This is my a part of my data set
> D[1:15,c(1,5:10)]
X. media IE.2005 IE.2006 IE.2007 IE.2008 IE.2009 IE.2010
1 1108 22.00000 60.0 39 4.0 8.0 16.0 5.0
2 1479 110.00000 NA NA 53.0 1166.0 344.8 110.0
3 1591 86.60000 247.0 87 95.0 94.0 81.0 76.0
4 3408 807.00000 302.0 322 621.0 1071.0 1301.0 1225.0
5 3423 9.00000 NA NA NA 410.8 7.0 11.0
6 3872 103.25000 288.6 113 116.0 90.0 94.0 12036.6
7 5823 73.00000 117.0 70 80.0 74.0 69.0 72.0
8 6051 73.00000 NA NA NA NA 60.0 86.0
9 8099 125.16667 196.0 161 150.0 94.0 72.0 78.0
10 8100 70.00000 NA NA NA NA 48.0 92.0
11 10640 67.33333 1256.6 1152 664.2 74.0 77.0 51.0
12 12600 2417.00000 1960.0 2383 2453.0 2506.0 2758.0 2442.0
13 14680 38.00000 30.0 61 373.6 42.0 19.0 220.8
14 14698 698.16667 553.0 664 847.0 800.0 679.0 646.0
15 17143 392.16667 323.0 322 434.0 383.0 459.0 432.0
I have done multiple imputation and now I have some outliers which I would
like to replace with the mean of this row or if it is possible with the mean of
the previos and the next value of this row, I mean for instance:
value 1 - Outlier- Value 2
I would like to replace the outlier with the mean of value 1 and value2, the
problem is that this values could be NA ( NA after the imputation because they
don't exist), in this case I would like to replace outlier with the mean of the
row.
An other problem I have is to detect correctly outlier values, for instance in
this example of data set for X=3872 and IE.2010, we can see an outlier, I have
thought to compare the values with the mean ( column media)
I have tried to do this code
D<-datos[, c(1,16:24)]
m<-as.matrix(D)
for( i in 1: nrow(D))
{
for( j in 5:(ncol(D)-1)) # I would change this in the new data set, because
I will have more years than 2010
{
if(!is.na(m[i,j])&& !is.na
(m[i,j+1])&&!is.na(m[i,j-1])&&!is.na(m[i,2])&&((m[i,j]/m[i,2])>4)){m[m[i,j]]<-
(m[i,j-1]+m[i,j+1])/2 # Here I would like to find the values that are much more
bigger than the mean of this row,
#if( !is.na(m[i,j])
# and replace them by the mean of the previous and the next values of the
same row.
}
}
}
D<-as.data.frame(m)
But I get a data.frame that I had previously, it changes nothing
I accept any idea.
Thanks a lot, Teresa
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