Means for groups of observations
Means.RdThe function Means() creates a table of group
means, optionally with standard errors, confidence intervals, and
numbers of valid observations.
Usage
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, ...)Arguments
- data
an object usually containing data, or a formula.
If
datais a numeric vector or an object that can be coerced into a data frame, it is changed into a data frame and the data frame method ofMeans()is applied to it.If
datais a formula, then a data frame is constructed from the variables in the formula andMeansis applied to this data frame, while the formula is passed on as aby=argument.- by
a formula, a vector of variable names or a data frame or list of factors.
If
byis a vector of variable names, they are extracted fromdatato define the groups for which means are computed, while the variables for which the means are computed are those not named inby.If
byis a data frame or a list of factors, these are used to defined the groups for which means are computed, while the variables for which the means are computed are those not inby.If
byis a formula, its left-hand side determines the variables of which means are computed, while its right-hand side determines the factors that define the groups.- weights
an optional vector of weights, usually a variable in
data.- subset
an optional logical vector to select observations, usually the result of an expression in variables from
data.- 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
as.data.frame(), a result ofMeans().- row.names
an optional character vector. This argmument presently is inconsequential and only included for reasons of compatiblity with the standard methods of
as.data.frame.- optional
an optional logical value. This argmument presently is inconsequential and only included for reasons of compatiblity with the standard methods of
as.data.frame.- 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.
Value
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".
Examples
# 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"))
#> , , = Murder
#>
#> region
#> division Northeast South North Central West
#> New England 3.383333
#> Middle Atlantic 7.400000
#> South Atlantic 9.775000
#> East South Central 12.300000
#> West South Central 10.475000
#> East North Central 7.780000
#> West North Central 3.485714
#> Mountain 7.187500
#> Pacific 7.260000
#>
Means(USstates[c("Murder","division","region")],by=USstates[c("division","region")])
#> , , = Murder
#>
#> region
#> division Northeast South North Central West
#> New England 3.383333
#> Middle Atlantic 7.400000
#> South Atlantic 9.775000
#> East South Central 12.300000
#> West South Central 10.475000
#> East North Central 7.780000
#> West North Central 3.485714
#> Mountain 7.187500
#> Pacific 7.260000
#>
Means(USstates[c("Murder")],1)
#> Mean
#> Murder 7.378
Means(USstates[c("Murder","region")],by=c("region"))
#>
#> region Murder
#> Northeast 4.722222
#> South 10.581250
#> North Central 5.275000
#> West 7.215385
# Using the formula method
# One 'dependent' variable
Means(Murder~1, data=USstates)
#> Mean
#> Murder 7.378
Means(Murder~division, data=USstates)
#>
#> division Murder
#> New England 3.383333
#> Middle Atlantic 7.400000
#> South Atlantic 9.775000
#> East South Central 12.300000
#> West South Central 10.475000
#> East North Central 7.780000
#> West North Central 3.485714
#> Mountain 7.187500
#> Pacific 7.260000
Means(Murder~division, data=USstates,weights=w)
#>
#> division Murder
#> New England 3.334936
#> Middle Atlantic 8.463128
#> South Atlantic 10.412793
#> East South Central 11.897595
#> West South Central 10.889880
#> East North Central 8.667188
#> West North Central 3.677417
#> Mountain 7.380093
#> Pacific 6.525287
Means(Murder~division+region, data=USstates)
#> , , = Murder
#>
#> region
#> division Northeast South North Central West
#> New England 3.383333
#> Middle Atlantic 7.400000
#> South Atlantic 9.775000
#> East South Central 12.300000
#> West South Central 10.475000
#> East North Central 7.780000
#> West North Central 3.485714
#> Mountain 7.187500
#> Pacific 7.260000
#>
as.data.frame(Means(Murder~division+region, data=USstates))
#> division region Murder
#> 1 New England Northeast 3.383333
#> 2 Middle Atlantic Northeast 7.400000
#> 12 South Atlantic South 9.775000
#> 13 East South Central South 12.300000
#> 14 West South Central South 10.475000
#> 24 East North Central North Central 7.780000
#> 25 West North Central North Central 3.485714
#> 35 Mountain West 7.187500
#> 36 Pacific West 7.260000
# Standard errors and counts
Means(Murder~division, data=USstates, se=TRUE, counts=TRUE)
#> , , Statistic = Mean
#>
#> Variable
#> division Murder
#> New England 3.3833333
#> Middle Atlantic 7.4000000
#> South Atlantic 9.7750000
#> East South Central 12.3000000
#> West South Central 10.4750000
#> East North Central 7.7800000
#> West North Central 3.4857143
#> Mountain 7.1875000
#> Pacific 7.2600000
#>
#> , , Statistic = SE
#>
#> Variable
#> division Murder
#> New England 0.4475241
#> Middle Atlantic 1.7691806
#> South Atlantic 0.9151015
#> East South Central 1.0189864
#> West South Central 1.5040916
#> East North Central 1.4287757
#> West North Central 1.0411597
#> Mountain 0.8565791
#> Pacific 1.4968634
#>
#> , , Statistic = N
#>
#> Variable
#> division Murder
#> New England 6.0000000
#> Middle Atlantic 3.0000000
#> South Atlantic 8.0000000
#> East South Central 4.0000000
#> West South Central 4.0000000
#> East North Central 5.0000000
#> West North Central 7.0000000
#> Mountain 8.0000000
#> Pacific 5.0000000
#>
drop(Means(Murder~division, data=USstates, se=TRUE, counts=TRUE))
#> Statistic
#> division Mean SE N
#> New England 3.3833333 0.4475241 6.0000000
#> Middle Atlantic 7.4000000 1.7691806 3.0000000
#> South Atlantic 9.7750000 0.9151015 8.0000000
#> East South Central 12.3000000 1.0189864 4.0000000
#> West South Central 10.4750000 1.5040916 4.0000000
#> East North Central 7.7800000 1.4287757 5.0000000
#> West North Central 3.4857143 1.0411597 7.0000000
#> Mountain 7.1875000 0.8565791 8.0000000
#> Pacific 7.2600000 1.4968634 5.0000000
as.data.frame(Means(Murder~division, data=USstates, se=TRUE, counts=TRUE))
#> division Variable Mean SE N
#> 1 New England Murder 3.383333 0.4475241 6
#> 2 Middle Atlantic Murder 7.400000 1.7691806 3
#> 3 South Atlantic Murder 9.775000 0.9151015 8
#> 4 East South Central Murder 12.300000 1.0189864 4
#> 5 West South Central Murder 10.475000 1.5040916 4
#> 6 East North Central Murder 7.780000 1.4287757 5
#> 7 West North Central Murder 3.485714 1.0411597 7
#> 8 Mountain Murder 7.187500 0.8565791 8
#> 9 Pacific Murder 7.260000 1.4968634 5
# Confidence intervals
Means(Murder~division, data=USstates, ci=TRUE)
#> , , Statistic = Mean
#>
#> Variable
#> division Murder
#> New England 3.3833333
#> Middle Atlantic 7.4000000
#> South Atlantic 9.7750000
#> East South Central 12.3000000
#> West South Central 10.4750000
#> East North Central 7.7800000
#> West North Central 3.4857143
#> Mountain 7.1875000
#> Pacific 7.2600000
#>
#> , , Statistic = Lower
#>
#> Variable
#> division Murder
#> New England 2.2329361
#> Middle Atlantic -0.2121697
#> South Atlantic 7.6111289
#> East South Central 9.0571304
#> West South Central 5.6883091
#> East North Central 3.8130827
#> West North Central 0.9380882
#> Mountain 5.1620124
#> Pacific 3.1040410
#>
#> , , Statistic = Upper
#>
#> Variable
#> division Murder
#> New England 2.2329361
#> Middle Atlantic -0.2121697
#> South Atlantic 7.6111289
#> East South Central 9.0571304
#> West South Central 5.6883091
#> East North Central 3.8130827
#> West North Central 0.9380882
#> Mountain 5.1620124
#> Pacific 3.1040410
#>
drop(Means(Murder~division, data=USstates, ci=TRUE))
#> Statistic
#> division Mean Lower Upper
#> New England 3.3833333 2.2329361 2.2329361
#> Middle Atlantic 7.4000000 -0.2121697 -0.2121697
#> South Atlantic 9.7750000 7.6111289 7.6111289
#> East South Central 12.3000000 9.0571304 9.0571304
#> West South Central 10.4750000 5.6883091 5.6883091
#> East North Central 7.7800000 3.8130827 3.8130827
#> West North Central 3.4857143 0.9380882 0.9380882
#> Mountain 7.1875000 5.1620124 5.1620124
#> Pacific 7.2600000 3.1040410 3.1040410
as.data.frame(Means(Murder~division, data=USstates, ci=TRUE))
#> division Variable Mean Lower Upper
#> 1 New England Murder 3.383333 2.2329361 2.2329361
#> 2 Middle Atlantic Murder 7.400000 -0.2121697 -0.2121697
#> 3 South Atlantic Murder 9.775000 7.6111289 7.6111289
#> 4 East South Central Murder 12.300000 9.0571304 9.0571304
#> 5 West South Central Murder 10.475000 5.6883091 5.6883091
#> 6 East North Central Murder 7.780000 3.8130827 3.8130827
#> 7 West North Central Murder 3.485714 0.9380882 0.9380882
#> 8 Mountain Murder 7.187500 5.1620124 5.1620124
#> 9 Pacific Murder 7.260000 3.1040410 3.1040410
# More than one dependent variable
Means(Murder+Illiteracy~division, data=USstates)
#>
#> division Murder Illiteracy
#> New England 3.3833333 0.9166667
#> Middle Atlantic 7.4000000 1.1666667
#> South Atlantic 9.7750000 1.5000000
#> East South Central 12.3000000 1.9500000
#> West South Central 10.4750000 2.0000000
#> East North Central 7.7800000 0.8000000
#> West North Central 3.4857143 0.6285714
#> Mountain 7.1875000 0.9500000
#> Pacific 7.2600000 1.1400000
as.data.frame(Means(Murder+Illiteracy~division, data=USstates))
#> division Murder Illiteracy
#> 1 New England 3.383333 0.9166667
#> 2 Middle Atlantic 7.400000 1.1666667
#> 3 South Atlantic 9.775000 1.5000000
#> 4 East South Central 12.300000 1.9500000
#> 5 West South Central 10.475000 2.0000000
#> 6 East North Central 7.780000 0.8000000
#> 7 West North Central 3.485714 0.6285714
#> 8 Mountain 7.187500 0.9500000
#> 9 Pacific 7.260000 1.1400000
# Confidence intervals
Means(Murder+Illiteracy~division, data=USstates, ci=TRUE)
#> , , Statistic = Mean
#>
#> Variable
#> division Murder Illiteracy
#> New England 3.3833333 0.9166667
#> Middle Atlantic 7.4000000 1.1666667
#> South Atlantic 9.7750000 1.5000000
#> East South Central 12.3000000 1.9500000
#> West South Central 10.4750000 2.0000000
#> East North Central 7.7800000 0.8000000
#> West North Central 3.4857143 0.6285714
#> Mountain 7.1875000 0.9500000
#> Pacific 7.2600000 1.1400000
#>
#> , , Statistic = Lower
#>
#> Variable
#> division Murder Illiteracy
#> New England 2.2329361 0.6167655
#> Middle Atlantic -0.2121697 0.6495522
#> South Atlantic 7.6111289 1.0807969
#> East South Central 9.0571304 1.3617494
#> West South Central 5.6883091 0.8748353
#> East North Central 3.8130827 0.6758336
#> West North Central 0.9380882 0.5126359
#> Mountain 5.1620124 0.3990592
#> Pacific 3.1040410 0.4343240
#>
#> , , Statistic = Upper
#>
#> Variable
#> division Murder Illiteracy
#> New England 2.2329361 0.6167655
#> Middle Atlantic -0.2121697 0.6495522
#> South Atlantic 7.6111289 1.0807969
#> East South Central 9.0571304 1.3617494
#> West South Central 5.6883091 0.8748353
#> East North Central 3.8130827 0.6758336
#> West North Central 0.9380882 0.5126359
#> Mountain 5.1620124 0.3990592
#> Pacific 3.1040410 0.4343240
#>
as.data.frame(Means(Murder+Illiteracy~division, data=USstates, ci=TRUE))
#> division Variable Mean Lower Upper
#> 1 New England Murder 3.3833333 2.2329361 2.2329361
#> 2 Middle Atlantic Murder 7.4000000 -0.2121697 -0.2121697
#> 3 South Atlantic Murder 9.7750000 7.6111289 7.6111289
#> 4 East South Central Murder 12.3000000 9.0571304 9.0571304
#> 5 West South Central Murder 10.4750000 5.6883091 5.6883091
#> 6 East North Central Murder 7.7800000 3.8130827 3.8130827
#> 7 West North Central Murder 3.4857143 0.9380882 0.9380882
#> 8 Mountain Murder 7.1875000 5.1620124 5.1620124
#> 9 Pacific Murder 7.2600000 3.1040410 3.1040410
#> 10 New England Illiteracy 0.9166667 0.6167655 0.6167655
#> 11 Middle Atlantic Illiteracy 1.1666667 0.6495522 0.6495522
#> 12 South Atlantic Illiteracy 1.5000000 1.0807969 1.0807969
#> 13 East South Central Illiteracy 1.9500000 1.3617494 1.3617494
#> 14 West South Central Illiteracy 2.0000000 0.8748353 0.8748353
#> 15 East North Central Illiteracy 0.8000000 0.6758336 0.6758336
#> 16 West North Central Illiteracy 0.6285714 0.5126359 0.5126359
#> 17 Mountain Illiteracy 0.9500000 0.3990592 0.3990592
#> 18 Pacific Illiteracy 1.1400000 0.4343240 0.4343240
# Some 'non-standard' but still valid usages:
with(USstates,
Means(Murder~division+region,subset=region!="Northeast"))
#> , , = Murder
#>
#> region
#> division South North Central West
#> South Atlantic 9.775000
#> East South Central 12.300000
#> West South Central 10.475000
#> East North Central 7.780000
#> West North Central 3.485714
#> Mountain 7.187500
#> Pacific 7.260000
#>
with(USstates,
Means(Murder,by=list(division,region)))
#> , , Murder
#>
#> Northeast South North Central West
#> New England 3.383333
#> Middle Atlantic 7.400000
#> South Atlantic 9.775000
#> East South Central 12.300000
#> West South Central 10.475000
#> East North Central 7.780000
#> West North Central 3.485714
#> Mountain 7.187500
#> Pacific 7.260000
#>