selecting only corresponding categories from a confusion matrix
On Nov 29, 2010, at 8:32 AM, drflxms wrote:
Dear R colleagues,
as a result of my calculations regarding the inter-observer-
variability
in bronchoscopy, I get a confusion matrix like the following:
0 1 1001 1010 11
0 609 11 54 36 6
1 1 2 6 0 2
10 14 0 0 8 4
100 4 0 0 0 0
1000 23 7 12 10 5
1001 0 0 4 0 0
1010 4 0 0 3 0
1011 1 0 1 0 2
11 0 0 3 3 1
110 1 0 0 0 0
1100 2 0 0 0 0
1110 1 0 0 0 0
The first column represents the categories found among observers, the
top row represents the categories found by the reference
("goldstandard").
I am looking for a way (general algorithm) to extract a data.frame
with
only the corresponding categories among observers and reference from
the
above confusion matrix. "Corresponding" means in this case, that a
category has been chosen by both: observers and reference.
In this example corresponding categories would be simply all
categories
that have been chosen by the reference (0,1,1001,1010,11), but
generally
there might also occur categories which are found by the reference
only
(and not among observers - in the first column).
So the solution-dataframe for the above example would look like:
0 1 1001 1010 11
0 609 11 54 36 6
1 1 2 6 0 2
1001 0 0 4 0 0
1010 4 0 0 3 0
11 0 0 3 3 1
I wasn't able to follow the confusing, er, confusion matrix
explanation but it appears from a comparison of the input and output
that you just want row indices that are the column names:
> mtx[colnames(mtx), ]
0 1 1001 1010 11
0 609 11 54 36 6
1 1 2 6 0 2
1001 0 0 4 0 0
1010 4 0 0 3 0
11 0 0 3 3 1
>
> # and the omitted
>
> mtx[!rownames(mtx) %in% colnames(mtx), ]
0 1 1001 1010 11
10 14 0 0 8 4
100 4 0 0 0 0
1000 23 7 12 10 5
1011 1 0 1 0 2
110 1 0 0 0 0
1100 2 0 0 0 0
1110 1 0 0 0 0
>
> # and their number:
>
> NROW(mtx[!rownames(mtx) %in% colnames(mtx), ])
[1] 7
All the categories found among observers only, were omitted. If the solution algorithm would include a method to list the omitted categories and to count their number as well as the number of omitted cases, it would be just perfect for me.
David Winsemius, MD West Hartford, CT