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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