KolmogorovMinDist {RobLoxBioC} | R Documentation |
Generic function for computing minimum Kolmogorov distance for biological data.
KolmogorovMinDist(x, D, ...) ## S4 method for signature 'matrix,Norm' KolmogorovMinDist(x, D, mad0 = 1e-4) ## S4 method for signature 'AffyBatch,AbscontDistribution' KolmogorovMinDist(x, D, bg.correct = TRUE, pmcorrect = TRUE, verbose = TRUE) ## S4 method for signature 'beadLevelData,AbscontDistribution' KolmogorovMinDist(x, D, log = FALSE, what = "Grn", probes = NULL, arrays = NULL)
x |
biological data. |
D |
object of class |
... |
additional parameters. |
mad0 |
scale estimate used if computed MAD is equal to zero. Median and MAD are used as start parameter for optimization. |
bg.correct |
if |
pmcorrect |
if |
verbose |
logical: if |
log |
if |
what |
character string specifying which intensities/values to summarize;
see |
probes |
Specify particular probes to summarize. If left |
arrays |
integer (scalar or vector) specifying the strips/arrays to summarize.
If |
The minimum Kolmogorov distance is computed for each row of a matrix, each Affymetrix probe, or each Illumina bead, respectively.
So far, only the minimum distance to the set of normal distributions can be computed.
List with components dist
containing a numeric vector
or matrix with minimum Kolmogorov distances and n
a numeric vector
or matrix with the corresponding sample sizes.
Matthias Kohl Matthias.Kohl@stamats.de
Huber, P.J. (1981) Robust Statistics. New York: Wiley.
Rieder, H. (1994) Robust Asymptotic Statistics. New York: Springer.
set.seed(123) # to have reproducible results for package checking ## matrix method for KolmogorovMinDist ind <- rbinom(200, size=1, prob=0.05) X <- matrix(rnorm(200, mean=ind*3, sd=(1-ind) + ind*9), nrow = 2) KolmogorovMinDist(X, D = Norm()) ## using Affymetrix data data(SpikeIn) probes <- log2(pm(SpikeIn)) (res <- KolmogorovMinDist(probes, Norm())) boxplot(res$dist) ## \donttest because of check time ## using Affymetrix data library(affydata) data(Dilution) res <- KolmogorovMinDist(Dilution[,1], Norm()) summary(res$dist) boxplot(res$dist) plot(res$n, res$dist, pch = 20, main = "Kolmogorov distance vs. sample size", xlab = "sample size", ylab = "Kolmogorov distance", ylim = c(0, max(res$dist))) uni.n <- min(res$n):max(res$n) lines(uni.n, 1/(2*uni.n), col = "orange", lwd = 2) legend("topright", legend = "minimal possible distance", fill = "orange") ## Illumina bead level data library(beadarrayExampleData) data(exampleBLData) res <- KolmogorovMinDist(exampleBLData, Norm(), arrays = 1) res1 <- KolmogorovMinDist(exampleBLData, Norm(), log = TRUE, arrays = 1) summary(cbind(res$dist, res1$dist)) boxplot(list(res$dist, res1$dist), names = c("raw", "log-raw")) sort(unique(res1$n)) plot(res1$n, res1$dist, pch = 20, main = "Kolmogorov distance vs. sample size", xlab = "sample size", ylab = "Kolmogorov distance", ylim = c(0, max(res1$dist)), xlim = c(min(res1$n), 56)) uni.n <- min(res1$n):56 lines(uni.n, 1/(2*uni.n), col = "orange", lwd = 2) legend("topright", legend = "minimal possible distance", fill = "orange")