Easy Creation of Tables of Percentages
percentages.RdThe generic function percentages and its methods
create one- or multidimensional tables of percentages. As such,
the function percentages can be viewed as a convenience
interface to prop.table. However, it also
allows to obtain standard errors and confidence intervals.
Usage
percentages(obj, ...)
# S3 method for class 'table'
percentages(obj,
by=NULL, which=NULL, se=FALSE, ci=FALSE, ci.level=.95, ...)
# S3 method for class 'formula'
percentages(obj,
data=parent.frame(), weights=NULL, ...)
# Default S3 method
percentages(obj,
weights=NULL, ...)
# S3 method for class 'data.frame'
percentages(obj,
weights=NULL, ...)
# S3 method for class 'list'
percentages(obj,
weights=NULL, ...)
# S3 method for class 'percentage.table'
as.data.frame(x, ...)
# S3 method for class 'xpercentage.table'
as.data.frame(x, ...)Arguments
- obj
an object; a contingency table or a formula. If it is a formula, its left-hand side determines the factor or combination of factors for which percentages are computed while its right-hand side determines the factor or combination of factors that define the groups within which percentages are computed.
- by
a character vector with the names of the factor variables that define the groups within which percentages are computed. Percentages sum to 100 within combination of levels of these factors.
- which
a character vector with the names of the factor variables for which percentages are computed.
- se
a logical value; determines whether standard errors are computed.
- ci
a logical value; determines whether confidence intervals are computed. Note that the confidence intervals are for infinite (or very large) populations.
- ci.level
a numerical value, the required confidence level of the confidence intervals.
- data
a contingency table (an object that inherits from "table") or a data frame or an object coercable into a data frame.
- weights
an optional vector of weights. Should be NULL or a numeric vector.
- ...
Further arguments passed on to the "table" method of
percentagesor ignored in case of a call toas.data.frame.- x
an object coerced into a data frame.
Value
An array that inherits classes "percentage.table" and "table". If
percentages was called with se=TRUE or ci=TRUE
then the result additionally inherits class "xpercentage.table".
Examples
percentages(UCBAdmissions)
#> , , Dept = A
#>
#> Gender
#> Admit Male Female
#> Admitted 11.3124171 1.9664163
#> Rejected 6.9155988 0.4197967
#>
#> , , Dept = B
#>
#> Gender
#> Admit Male Female
#> Admitted 7.7993814 0.3756076
#> Rejected 4.5735749 0.1767565
#>
#> , , Dept = C
#>
#> Gender
#> Admit Male Female
#> Admitted 2.6513478 4.4631021
#> Rejected 4.5293858 8.6389748
#>
#> , , Dept = D
#>
#> Gender
#> Admit Male Female
#> Admitted 3.0490499 2.8943880
#> Rejected 6.1643836 5.3910738
#>
#> , , Dept = E
#>
#> Gender
#> Admit Male Female
#> Admitted 1.1710119 2.0768891
#> Rejected 3.0490499 6.6062749
#>
#> , , Dept = F
#>
#> Gender
#> Admit Male Female
#> Admitted 0.4860804 0.5302696
#> Rejected 7.7551922 7.0039770
#>
# Three equivalent ways to create the same table of conditional
# percentages
percentages(Admit~Gender+Dept,data=UCBAdmissions)
#> , , Dept = A
#>
#> Gender
#> Admit Male Female
#> Admitted 62.060606 82.407407
#> Rejected 37.939394 17.592593
#>
#> , , Dept = B
#>
#> Gender
#> Admit Male Female
#> Admitted 63.035714 68.000000
#> Rejected 36.964286 32.000000
#>
#> , , Dept = C
#>
#> Gender
#> Admit Male Female
#> Admitted 36.923077 34.064081
#> Rejected 63.076923 65.935919
#>
#> , , Dept = D
#>
#> Gender
#> Admit Male Female
#> Admitted 33.093525 34.933333
#> Rejected 66.906475 65.066667
#>
#> , , Dept = E
#>
#> Gender
#> Admit Male Female
#> Admitted 27.748691 23.918575
#> Rejected 72.251309 76.081425
#>
#> , , Dept = F
#>
#> Gender
#> Admit Male Female
#> Admitted 5.898123 7.038123
#> Rejected 94.101877 92.961877
#>
percentages(UCBAdmissions,by=c("Gender","Dept"))
#> , , Dept = A
#>
#> Gender
#> Admit Male Female
#> Admitted 62.060606 82.407407
#> Rejected 37.939394 17.592593
#>
#> , , Dept = B
#>
#> Gender
#> Admit Male Female
#> Admitted 63.035714 68.000000
#> Rejected 36.964286 32.000000
#>
#> , , Dept = C
#>
#> Gender
#> Admit Male Female
#> Admitted 36.923077 34.064081
#> Rejected 63.076923 65.935919
#>
#> , , Dept = D
#>
#> Gender
#> Admit Male Female
#> Admitted 33.093525 34.933333
#> Rejected 66.906475 65.066667
#>
#> , , Dept = E
#>
#> Gender
#> Admit Male Female
#> Admitted 27.748691 23.918575
#> Rejected 72.251309 76.081425
#>
#> , , Dept = F
#>
#> Gender
#> Admit Male Female
#> Admitted 5.898123 7.038123
#> Rejected 94.101877 92.961877
#>
percentages(UCBAdmissions,which="Admit")
#> , , Dept = A
#>
#> Gender
#> Admit Male Female
#> Admitted 62.060606 82.407407
#> Rejected 37.939394 17.592593
#>
#> , , Dept = B
#>
#> Gender
#> Admit Male Female
#> Admitted 63.035714 68.000000
#> Rejected 36.964286 32.000000
#>
#> , , Dept = C
#>
#> Gender
#> Admit Male Female
#> Admitted 36.923077 34.064081
#> Rejected 63.076923 65.935919
#>
#> , , Dept = D
#>
#> Gender
#> Admit Male Female
#> Admitted 33.093525 34.933333
#> Rejected 66.906475 65.066667
#>
#> , , Dept = E
#>
#> Gender
#> Admit Male Female
#> Admitted 27.748691 23.918575
#> Rejected 72.251309 76.081425
#>
#> , , Dept = F
#>
#> Gender
#> Admit Male Female
#> Admitted 5.898123 7.038123
#> Rejected 94.101877 92.961877
#>
# Percentage table as data frame
as.data.frame(percentages(Admit~Gender+Dept,data=UCBAdmissions))
#> Admit Gender Dept Percentage
#> 1 Admitted Male A 62.060606
#> 2 Rejected Male A 37.939394
#> 3 Admitted Female A 82.407407
#> 4 Rejected Female A 17.592593
#> 5 Admitted Male B 63.035714
#> 6 Rejected Male B 36.964286
#> 7 Admitted Female B 68.000000
#> 8 Rejected Female B 32.000000
#> 9 Admitted Male C 36.923077
#> 10 Rejected Male C 63.076923
#> 11 Admitted Female C 34.064081
#> 12 Rejected Female C 65.935919
#> 13 Admitted Male D 33.093525
#> 14 Rejected Male D 66.906475
#> 15 Admitted Female D 34.933333
#> 16 Rejected Female D 65.066667
#> 17 Admitted Male E 27.748691
#> 18 Rejected Male E 72.251309
#> 19 Admitted Female E 23.918575
#> 20 Rejected Female E 76.081425
#> 21 Admitted Male F 5.898123
#> 22 Rejected Male F 94.101877
#> 23 Admitted Female F 7.038123
#> 24 Rejected Female F 92.961877
# Standard errors and confidence intervals
percentages(Admit~Dept,data=UCBAdmissions,se=TRUE)
#> , , Result = Percentage
#>
#> Dept
#> Admit A B C D E F
#> Admitted 64.4158628 63.2478632 35.0762527 33.9646465 25.1712329 6.4425770
#> Rejected 35.5841372 36.7521368 64.9237473 66.0353535 74.8287671 93.5574230
#>
#> , , Result = SE
#>
#> Dept
#> Admit A B C D E F
#> Admitted 1.5674143 1.9933625 1.5750235 1.6828260 1.7958916 0.9187976
#> Rejected 1.5674143 1.9933625 1.5750235 1.6828260 1.7958916 0.9187976
#>
percentages(Admit~Dept,data=UCBAdmissions,ci=TRUE)
#> , , Result = Percentage
#>
#> Dept
#> Admit A B C D E F
#> Admitted 64.415863 63.247863 35.076253 33.964646 25.171233 6.442577
#> Rejected 35.584137 36.752137 64.923747 66.035354 74.828767 93.557423
#>
#> , , Result = Lower bound
#>
#> Dept
#> Admit A B C D E F
#> Admitted 61.248477 59.196386 31.986890 30.668157 21.698820 4.755024
#> Rejected 32.508100 32.835173 61.738250 62.618938 71.103049 91.499717
#>
#> , , Result = Upper bound
#>
#> Dept
#> Admit A B C D E F
#> Admitted 67.491900 67.164827 38.261750 37.381062 28.896951 8.500283
#> Rejected 38.751523 40.803614 68.013110 69.331843 78.301180 95.244976
#>
(p<- percentages(Admit~Dept,data=UCBAdmissions,ci=TRUE,se=TRUE))
#> , , Result = Percentage
#>
#> Dept
#> Admit A B C D E F
#> Admitted 64.4158628 63.2478632 35.0762527 33.9646465 25.1712329 6.4425770
#> Rejected 35.5841372 36.7521368 64.9237473 66.0353535 74.8287671 93.5574230
#>
#> , , Result = SE
#>
#> Dept
#> Admit A B C D E F
#> Admitted 1.5674143 1.9933625 1.5750235 1.6828260 1.7958916 0.9187976
#> Rejected 1.5674143 1.9933625 1.5750235 1.6828260 1.7958916 0.9187976
#>
#> , , Result = Lower bound
#>
#> Dept
#> Admit A B C D E F
#> Admitted 61.2484768 59.1963862 31.9868896 30.6681566 21.6988202 4.7550241
#> Rejected 32.5081003 32.8351731 61.7382497 62.6189380 71.1030491 91.4997166
#>
#> , , Result = Upper bound
#>
#> Dept
#> Admit A B C D E F
#> Admitted 67.4918997 67.1648269 38.2617503 37.3810620 28.8969509 8.5002834
#> Rejected 38.7515232 40.8036138 68.0131104 69.3318434 78.3011798 95.2449759
#>
# An extended table of percentages as data frame
as.data.frame(p)
#> Admit Dept Percentage SE Lower bound Upper bound
#> 1 Admitted A 64.415863 1.5674143 61.248477 67.491900
#> 2 Admitted B 63.247863 1.9933625 59.196386 67.164827
#> 3 Admitted C 35.076253 1.5750235 31.986890 38.261750
#> 4 Admitted D 33.964646 1.6828260 30.668157 37.381062
#> 5 Admitted E 25.171233 1.7958916 21.698820 28.896951
#> 6 Admitted F 6.442577 0.9187976 4.755024 8.500283
#> 7 Rejected A 35.584137 1.5674143 32.508100 38.751523
#> 8 Rejected B 36.752137 1.9933625 32.835173 40.803614
#> 9 Rejected C 64.923747 1.5750235 61.738250 68.013110
#> 10 Rejected D 66.035354 1.6828260 62.618938 69.331843
#> 11 Rejected E 74.828767 1.7958916 71.103049 78.301180
#> 12 Rejected F 93.557423 0.9187976 91.499717 95.244976
# A table of percentages of a factor
percentages(iris$Species)
#> iris$Species
#> setosa versicolor virginica
#> 33.33333 33.33333 33.33333
UCBA <- as.data.frame(UCBAdmissions)
percentages(UCBA$Admit,weights=UCBA$Freq)
#> UCBA$Admit
#> Admitted Rejected
#> 38.77596 61.22404
percentages(UCBA,weights=UCBA$Freq)
#> $Admit
#> Admit
#> Admitted Rejected
#> 38.77596 61.22404
#>
#> $Gender
#> Gender
#> Male Female
#> 59.45647 40.54353
#>
#> $Dept
#> Dept
#> A B C D E F
#> 20.61423 12.92532 20.28281 17.49890 12.90323 15.77552
#>
#> $Freq
#> Freq
#> 8 17 19 22 24 53 89
#> 0.1767565 0.3756076 0.4197967 0.4860804 0.5302696 1.1710119 1.9664163
#> 94 120 131 138 202 205 207
#> 2.0768891 2.6513478 2.8943880 6.0980999 4.4631021 4.5293858 4.5735749
#> 244 279 299 313 317 351 353
#> 5.3910738 6.1643836 6.6062749 6.9155988 7.0039770 7.7551922 7.7993814
#> 391 512
#> 8.6389748 11.3124171
#>