View source: R/connectivitysimilarity.R
connectivity_similarity | R Documentation |
Calculate a similarity score between connectivity matrices that penalises small differences between low and does not heavily penalise large differences between high weights. Algorithm from Jarrell et al. 2012.
connectivity_similarity(x, y, c1 = 0.5, c2 = 0.18, normalise = TRUE)
## S3 method for class 'numeric'
connectivity_similarity(x, y, c1 = 0.5, c2 = 0.18, normalise = TRUE)
## S3 method for class 'matrix'
connectivity_similarity(x, y, c1 = 0.5, c2 = 0.18, normalise = TRUE)
connectivity_similarity_distance(
m,
c1 = 0.5,
c2 = 0.18,
normalise = FALSE,
diag = FALSE,
upper = FALSE
)
connectivity_similarity_matrix(
m,
c1 = 0.5,
c2 = 0.18,
normalise = FALSE,
diag = FALSE,
upper = FALSE
)
x |
a vector/matrix of connectivities, where each entry in the vector or each column in the matrix is a different target/input neuron/cell type |
y |
a different vector/matrix of connectivities |
c1 |
determines how negatively we want to punish a case such as the one above. Default C1 is chosen so that 1 and 5 are weakly dissimilar. |
c2 |
determines the point where the similarity of the two numbers switches from negative to positive. Default C2 is chosen so that 10 and 100 synapses are weakly similar. |
normalise |
perform a min-max normalisation on the similarity scores as in Schlegel et al. 2015 |
m |
an n x m adjacency matrix |
diag |
for connectivity_similarity_distance. Logical value indicating whether the diagonal of the distance matrix should be printed by print.dist. |
upper |
for connectivity_similarity_distance. Logical value indicating whether the upper triangle of the distance matrix should be printed by print.dist. |
Jarrell TA, Wang Y, Bloniarz AE, Brittin CA, Xu M, Thomson JN, Albertson DG, Hall DH, Emmons SW (2012) The connectome of a decision-making neural network. Science (80- ) 337: 437–444.
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