Hi, I am a little puzzled by the behavior of fit.li.wong function (affy package) in conducting outlier detection. I created a matrix x <- sweep(matrix(2^rnorm(600),30,20),1,seq(1,2,len=30),FUN="+") then set x[30,20] as the outlier: x[30,20]=9999 When I ran fit.li.wong(x,outlier.detection=TRUE), x[30,20] was recognized as an outlier, but apparently it was NOT removed from the computation of theta. theta[30] is obviously affected by the single outlier. I would like to know how to fix this. Any suggestion is welcome. Yi Xing

Hi Yi, Yi Xing wrote: > Hi, > > I am a little puzzled by the behavior of fit.li.wong function (affy > package) in conducting outlier detection. I created a matrix > x <- sweep(matrix(2^rnorm(600),30,20),1,seq(1,2,len=30),FUN="+") > > then set x[30,20] as the outlier: > x[30,20]=9999 > > When I ran fit.li.wong(x,outlier.detection=TRUE), x[30,20] was > recognized as an outlier, but apparently it was NOT removed from the > computation of theta. theta[30] is obviously affected by the single > outlier. I don't see an argument for outlier.detection in fit.li.wong. If you mean remove.outliers, then I believe it is working as advertised. Using your example: > all.equal(fit.li.wong(x)$theta, fit.li.wong(x, remove.outliers=F)$theta) [1] "Mean relative difference: 2.632672" > cbind(fit.li.wong(x)$theta, fit.li.wong(x, remove.outliers=F)$theta) [,1] [,2] [1,] 2.477412 0.7790772 [2,] 2.440048 0.9618730 [3,] 2.283680 0.3710469 [4,] 2.004736 0.3537434 [5,] 2.302030 0.2815720 [6,] 2.368680 0.3209734 [7,] 2.508436 0.6738310 [8,] 2.426458 0.7175141 [9,] 2.397586 0.6339105 [10,] 2.662556 0.6344126 [11,] 2.476010 0.5114757 [12,] 2.495807 0.4771915 [13,] 2.801699 0.5022871 [14,] 2.641723 0.5031644 [15,] 3.178295 0.5674871 [16,] 3.065739 0.3789646 [17,] 2.741703 0.6520351 [18,] 2.799087 0.4864749 [19,] 2.889033 0.6559938 [20,] 2.841164 0.6615991 [21,] 2.825730 0.5604023 [22,] 3.030698 0.7263582 [23,] 2.839171 0.5869337 [24,] 2.751788 1.2154618 [25,] 3.026560 0.8351068 [26,] 3.215382 0.5823551 [27,] 3.051072 0.6278876 [28,] 3.350610 0.5773556 [29,] 3.841350 2.9516553 [30,] 574.199766 2235.8471717 I think there is a difference between what you expect remove.outliers to do and what it actually does (e.g., remove an outlier from the computation of theta vs remove an outlier from a dataset and pretend it never existed). Best, Jim > > I would like to know how to fix this. Any suggestion is welcome. > > Yi Xing > > _______________________________________________ > Bioconductor mailing list > Bioconductor at stat.math.ethz.ch > https://stat.ethz.ch/mailman/listinfo/bioconductor > Search the archives: http://news.gmane.org/gmane.science.biology.informatics.conductor -- James W. MacDonald, M.S. Biostatistician Affymetrix and cDNA Microarray Core University of Michigan Cancer Center 1500 E. Medical Center Drive 7410 CCGC Ann Arbor MI 48109 734-647-5623