Hi
I have a data frame like this:
V1 V2 V3 V4
Min. :0.01146 Min. :0.0006714 Min. :0.004912 Min. : 0
1st Qu.:0.03938 1st Qu.:0.0072805 1st Qu.:0.052719 1st Qu.:1150
Median :0.04224 Median :0.0077581 Median :0.056388 Median :1150
Mean :0.04010 Mean :0.0074669 Mean :0.052602 Mean :1173
3rd Qu.:0.04250 3rd Qu.:0.0082344 3rd Qu.:0.056388 3rd Qu.:1150
Max. :0.04282 Max. :0.0085154 Max. :0.056388 Max. :6610
V5 V6 V7 V8
Min. :5.000 Min. :0.02393 Min. :0.03079 Min. :0.03427
1st Qu.:6.000 1st Qu.:0.03849 1st Qu.:0.04493 1st Qu.:0.21231
Median :6.000 Median :0.04005 Median :0.04637 Median :0.21231
Mean :6.024 Mean :0.03998 Mean :0.04692 Mean :0.21123
3rd Qu.:6.000 3rd Qu.:0.04116 3rd Qu.:0.05285 3rd Qu.:0.21231
Max. :7.000 Max. :0.04477 Max. :0.05285 Max. :0.21231
V9 V10 V11
Min. :-2.000 Min. : 0.0 Min. : 0.00
1st Qu.: 0.000 1st Qu.:136.1 1st Qu.:12.00
Median : 1.000 Median :136.1 Median :17.32
Mean : 1.185 Mean :131.6 Mean :15.28
3rd Qu.: 2.000 3rd Qu.:136.1 3rd Qu.:17.90
Max. :10.000 Max. :136.1 Max. :22.00
The following command using outlier{randomForest} generates a lot of NAs. I
am not sure under what conditions they were thus got ?
t0 = randomForest(x0[1:1000,])
t1 = outlier(t0)
> summary(t1)
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
-1.09600 0.09848 0.90990 1.34400 2.61000 4.67200 971.00000
Thanks,
--
Weiwei Shi, Ph.D
Research Scientist
GeneGO, Inc.
"Did you always know?"
"No, I did not. But I believed..."
---Matrix III
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