Hi, Thanks for the help! The prop.table definitely will save some time but my question is how would you actually build the "mat" matrix.
What I have now is 1000 matrices that are nx1 which have a predicted value for each of the n observations. I want to count then how many elements are really in class 1, but were classified as 1, as 2, as 3, etc... What I need to do is build what we have for "mat" below. Thanks! Greg On Oct 6, 2010, at 1:46 PM, Tom Fletcher wrote: > I think what you are looking for is > > ?table > and/or > ?prop.table > > So, let's say you have two matrices: ACTUAL and CLASS, you can ... > > table(ACTUAL, CLASS) > > > > Or, diag(1-prop.table(table(ACTUAL, CLASS), 1)) to get row percentages > and take the diagonal. > > So, using your example: > > # table() as above would render the following matrix > mat <- matrix(c(100, 25, 10, 250, 100, 5, 50, 25, 40), 3) > > and prop.table() renders: > >> prop.table(mat, 1) > [,1] [,2] [,3] > [1,] 0.2500000 0.6250000 0.1250000 > [2,] 0.1666667 0.6666667 0.1666667 > [3,] 0.1818182 0.0909091 0.7272727 > > In the above, the diagonal is the % correctly classified. > >> diag(1-prop.table(mat, 1)) > [1] 0.7500000 0.3333333 0.2727273 > > > TF > > > > -----Original Message----- > From: r-help-boun...@r-project.org [mailto:r-help-boun...@r-project.org] > On Behalf Of Gregory Ryslik > Sent: Wednesday, October 06, 2010 10:19 AM > To: R Help > Subject: [R] misclassification matrix > > HI Everyone, > > I am working with the following situation. I have n observations and j > possible outcomes and each one of the n observations is assigned a class > from 1 to j. Furthermore, this process is done m times (for some large > m > 1000). > > What I want to do is create a misclassification matrix which tells me > for each one of the possible classes, how many observations were > classified correctly, and then how many were incorrectly at each level. > Here is an example of what I mean > > 0 1 2 Misclassification > 0 100 250 50 .75 > 1 25 100 25 .333333 > 2 10 5 40 .2727273 > > > For each one of the 1 to j elements, I can use a nested for loop to > count how many were classified as 0, are in 1, etc and then construct > such matrix. Thus for each element I have j comparisons and then I have > j total rows leading to an O(j^2) running time. > > Any way I can avoid such a for loop and perhaps make it run a bit > quicker? If not, any ideas then at least how to avoid the double for > loop and make the code more aesthetically pleasing? > > As always, thank you for your help! > > Kind regards, > Greg > ______________________________________________ > R-help@r-project.org mailing list > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide > http://www.R-project.org/posting-guide.html > and provide commented, minimal, self-contained, reproducible code. ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
