wrMisc (version 1.2.3)

normalizeThis: Normalize data in various modes

Description

Generic normalization of 'dat' (by columns), multiple methods may be applied. The choice of normalization procedures must be done with care, plotting the data before and after normalization may be critical to understandig the initial data structure and the effect of the procedure applied. Inappropriate methods chosen may render interpretation of (further) results incorrect. Normalization using the method vsn runs justvsn from vsn (this requires a minimum of 42 rows of input-data). Note : Depending on the procedure chosen, the normalized data may appear on a different scale.

Usage

normalizeThis(
  dat,
  method = "mean",
  refLines = NULL,
  refGrp = NULL,
  trimFa = NULL,
  quantFa = NULL,
  expFa = NULL,
  silent = FALSE,
  callFrom = NULL
)

Arguments

dat

matrix or data.frame

method

(character) may be "mean","median","NULL","none", "trimMean", "slope", "exponent", "slope2Sections", "vsn"; When NULL or 'none' is chosen the input will be returned

refLines

(NULL or numeric) allows to consider only specific lines of 'dat' when determining normalization factors (all data will be normalized)

refGrp

Only the columns indicated will be used as reference, default all columns (integer or colnames)

trimFa

(numeric, length=1) additional parameters for trimmed mean

quantFa

(numeric, length=2) additional parameters for quantiles to use with method='slope'

expFa

(numeric, length=1) additional parameters for method='exponent'

silent

(logical) suppress messages

callFrom

(character) allow easier tracking of message produced

Value

matrix of normalized data

See Also

exponNormalize, adjBy2ptReg, justvsn

Examples

# NOT RUN {
set.seed(2015); rand1 <- round(runif(300)+rnorm(300,0,2),3)
dat1 <- cbind(ser1=round(100:1+rand1[1:100]),ser2=round(1.2*(100:1+rand1[101:200])-2),
  ser3=round((100:1+rand1[201:300])^1.2-3))
dat1 <- cbind(dat1,ser4=round(dat1[,1]^seq(2,5,length.out=100)+rand1[11:110],1))
dat1[dat1 <1] <- NA
  summary(dat1)
  head( .normalize(dat1,"mean",list()))
  dat1[c(1:5,50:54,95:100),]
no1 <- normalizeThis(dat1,refGrp=1:3,meth="mean")
no2 <- normalizeThis(dat1,refGrp=1:3,meth="trimMean",trim=0.4)
no3 <- normalizeThis(dat1,refGrp=1:3,meth="median")
no4 <- normalizeThis(dat1,refGrp=1:3,meth="slope",quantFa=c(0.2,0.8))
dat1[c(1:10,91:100),]
cor(dat1[,3],rowMeans(dat1[,1:2],na.rm=TRUE),use="complete.obs")             # high
cor(dat1[,4],rowMeans(dat1[,1:2],na.rm=TRUE),use="complete.obs")             # bad
cor(dat1[c(1:10,91:100),4],rowMeans(dat1[c(1:10,91:100),1:2],na.rm=TRUE),use="complete.obs")
cor(dat1[,3],rowMeans(dat1[,1:2],na.rm=TRUE)^ (1/seq(2,5,length.out=100)),use="complete.obs")
# }