degree',
nodecolor= 'Activity_Score', edgewidth= 'NO')
``` |

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