I have been looking for a solution to this problem for days, so I hope you can help me.

I have several datasets containing hundreds of variables, measured at the same time point and with the same method.

Some of these variables have been measured more often and assume consistent values. Some were measured less often and have highly disperse values.

Example:

I removed the outliers using
in the
package in R.

I need an estimator that can tell me the value assumed by each variable during my experiment.

Therefore, I calculated

I have been looking for days without success for a method which I could use to identify the variable that assume values to variable/disperse to be considered reliable. In other words, I'm looking for a method I could use to state that using that specific method I did not obtain consistent values for a specific variable.

For example in the above data I would say that I cannot trust the measurement performed for
.

These data come from biological experiments, and having a variable with such huge difference, as

Therefore, it has to come from a methodological error.

I would expect that all values of each variable are more or less similar. Therefore, if I have a variable that has values too different compared to a defined range (which is defined considering an accepted dispersion range) I will remove it.

Maybe the range can be defined by and estimation of the "average" mad of all variables, or something like that. This could be an estimation of the variability I could expect and accept. But I would need a test to verify that.

I hope I managed to explain my problem.

Do you have any suggestion?

Thank you very much for your help.

I have several datasets containing hundreds of variables, measured at the same time point and with the same method.

Some of these variables have been measured more often and assume consistent values. Some were measured less often and have highly disperse values.

Example:

Code:

```
v1 2 1.8 1.5 1.9 2.1 1.78 1.95 2.0 2.1
v2 2 100 -5.2
v3 1 -1.3 -2 2.3
v4 1 1.5 1.6 1.9 2.1 2.0 2.4 -1.1 2.3 1.5 1.6 1.9 1.8 1.6
```

Code:

`adjboxStats`

Code:

`robustbase`

I need an estimator that can tell me the value assumed by each variable during my experiment.

Therefore, I calculated

**median**and**Median absolute deviation**(mad), since they are robust estimators not influenced by outliers.I have been looking for days without success for a method which I could use to identify the variable that assume values to variable/disperse to be considered reliable. In other words, I'm looking for a method I could use to state that using that specific method I did not obtain consistent values for a specific variable.

For example in the above data I would say that I cannot trust the measurement performed for

Code:

`v2`

These data come from biological experiments, and having a variable with such huge difference, as

*v2*, doesn't have sense.Therefore, it has to come from a methodological error.

I would expect that all values of each variable are more or less similar. Therefore, if I have a variable that has values too different compared to a defined range (which is defined considering an accepted dispersion range) I will remove it.

Maybe the range can be defined by and estimation of the "average" mad of all variables, or something like that. This could be an estimation of the variability I could expect and accept. But I would need a test to verify that.

I hope I managed to explain my problem.

Do you have any suggestion?

Thank you very much for your help.

Last edited: