# path_weights

##### Utility functions to manipulate pairwise information.

These functions perform calculations on edge matrices containing pairwise information.

- Keywords
- graphs

##### Usage

```
path_weights(edgew, path, symmetric = TRUE,edge.index=edge_index(edgew),...)
path_cis(edgew, path,edge.index=edge_index(edgew),ci.pos=FALSE)
edge2dist(edgew, edge.index=edge_index(edgew))
dist2edge(d)
edge_index(x, order="default")
```

##### Arguments

- edgew
A Matrix (or vector) whose ith row (or element) has weights for pair indexed by pair in row i of edge.index. For

`edge2dist`

,`edgew`

should be a vector.- path
Vector of indices into rows of

`edgew`

.- symmetric
If

`TRUE`

edge weights are interpreted as symmetric.- edge.index
A 2-column matrix with each row giving indices for corresponding weight in

`edgew`

.- ci.pos
If TRUE, all CIs are mu(max) - mu(min), otherwise mu(right) - mu(left).

- d
A

`dist`

or matrx of distances.- order
If "low.order.first" or "scagnostics", lists lowest index pairs first, otherwise lists pairs starting with 1, then 2 etc.

- x
An edgew matrix or vector, or a positive integer.

- ...
Ignored

##### Details

`path_weights`

- Returns matrix of path weights so that the ith row of result contains weights for indices path[i], path[i+1]

`path_cis`

- Returns matrix of path confidence intervals so that the ith row of result contains intervals for mean-path[i] - mean-path[i+1]

`edge2dist`

- Returns a `dist`

,
containing elements of `edgew`

.

`dist2edge`

- Returns a vector of edge weights.

`edge_index`

-A generic function. Returns a 2-column matrix with one row for
each edge. Each row contains an index pair i,j. If `order`

is "low.order.first" or "scagnostics", lists lowest index pairs first - this is the default ordering for class `scagdf`

, otherwise lists pairs
starting with 1, then 2 etc

`nnodes`

- Here `edgew`

contains edge weights for a complete graph; returns the number of nodes in this complete graph.

##### References

see overview

##### Examples

```
# NOT RUN {
require(PairViz)
s <- matrix(1:40,nrow=10,ncol=4)
edge2dist(s[,1])
path_weights(s,1:4)
path_weights(s,eseq(5))
fm1 <- aov(breaks ~ wool + tension, data = warpbreaks)
tuk <- TukeyHSD(fm1, "tension")[[1]]
# Here the first argument (weight matrix) can have number of columns
path_weights(tuk,c(1:3,1))
# Here the first argument (weight matrix) should have an odd number of columns-
# the first is the mean difference, other column pairs are endpoints of CIs
path_cis(tuk[,-4],c(1:3,1))
# }
```

*Documentation reproduced from package PairViz, version 1.3.2, License: GPL-2*