On Jun 1, 2011, at 9:24 PM, <jin...@ga.gov.au> <jin...@ga.gov.au> wrote:
Please note that predicted1 and predicted2 are two sets of
predictions instead of predictors. As you can see the predictions
with only two levels, 1 is for hard and 2 for soft.
Yes, I (very clearly I think) saw that.
I need to assess which one is more accurate. Hope this is clear now.
Thanks.
Jin
So how big do you want to dig your hole? AUC is not designed to be a
score for categorical variables. It's designed for a continuous
predictor. The only information in your two-way classification of
dichotomous states is in the off-axis values.... 11 to naught versus
11 to 2. Other than that you have total agreement. Not much to work on.
--
david.
-----Original Message-----
From: David Winsemius [mailto:dwinsem...@comcast.net]
Sent: Thursday, 2 June 2011 10:55 AM
To: Li Jin
Cc: R-help@r-project.org
Subject: Re: [R] aucRoc in caret package [SEC=UNCLASSIFIED]
Using AUC for discrete predictor variables with inly two levels
doesn't seem very sensible. What are you planning to to with this
measure?
--
David.
On Jun 1, 2011, at 8:47 PM, <jin...@ga.gov.au> <jin...@ga.gov.au>
wrote:
Hi all,
I used the following code and data to get auc values for two sets of
predictions:
library(caret)
table(predicted1, trainy)
trainy
hard soft
1 27 0
2 11 99
aucRoc(roc(predicted1, trainy))
[1] 0.5
table(predicted2, trainy)
trainy
hard soft
1 27 2
2 11 97
aucRoc(roc(predicted2, trainy))
[1] 0.8451621
predicted1:
1 1 2 2 2 1 2 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 1 2 2 2 2 2 2 2 2 2 1 2
2 2 2 2 1 2 2 2 2 1 1 2 2 2 2 2 1 2 2 2 2 2 1 2 2 2 2 2 2 2 2 2 2 2
2 2 2 1 2 2 2 2 2 2 2 1 2 2 2 2 2 1 1 1 2 2 1 1 1 2 2 2 2 2 1 1 2 2
2 2 2 2 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
predicted2:
1 1 2 1 2 1 2 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 1 2 2 2 2 2 2 2 2 1 1 2
2 2 2 2 1 2 2 2 2 1 1 2 2 2 2 2 1 2 2 2 2 2 1 2 2 2 2 2 2 2 2 2 2 2
2 2 2 1 2 2 2 2 2 2 2 1 2 2 2 2 2 1 1 1 2 2 1 1 1 2 2 2 2 2 1 1 2 2
2 2 2 2 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
trainy:
hard hard hard soft soft hard hard hard hard soft soft soft soft
soft soft hard soft soft soft soft soft soft hard soft soft soft
soft soft soft soft soft soft hard soft soft soft soft soft hard
soft soft soft soft hard hard soft soft soft hard soft hard soft
soft soft soft soft hard soft soft soft soft soft soft soft soft
hard soft soft soft soft soft hard soft soft soft soft soft soft
soft hard soft soft soft hard hard hard hard hard soft soft hard
hard hard soft hard soft soft soft hard hard soft soft soft soft
soft hard hard hard hard hard hard hard soft soft soft soft soft
soft soft soft soft soft soft soft soft soft soft soft hard soft
soft soft soft soft soft soft soft
Levels: hard soft
Sys.info()
sysname
release version nodename
"Windows" "XP" "build
2600, Service Pack 3" "PC-60772"
machine
"x86"
I would expect predicted1 is more accurate that the predicted2. But
the auc values show an opposite. I was wondering whether this is a
bug or I have done something wrong. Thanks for your help in advance!
Cheers,
Jin
____________________________________
Jin Li, PhD
Spatial Modeller/Computational Statistician
David Winsemius, MD
West Hartford, CT
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