Description Usage Arguments Details Value Author(s) See Also Examples

Compute Row (weighted) means across columns of a numeric matrix-like object for each level of a grouping variable.

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`M` |
a matrix, data frame or vector of numeric data. Missing values are allowed. A numeric vector will be treated as a column vector. |

`group` |
a vector or factor giving the grouping, with one element per row of M. Default: rownames of M. |

`w` |
a vector giving the weights that must be applied to each of the stacked blocks of an original object |

`reord` |
if TRUE, then the result will be in order of sort(unique(group)), if FALSE (the default), it will be in the order that groups were encountered. |

`na_rm` |
logical (TRUE or FALSE). Should NA (including NaN) values be discarded? |

`big` |
is your object big and integer overflow is likely? If TRUE, then M is multiplied by 1.0 to ensure values are of type double (perhaps taking more RAM). |

`...` |
other arguments to be passed to or from methods. |

This function is a wrapper for analytics function `rowmean`

which allows one to compute the (weighted) mean instead of the sum,
while handling integer overflow.

Note: although data frames ara allowed, keep in mind that data frames do not allow duplicate row names. Hence if you have a dataframe with more than 1 group, you may want to use the function as.matrix() to convert it to an object of class matrix

To compute the mean over all the rows of a matrix (i.e. a single group) use colMeans, which should be even faster.

A matrix-like object containing the means by group. There will be one row per unique value of group.
If object supplied in fact (explicitly) had just one group, base function
`colMeans`

is called for maximum efficiency and a numeric vector containing
the mean of each column is returned.

Albert Dorador

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