[R] How to ignore data

Steve Sidney sbsidney at mweb.co.za
Mon Dec 13 18:58:18 CET 2010

Thanks for the comments

Please see my reply to Stavros - the counts represent organisms and btw 
both mean and the median are virtually unaffected by the removal of 
these valuse.

Furthermore, experience rather than statistics indicates that these 
values are in fact gross errors and as you of course mention I think one 
can quite safely remove them.

I totally agree about the question of what is an outlier but since these 
results are obtained from a Proficiency Testing programme, we are pretty 
sure what the aniticpated results. At least the range and in this case 
these values are considered errors.


On 2010/12/13 07:09 PM, Bert Gunter wrote:
>>> Values to be ignored
>>> 0 - zero and 1 this is in addition to NA (null)
>>> The reason is that I need to use the log10 of the values when performing
>>> the calculation.
>>> Currently I hand massage the data set, about a 100 values, of which less
>>> than 5 to 10 are in this category.
> This is probably a bad idea, perhaps even a VERY bad idea, though
> without knowing the details of what you are doing, one cannot be sure.
> The reason is that by removing these values you may be biasing the
> analysis. For example, if they are values that are below some
> threshhold LOD (limit of detection) they are censored, and this
> censoring needs to be explicitly accounted for (e.g. with the survival
> package). If they represent in some sense "unusual" values (some call
> these "outliers", a pejorative label that I believe should be avoided
> given all the scientfic and statistical BS associated with the term),
> one is then bound to ask, "How unusual? Why unusual? What do they tell
> us about the scientific questions of concern?" If they are just
> "errors" of some sort (like negative incomes or volumes), well then,
> you're probably OK removing them.
> The reason I mention this is that I have seen scientists too often use
> poor strategies for analyzing censored data, and this can end up
> producing baloney conclusions that don't replicate. It's a somewhat
> subtle, but surprisingly common issue (due to measurement limitations)
> that most scientists are neither trained to recognize nor deal with.
> So their problematical approaches are understandable, but unfortunate.
>   Therefore take care ... and, if necessary, consuilt your local
> statistician for help.
> -- Bert
>>> The NA values are NOT the problem
>>> What I was hoping was that I did not have to use a series of if and
>>> ifelse statements. Perhaps there is a more elegant solution.
>>   It would help to have a more precise/reproducible example, but if
>> your data set (a data frame) is d, and you want to ignore cases where
>> the response variable x is either 0 or 1, you could say
>>   ds<- subset(d,!x %in% c(0,1))
>> Some modeling functions (such as lm()), but not all of them, have
>> a 'subset' argument so you can provide this criterion on the fly:
>>   lm(...,subset=(!x %in% c(0,1)))
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>> and provide commented, minimal, self-contained, reproducible code.

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