robustMahalanobis | R Documentation |
These function implement the MR method of Itzhak et al
robustMahalanobis(delta) reprodScore(x, y, method = c("pearson")) mrMethod(objectCond1, objectCond2, method = "2017")
delta |
The difference profile to compute the squared mahalanobis distance |
x |
Numeric vector to compute reproducibility score |
y |
Numeric vector to compute reproducibility score |
method |
Correlation method. Default is Pearson |
objectCond1 |
A list of |
objectCond2 |
A list of |
The squared Mahalanobis distance
The R score
The MR score of the Ithzak et al. 2016/2017
## Generate some example data library("pRolocdata") data("tan2009r1") set.seed(1) tansim <- sim_dynamic(object = tan2009r1, numRep = 4L, numDyn = 100L) data <- tansim$lopitrep control <- data[1:2] treatment <- data[3:4] ## compute delta matrix deltaMatrix <- exprs(control[[1]]) - exprs(treatment[[1]]) res <- bandle:::robustMahalanobis(deltaMatrix) ##' @examples ## Generate some example data library("pRolocdata") data("tan2009r1") set.seed(1) tansim <- sim_dynamic(object = tan2009r1, numRep = 4L, numDyn = 100L) data <- tansim$lopitrep control <- data[1:2] treatment <- data[3:4] ## compute delta matrix deltaMatrix1 <- exprs(control[[1]]) - exprs(treatment[[1]]) deltaMatrix2 <- exprs(control[[2]]) - exprs(treatment[[2]]) mr_score <- bandle:::reprodScore(deltaMatrix1, deltaMatrix2) library(pRolocdata) data("tan2009r1") set.seed(1) tansim <- sim_dynamic(object = tan2009r1, numRep = 6L, numDyn = 100L) d1 <- tansim$lopitrep control1 <- d1[1:3] treatment1 <- d1[4:6] mr1 <- mrMethod(objectCond1 = control1, objectCond2 = treatment1) plot(mr1$Mscore, mr1$Rscore, pch = 21, xlab = "MScore", ylab = "RScore")
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