how to calculate mean of erased outlier and insert them in their place

dear all,

using the code "rmoutlier", i was able to detect outliers as i intended.

But instead of deleting the outlier and thus making the sample size smaller, i was planning to calculate a mean at the location of the outlier using pre- & post-outliers value.

Situation)

I had a value W with idx variables.

Using rmoutlier and 'quartiles', i deleted all the outliers and got a new vector W_rmoutlers (with smaller amout of arrays due to deletion of outliers) and OutlierPosition.

OutlierPosition is another vector product that shows which value within W(idx) were outliers with 1/0. 1 being outlier and 0 being non-outlier.

ex)

[W_rmoutliers,OutlierPosition] = rmoutliers(W(idx),'quartiles')

W_rmoutliers = [2 2 2 2 3 3 3 3 1 1 2 2 5 5 3 3 .................]

OutlierPosition = [0 0 0 0 0 0 0 1 1 0 0 0 0 1 1 1 1 0 0 0 0 1 1 1 0 0 1 0 0 0 1 1 ......................]

searching)

using find code, i was able to locate the position of deleted outlier

OutlierPosition2 = find(OutlierPosition)

OutlierPosition2 = [8 9 14 15 16 .......]

As you may see, there are outliers that have been found in succesive order.

How may i calculte the mean values using pre- & post-OutlierPosition2 value

and place the calculated mean value back in the erased position of the original vector?

Thanks all.

[Tommy]

What do you mean by pre- and post- outlier value?

You can get the outliers with

W(OutlierPosition)

and you can find the mean value of these outliers with

mean(W(OutlierPosition))

If you want to replace every outlier in W with this mean value, you can use

W(OutlierPosition) = mean(W(OutlierPosition))