| histcutoffs {phyloarray} | R Documentation |
Calculate cutoff values for standard deviation/signal intensities for badspots. A bad spot has high sd/signal intensity. The cutoff value is based on a fivenum/boxplot analysis.
histcutoffs(datalist, cutat=1.7)
datalist |
An object of type phyloarray for which the
cutoffs should be calculated. |
cutat |
A value giving how many times the length of the boxplot
the cutoff should be set. This is the coef-argument of the
boxplot.stats function. |
For the function, the standard deviation/signal values are calculated
and log-transformed. Using the log-transformed values, a boxplot is
calculated, with the coefficient for outliers at cutat times
the length of the box itself.
Concerning cutat: If(!) the log-transformed values are normal
distributed, setting cutat to 1.0, means having a certainty of
97.7250% of no false negatived. 1.35 has 99.3890% certainty, 1.7 has
99.8650% certainty and 2.5 has 99.9968% certainty of no false
negatives. It is stressed that these values are only true for normal
distributions!
The return value is the object datalist of class phyloarray, but with one
attribute added, i.e. "cutoff"
Kurt Sys (kurt.sys@advalvas.be)
# load data this-is-escaped-codenormal-bracket40bracket-normal, i.e. this-is-escaped-codenormal-bracket41bracket-normal data(Phylodata) # show some histograms hist(scans$Rsd[,1]/scans$R[,1], nclass=10000, xlim=c(0,5)) hist(scans$Gsd[,1]/scans$G[,1], nclass=10000, xlim=c(0,1)) # calculate cutoff values scans <- histcutoffs(scans, cutat=2.5) # the cutoff values attr(scans, "cutoff") # which gives the same as attributes(scans)$cutoff