问题
I have trouble solving the following problem concerning the (simplified by limiting number of columns) data frame 'annotations' below.
require(irr)
# data
annotations <- read.table(text = "Obj1 Obj2 Obj3
Rater1 a b c
Rater2 a b b
Rater3 a b c", header = TRUE, stringsAsFactors = FALSE)
I would like to apply the function agree from the irr package to all combinations (not permutations) of rows, resulting in the following.
Agreement rater 1-2: 67%
Agreement rater 1-3: 100%
Agreement rater 2-3: 67%
I need to run a function on all combinations of rows and the function would need to access a number of/all columns.
I have worked out parts of the answer to the problem; I have generated a list of combinations running combn(rownames(annotations), 2), but I don't see how to use this list without writing inefficient for loops.
I have tried apply, as in apply(annotations, 1, agree), but I can only get this to work on one row, not the combinations mentioned before.
Does anyone have an idea how to proceed?
UPDATE: The following solution, based on your suggestions, works. (I have used kappa2 from the irr package instead of agree, but the solution to the main question remains the same.)
require(irr) #require the irr library for agreement calculations
annotations <- read.table(text = "Obj1 Obj2 Obj3
Rater1 a b c
Rater2 a b b
Rater3 a b c
Rater4 c a a", header = TRUE, stringsAsFactors = FALSE)
annotations <- t(annotations) #transpose annotations (rows become columns and vice versa)
kappa_list <- combn(colnames(annotations), 2, FUN=function(x) kappa_list[[length(kappa_list)+1]] = kappa2(matrix(c(annotations[,x[1]], annotations[,x[2]]), ncol=2))$value) #fill kappa_list with all pairs of columns (combinations of 2 raters) in annotations and, per combination, add a value to kappa_list that consists of the value of kappa2 applied to the current combination of raters
kappa_list # display the list of values
回答1:
You are close, you just need to apply on the result of combn instead. I have no idea what function you are referring to, but this should work the same if you plug in your function.
First, save the results as a list instead, because it is easier to add names (which I am adding my combining the two entries together):
toCheck <- combn(rownames(annotations), 2, simplify = FALSE)
names(toCheck) <-
sapply(toCheck, paste, collapse = " - ")
Then, use sapply to work through your combinations. Here, I am using mean to do the comparison, but use what you need here. If you are returning more than a single value, use lapply then work with the result to print as desired
sapply(toCheck, function(x){
mean(annotations[x[1], ] == annotations[x[2], ])
})
Which returns:
Rater 1 - Rater 2 Rater 1 - Rater 3 Rater 2 - Rater 3
0.6666667 1.0000000 0.6666667
回答2:
Applying the function f(x):= 2x+5 to all entries of a column corresponding to combinations. Instead of f(x):= 2x+5, one can write his/her own function:
Step1: Design specific combinations dataframe. (The following was for my own case)
causalitycombinations <- function (nvars, ncausers, ndependents)
{
independents <- combn(nvars, ncausers)
swingnumber <- dim(combn(nvars - ncausers, ndependents))[[2]]
numberofallcombinations <- dim(combn(nvars, ncausers))[[2]] * swingnumber
dependents <- matrix(, nrow = dim(combn(nvars, ncausers))[[2]] * swingnumber, ncol = ndependents)
for (i in as.integer(1:dim(combn(nvars, ncausers))[[2]])) {
dependents[(swingnumber * (i - 1) + 1):(swingnumber * i), ] <- t(combn(setdiff(seq(1:nvars), independents[, i]), ndependents))
}
swingedindependents <- matrix(, nrow = dim(combn(nvars, ncausers))[[2]] * swingnumber, ncol = ncausers)
for (i in as.integer(1:dim(combn(nvars, ncausers))[[2]])) {
for (j in as.integer(1:swingnumber)) {
swingedindependents[(i - 1) * swingnumber + j, ] <- independents[, i]
}
}
independentsdependents <- cbind(swingedindependents, dependents)
others <- matrix(, nrow = dim(combn(nvars, ncausers))[[2]] * swingnumber, ncol = nvars - ncausers - ndependents)
for (i in as.integer(1:((dim(combn(nvars, ncausers))[[2]]) * swingnumber))) {
others[i, ] <- setdiff(seq(1:nvars), independentsdependents[i, ])
}
causalitiestemplate <- cbind(independentsdependents, others)
causalitiestemplate
}
causalitycombinations(3,1,1)
# [,1] [,2] [,3]
#[1,] 1 2 3
#[2,] 1 3 2
#[3,] 2 1 3
#[4,] 2 3 1
#[5,] 3 1 2
#[6,] 3 2 1
Step2: Append the data to the combinations
(one can append multiple columns, I added only 1 for simplicity)
set.seed(1)
mydataframer <- cbind(causalitycombinations(3,1,1), rnorm(6))
mydataframer
# [,1] [,2] [,3] [,4]
#[1,] 1 2 3 -0.6264538
#[2,] 1 3 2 0.1836433
#[3,] 2 1 3 -0.8356286
#[4,] 2 3 1 1.5952808
#[5,] 3 1 2 0.3295078
#[6,] 3 2 1 -0.8204684
Step3: Apply the function via lapply while taking into account number of rows of the composite dataframe
lapply(1: dim(mydataframer)[[1]], function(x) {2*mydataframer[x,4] + 5})
# 3.747092
# 5.367287
# 3.328743
# 8.190562
# 5.659016
# 3.359063
That is it.
By the way, ?irr::agree help file states that nxm ratings matrix/dataframe is "n subjects, m raters". Hence, questioner may design this better via:
annotations <- read.table(text = "Rater1 Rater2 Rater3
Subject1 a b c
Subject2 a b b
Subject3 a b c", header = TRUE, stringsAsFactors = FALSE)
annotations
# Rater1 Rater2 Rater3
# Subject1 a b c
# Subject2 a b b
# Subject3 a b c
Also, one thing still needs to be clarified whether questioner want to loop over all such combinations of annotations. If that's the case, i.e.,
annotations
# Rater1 Rater2 Rater3
# Subject1 a a a
# Subject2 a a a
# Subject3 a a a
annotations
# Rater1 Rater2 Rater3
# Subject1 a a b
# Subject2 a a a
# Subject3 a a a
annotations
# Rater1 Rater2 Rater3
# Subject1 a a c
# Subject2 a a a
# Subject3 a a a
annotations
# Rater1 Rater2 Rater3
# Subject1 a b a
# Subject2 a a a
# Subject3 a a a
# .... after consuming all Subject1 possibilities, this time consuming Subject2 possibilities,
annotations
# Rater1 Rater2 Rater3
# Subject1 a a a
# Subject2 a a b
# Subject3 a a a
and then Subject3 possibilities, and thereby collecting all the possibilities of agreements, then the problem changes completely.
The irr::agree function designed for multiple rows. Observe from its help file:
data(video)
video
# rater1 rater2 rater3 rater4
# 1 4 4 3 4
# 2 4 4 4 5
# ..............................
# 20 4 5 5 4
agree(video) # Simple percentage agreement
# Percentage agreement (Tolerance=0)
# Subjects = 20; Raters = 4; %-agree = 35
agree(video, 1) # Extended percentage agreement
# Percentage agreement (Tolerance=1)
# Subjects = 20; Raters = 4; %-agree = 90
whereas in the case where the questioner wanna apply row-wise agrees (only 1 subject!), the %-agree is always 0:
agree(video[1,])
# Percentage agreement (Tolerance=0)
# Subjects = 1; Raters = 4; %-agree = 0
...
agree(video[20,])
# Percentage agreement (Tolerance=0)
# Subjects = 1; Raters = 4; %-agree = 0
来源：https://stackoverflow.com/questions/40516059/how-to-apply-a-function-to-all-combinations-of-rows-in-a-data-frame