Means | R Documentation |

The function `Means()`

creates a table of group
means, optionally with standard errors, confidence intervals, and
numbers of valid observations.

```
Means(data, ...)
## S3 method for class 'data.frame'
Means(data,
by, weights=NULL, subset=NULL,
default=NA,
se=FALSE, ci=FALSE, ci.level=.95,
counts=FALSE, ...)
## S3 method for class 'formula'
Means(data, subset, weights, ...)
## S3 method for class 'numeric'
Means(data, ...)
## S3 method for class 'means.table'
as.data.frame(x, row.names=NULL, optional=TRUE, drop=TRUE, ...)
## S3 method for class 'xmeans.table'
as.data.frame(x, row.names=NULL, optional=TRUE, drop=TRUE, ...)
```

`data` |
an object usually containing data, or a formula. If If |

`by` |
a formula, a vector of variable names or a data frame or list of factors. If If If |

`weights` |
an optional vector of weights, usually a variable in |

`subset` |
an optional logical vector to select observations,
usually the result of an expression in variables from |

`default` |
a default value used for empty cells without observations. |

`se` |
a logical value, indicates whether standard errors should be computed. |

`ci` |
a logical value, indicates whether limits of confidence intervals should be computed. |

`ci.level` |
a number, the confidence level of the confidence interval |

`counts` |
a logical value, indicates whether numbers of valid observations should be reported. |

`x` |
for |

`row.names` |
an optional character vector. This argmument presently is
inconsequential and only included for reasons of compatiblity
with the standard methods of |

`optional` |
an optional logical value. This argmument presently is
inconsequential and only included for reasons of compatiblity
with the standard methods of |

`drop` |
a logical value, determines whether "empty cells" should be dropped from the resulting data frame. |

`...` |
other arguments, either ignored or passed on to other methods where applicable. |

An array that inherits classes "means.table" and "table". If
`Means`

was called with `se=TRUE`

or `ci=TRUE`

then the result additionally inherits class "xmeans.table".

```
# Preparing example data
USstates <- as.data.frame(state.x77)
USstates <- within(USstates,{
region <- state.region
name <- state.name
abb <- state.abb
division <- state.division
})
USstates$w <- sample(runif(n=6),size=nrow(USstates),replace=TRUE)
# Using the data frame method
Means(USstates[c("Murder","division","region")],by=c("division","region"))
Means(USstates[c("Murder","division","region")],by=USstates[c("division","region")])
Means(USstates[c("Murder")],1)
Means(USstates[c("Murder","region")],by=c("region"))
# Using the formula method
# One 'dependent' variable
Means(Murder~1, data=USstates)
Means(Murder~division, data=USstates)
Means(Murder~division, data=USstates,weights=w)
Means(Murder~division+region, data=USstates)
as.data.frame(Means(Murder~division+region, data=USstates))
# Standard errors and counts
Means(Murder~division, data=USstates, se=TRUE, counts=TRUE)
drop(Means(Murder~division, data=USstates, se=TRUE, counts=TRUE))
as.data.frame(Means(Murder~division, data=USstates, se=TRUE, counts=TRUE))
# Confidence intervals
Means(Murder~division, data=USstates, ci=TRUE)
drop(Means(Murder~division, data=USstates, ci=TRUE))
as.data.frame(Means(Murder~division, data=USstates, ci=TRUE))
# More than one dependent variable
Means(Murder+Illiteracy~division, data=USstates)
as.data.frame(Means(Murder+Illiteracy~division, data=USstates))
# Confidence intervals
Means(Murder+Illiteracy~division, data=USstates, ci=TRUE)
as.data.frame(Means(Murder+Illiteracy~division, data=USstates, ci=TRUE))
# Some 'non-standard' but still valid usages:
with(USstates,
Means(Murder~division+region,subset=region!="Northeast"))
with(USstates,
Means(Murder,by=list(division,region)))
```

memisc documentation built on March 31, 2023, 7:29 p.m.

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