Nothing

```
##Name: rm.outliers
##Description: removes outling sample groups based on their distance from the mean of sample groups
##O/S: for R
##Date: 2/17/2010
##Author: Shannon M. Bell
##Company: Michigan State University
##notes:
#this function removes the outlier groups for Quality Control purposes
#the removal is based on whatever attribute is assigned, input data is expected to have a specific format
#first column is the 'parameter' the subsequent are all data to be evaluated
#this will remove only the data where the median value of the group is > n*MAD of the all group medians
#very important that data is ordered based on parameter and that parameter is the first column of the dataframe
#updated 11/11 to permit use of any column label as parameter, validated to ensure same result as with prior version
rm.outliers<-function(data, parameter='FLATCODE', n=3, ...){
#first calculate the median of the each value by the grouping parameter to get initial.median
#times is the number of observations made for each grouping parameter obtaitained by cal length,
#this is used later on to maintain dimension
if(! is.null(parameter)){
form<-as.formula(paste('.~', parameter))
initial.median<-summaryBy(form, data=data, FUN=median, na.rm=TRUE, keep.names=TRUE)
times<-summaryBy(form, data=data, FUN=length, keep.names=TRUE)
}
r<-ncol(initial.median)
#this is the median of the parameter medians
pre.median<-apply(initial.median[,2:r], 2, median, na.rm=T)
#this is the MAD for the parameter medians (so like the stdev)
pre.mad<-apply(initial.median[,2:r], 2, mad, na.rm=T)
#this is the difference between the paramter median and the median of the parameter medians
pdiff<-sweep(initial.median[,2:r],2,FUN='-', pre.median)
#adding back on the label for parameter to the df of differences
fpdiff<-cbind(initial.median[,1], pdiff)
#This goes through and looks to see if the difference b/w the parameter median and median of parameter medians
#is beyound the tollerance n
#for example, less than n=3*MAD or ~ 3 stddev
#this is eq to saying that I am considering groups outside ~99.6% confidence interval as faulty samples.
#by setting value for indivuduals w/in cutoff to 1 it will retain the value in the multiplication step later on
#if missing data or median is outside the cutoff (ie 3MAD), the value will be NA (droped) in multiplication
listing2<-NULL
for (i in 1:nrow(pdiff)){
calls<-NULL
for (j in 1:ncol(pdiff)){
#3 stdevis is ~99.6%; 95%~1.98 stdev
#note the correction for if the value is na
if(abs(pdiff[i,j]) <= n*pre.mad[j] && is.na(pdiff[i,j]) != 1){
calls<-cbind(calls, 1)
}
else{
calls<-cbind(calls, NA)
}
}
temp<-cbind(fpdiff[i,1], as.data.frame(calls))
listing2<-rbind(listing2, temp)
}
colnames(listing2)<-colnames(fpdiff)
#repeat the rows in median info to the same dimensions as the input data based on labels
#the output is a df with the same dimensions but just 1 and na for values
f.listing2<-listing2[rep(1:nrow(listing2),times[,2]),]
#this makes the values for any sample group deemed outside the cutoffs equal to NA and wont be used for further analysis
#this is by attribute so a sample group can be outside the bounds in just one feature (e.g. assay)
ps.flats<-data[,2:r] * f.listing2[,2:r]
#adding back on labels
ps.flats<-cbind(data[,1], as.data.frame(ps.flats))
colnames(ps.flats)<-colnames(data)
ps.flats
}
###############
```

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