corbetw2mat | R Documentation |

For matrices x and y, calculate the correlation between columns of x and columns of y.

corbetw2mat( x, y, what = c("paired", "bestright", "bestpairs", "all"), corthresh = 0.9 )

`x` |
A numeric matrix. |

`y` |
A numeric matrix with the same number of rows as |

`what` |
Indicates which correlations to calculate and return. See value, below. |

`corthresh` |
Threshold on correlations if |

Missing values (`NA`

) are ignored, and we calculate the correlation
using all complete pairs, as in `stats::cor()`

with
`use="pairwise.complete.obs"`

.

If `what="paired"`

, the return value is a vector of
correlations, between columns of `x`

and the corresponding column of
`y`

. `x`

and `y`

must have the same number of columns.

If `what="bestright"`

, we return a data frame of size `ncol(x)`

by
`3`

, with the *i*th row being the maximum correlation between
column *i* of `x`

and a column of `y`

, and then the
`y`

-column index and `y`

-column name with that correlation. (In
case of ties, we give the first one.)

If `what="bestpairs"`

, we return a data frame with five columns,
containing all pairs of columns (with one in `x`

and one in `y`

)
with correlation *≥* `corthresh`

. Each row corresponds to a
column pair, and contains the correlation and then the `x`

- and
`y`

-column indices followed by the `x`

- and `y`

-column names.

If `what="all"`

, the output is a matrix of size `ncol(x)`

by
`ncol(y)`

, with all correlations between columns of `x`

and
columns of `y`

.

Karl W Broman, broman@wisc.edu

`distee()`

, `findCommonID()`

data(expr1, expr2) # correlations with paired columns r <- corbetw2mat(expr1, expr2) # top 10, by absolute value r[order(abs(r), decreasing=TRUE)[1:10]] # all pairs of columns with correlation >= 0.8 r_allpairs <- corbetw2mat(expr1, expr2, what="bestpairs", corthresh=0.6) # for each column in left matrix, most-correlated column in right matrix r_bestright <- corbetw2mat(expr1, expr2, what="bestright")

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