cats2ranks: Ordered option selections to ranks
In crank: Completing Ranks
Description
Usage
Arguments
Details
Value
Author(s)
See Also
Examples
Description
Convert ordered option selections to ranks, assigning the mean of unused
ranks to unselected options.
Usage
1
cats2ranks(x,cats=NULL)
Arguments
x
A matrix or data frame of numeric or characters labels for options.
Rows are interpreted as cases or respondents and columns are interpreted as
the order of option selections, beginning with the highest ranking
(usually something like "Most important") and descending.
cats
The range of numbers that represent options. The default is the
vector of unique entries in x.
Details
cats2ranks converts ordered option selections to mean ranks. It is
useful in the situation where a respondent is asked to select one of a number
of options as the most important, another as the second most important, and
so on. It counts the number of times each option code appears in each column
and calculates the mean ranking of options. It is expected that there will be
fewer selections available than there are options, thus creating the opportunity
for biased rankings. This can occur when one or more options are not commonly
chosen, but are given extreme (usually high) ranks when they are. The function
calculates the mean of unallocated ranks and assigns this to all options not
chosen by each respondent, correcting for this bias. The correction assumes
that the respondent does not differentiate between unranked options, but these
are all ranked lower than the options selected.
cats2ranks is especially useful when respondents do not select the same
number of options. The mean of unallocated ranks is calculated for each
respondent so that all options are entered into the calculation of mean ranks.
Note that cats2ranks interprets each value in x as a nominal
level variable and its column index as the rank, while meanranks
interprets values as ordinal level (ranks). Thus if a matrix or data frame
of ranks is passed to cats2ranks, it will not give the correct mean
ranks or relative positions.
Value
A list with four components:
ranks
The matrix of completed ranks.
cats
The vector of options as passed or calculated.
ranksum
The sum of ranks for each option.
rankcount
The number of times each option was selected.
Author(s)
Jim Lemon
See Also
muranks,meanranks
Examples
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
# first a standard 1:m numerically coded selection
opchoice<-matrix(NA,nrow=40,ncol=5)
for(i in 1:40) opchoice[i,]<-sample(1:10,5)
opchoice
cats2ranks(opchoice)
# now a messy character choice with missing values
opchoice<-matrix(NA,nrow=40,ncol=5)
tencolors<-c("red","green","blue","yellow","magenta","cyan",
"purple","orange","brown","pink")
for(i in 1:40) {
nchoices<-sample(3:5,1)
opchoice[i,1:nchoices]<-sample(tencolors,nchoices)
}
opchoice
cats2ranks(opchoice)
Example output
[,1] [,2] [,3] [,4] [,5]
[1,] 3 2 5 7 10
[2,] 4 1 8 6 10
[3,] 6 2 5 10 4
[4,] 9 1 7 10 4
[5,] 8 1 10 4 6
[6,] 9 1 4 2 8
[7,] 3 9 4 1 7
[8,] 5 2 10 4 7
[9,] 10 4 8 9 7
[10,] 7 9 8 4 3
[11,] 1 6 7 10 8
[12,] 6 10 2 1 3
[13,] 10 3 9 8 2
[14,] 5 2 6 8 4
[15,] 7 4 1 2 8
[16,] 1 10 3 8 5
[17,] 7 10 9 2 3
[18,] 3 10 9 4 8
[19,] 7 3 4 10 8
[20,] 5 6 10 8 3
[21,] 2 10 5 4 3
[22,] 7 4 9 10 6
[23,] 6 9 7 4 8
[24,] 1 8 3 4 6
[25,] 10 8 4 1 5
[26,] 4 9 10 1 5
[27,] 2 4 1 7 9
[28,] 10 4 2 9 8
[29,] 8 6 5 2 7
[30,] 5 3 8 1 10
[31,] 10 3 7 5 8
[32,] 7 1 10 9 3
[33,] 4 3 6 7 1
[34,] 9 5 1 3 7
[35,] 2 10 6 4 7
[36,] 8 7 6 9 10
[37,] 6 1 5 7 9
[38,] 9 4 1 6 5
[39,] 3 10 2 4 1
[40,] 2 10 8 3 6
Mean rank N
10 4.5 27
4 4.95 25
1 5.42 20
7 5.45 21
8 5.55 22
3 5.65 19
9 5.65 18
2 5.85 16
6 5.85 17
5 6.12 15
[,1] [,2] [,3] [,4] [,5]
[1,] "blue" "pink" "red" "cyan" "magenta"
[2,] "red" "blue" "pink" "purple" "green"
[3,] "green" "brown" "red" "yellow" "pink"
[4,] "purple" "yellow" "brown" "pink" NA
[5,] "red" "purple" "brown" "orange" NA
[6,] "blue" "orange" "green" "brown" "red"
[7,] "red" "cyan" "pink" "purple" "magenta"
[8,] "brown" "orange" "purple" NA NA
[9,] "blue" "purple" "yellow" NA NA
[10,] "pink" "green" "purple" NA NA
[11,] "brown" "pink" "purple" "green" "red"
[12,] "magenta" "green" "pink" "yellow" "purple"
[13,] "orange" "cyan" "green" "pink" "blue"
[14,] "pink" "brown" "purple" "cyan" NA
[15,] "green" "orange" "blue" "purple" "brown"
[16,] "yellow" "blue" "pink" NA NA
[17,] "magenta" "green" "yellow" "orange" NA
[18,] "purple" "brown" "orange" "red" NA
[19,] "green" "orange" "pink" "cyan" "brown"
[20,] "green" "yellow" "orange" "cyan" NA
[21,] "yellow" "purple" "red" "green" "pink"
[22,] "yellow" "pink" "magenta" NA NA
[23,] "brown" "cyan" "yellow" "green" NA
[24,] "green" "brown" "red" "pink" NA
[25,] "magenta" "pink" "orange" NA NA
[26,] "cyan" "purple" "magenta" "orange" "pink"
[27,] "magenta" "orange" "yellow" NA NA
[28,] "orange" "cyan" "green" "yellow" NA
[29,] "green" "pink" "blue" NA NA
[30,] "blue" "cyan" "brown" "green" NA
[31,] "orange" "brown" "cyan" NA NA
[32,] "blue" "red" "cyan" "green" "pink"
[33,] "blue" "magenta" "green" "red" "yellow"
[34,] "brown" "magenta" "pink" "cyan" "red"
[35,] "red" "orange" "green" "pink" NA
[36,] "yellow" "blue" "pink" NA NA
[37,] "brown" "pink" "green" NA NA
[38,] "brown" "red" "orange" "pink" "yellow"
[39,] "yellow" "cyan" "red" "purple" "magenta"
[40,] "orange" "magenta" "green" NA NA
Mean rank N
green 4.8 22
pink 4.86 24
brown 5.3 17
orange 5.36 17
red 5.56 16
yellow 5.62 16
purple 5.76 15
cyan 5.83 14
blue 5.85 12
magenta 6.05 12
crank documentation built on May 1, 2019, 9:39 p.m.
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Description Usage Arguments Details Value Author(s) See Also Examples
Description
Convert ordered option selections to ranks, assigning the mean of unused ranks to unselected options.
Usage
1 | cats2ranks(x,cats=NULL)
|
Arguments
x |
A matrix or data frame of numeric or characters labels for options. Rows are interpreted as cases or respondents and columns are interpreted as the order of option selections, beginning with the highest ranking (usually something like "Most important") and descending. |
cats |
The range of numbers that represent options. The default is the vector of unique entries in x. |
Details
cats2ranks converts ordered option selections to mean ranks. It is useful in the situation where a respondent is asked to select one of a number of options as the most important, another as the second most important, and so on. It counts the number of times each option code appears in each column and calculates the mean ranking of options. It is expected that there will be fewer selections available than there are options, thus creating the opportunity for biased rankings. This can occur when one or more options are not commonly chosen, but are given extreme (usually high) ranks when they are. The function calculates the mean of unallocated ranks and assigns this to all options not chosen by each respondent, correcting for this bias. The correction assumes that the respondent does not differentiate between unranked options, but these are all ranked lower than the options selected.
cats2ranks is especially useful when respondents do not select the same number of options. The mean of unallocated ranks is calculated for each respondent so that all options are entered into the calculation of mean ranks.
Note that cats2ranks interprets each value in x as a nominal level variable and its column index as the rank, while meanranks interprets values as ordinal level (ranks). Thus if a matrix or data frame of ranks is passed to cats2ranks, it will not give the correct mean ranks or relative positions.
Value
A list with four components:
ranks |
The matrix of completed ranks. |
cats |
The vector of options as passed or calculated. |
ranksum |
The sum of ranks for each option. |
rankcount |
The number of times each option was selected. |
Author(s)
Jim Lemon
See Also
muranks,meanranks
Examples
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 | # first a standard 1:m numerically coded selection
opchoice<-matrix(NA,nrow=40,ncol=5)
for(i in 1:40) opchoice[i,]<-sample(1:10,5)
opchoice
cats2ranks(opchoice)
# now a messy character choice with missing values
opchoice<-matrix(NA,nrow=40,ncol=5)
tencolors<-c("red","green","blue","yellow","magenta","cyan",
"purple","orange","brown","pink")
for(i in 1:40) {
nchoices<-sample(3:5,1)
opchoice[i,1:nchoices]<-sample(tencolors,nchoices)
}
opchoice
cats2ranks(opchoice)
|
Example output
[,1] [,2] [,3] [,4] [,5]
[1,] 3 2 5 7 10
[2,] 4 1 8 6 10
[3,] 6 2 5 10 4
[4,] 9 1 7 10 4
[5,] 8 1 10 4 6
[6,] 9 1 4 2 8
[7,] 3 9 4 1 7
[8,] 5 2 10 4 7
[9,] 10 4 8 9 7
[10,] 7 9 8 4 3
[11,] 1 6 7 10 8
[12,] 6 10 2 1 3
[13,] 10 3 9 8 2
[14,] 5 2 6 8 4
[15,] 7 4 1 2 8
[16,] 1 10 3 8 5
[17,] 7 10 9 2 3
[18,] 3 10 9 4 8
[19,] 7 3 4 10 8
[20,] 5 6 10 8 3
[21,] 2 10 5 4 3
[22,] 7 4 9 10 6
[23,] 6 9 7 4 8
[24,] 1 8 3 4 6
[25,] 10 8 4 1 5
[26,] 4 9 10 1 5
[27,] 2 4 1 7 9
[28,] 10 4 2 9 8
[29,] 8 6 5 2 7
[30,] 5 3 8 1 10
[31,] 10 3 7 5 8
[32,] 7 1 10 9 3
[33,] 4 3 6 7 1
[34,] 9 5 1 3 7
[35,] 2 10 6 4 7
[36,] 8 7 6 9 10
[37,] 6 1 5 7 9
[38,] 9 4 1 6 5
[39,] 3 10 2 4 1
[40,] 2 10 8 3 6
Mean rank N
10 4.5 27
4 4.95 25
1 5.42 20
7 5.45 21
8 5.55 22
3 5.65 19
9 5.65 18
2 5.85 16
6 5.85 17
5 6.12 15
[,1] [,2] [,3] [,4] [,5]
[1,] "blue" "pink" "red" "cyan" "magenta"
[2,] "red" "blue" "pink" "purple" "green"
[3,] "green" "brown" "red" "yellow" "pink"
[4,] "purple" "yellow" "brown" "pink" NA
[5,] "red" "purple" "brown" "orange" NA
[6,] "blue" "orange" "green" "brown" "red"
[7,] "red" "cyan" "pink" "purple" "magenta"
[8,] "brown" "orange" "purple" NA NA
[9,] "blue" "purple" "yellow" NA NA
[10,] "pink" "green" "purple" NA NA
[11,] "brown" "pink" "purple" "green" "red"
[12,] "magenta" "green" "pink" "yellow" "purple"
[13,] "orange" "cyan" "green" "pink" "blue"
[14,] "pink" "brown" "purple" "cyan" NA
[15,] "green" "orange" "blue" "purple" "brown"
[16,] "yellow" "blue" "pink" NA NA
[17,] "magenta" "green" "yellow" "orange" NA
[18,] "purple" "brown" "orange" "red" NA
[19,] "green" "orange" "pink" "cyan" "brown"
[20,] "green" "yellow" "orange" "cyan" NA
[21,] "yellow" "purple" "red" "green" "pink"
[22,] "yellow" "pink" "magenta" NA NA
[23,] "brown" "cyan" "yellow" "green" NA
[24,] "green" "brown" "red" "pink" NA
[25,] "magenta" "pink" "orange" NA NA
[26,] "cyan" "purple" "magenta" "orange" "pink"
[27,] "magenta" "orange" "yellow" NA NA
[28,] "orange" "cyan" "green" "yellow" NA
[29,] "green" "pink" "blue" NA NA
[30,] "blue" "cyan" "brown" "green" NA
[31,] "orange" "brown" "cyan" NA NA
[32,] "blue" "red" "cyan" "green" "pink"
[33,] "blue" "magenta" "green" "red" "yellow"
[34,] "brown" "magenta" "pink" "cyan" "red"
[35,] "red" "orange" "green" "pink" NA
[36,] "yellow" "blue" "pink" NA NA
[37,] "brown" "pink" "green" NA NA
[38,] "brown" "red" "orange" "pink" "yellow"
[39,] "yellow" "cyan" "red" "purple" "magenta"
[40,] "orange" "magenta" "green" NA NA
Mean rank N
green 4.8 22
pink 4.86 24
brown 5.3 17
orange 5.36 17
red 5.56 16
yellow 5.62 16
purple 5.76 15
cyan 5.83 14
blue 5.85 12
magenta 6.05 12
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