adjBy2ptReg | R Documentation |
adjBy2ptReg
takes data within window defined by 'lims' and determines linear transformation so that these points get the regression characteristics 'regrTo',
all other points (ie beyond the limits) will follow the same transformation.
In other words, this function performs 'linear rescaling', by adjusting (normalizing) the vector 'dat' by linear regression so that points falling in 'lims'
(list with upper & lower boundaries) will end up as 'regrTo'.
adjBy2ptReg(
dat,
lims,
regrTo = c(0.1, 0.9),
refLines = NULL,
silent = FALSE,
debug = FALSE,
callFrom = NULL
)
dat |
numeric vector, matrix or data.frame |
lims |
(list, length=2) should be list giving limits (list(lo=c(min,max),hi=c(min,max)) in data allowing identifying which points will be used for determining slope & offset |
regrTo |
(numeric, length=2) to which characteristics data should be regressed |
refLines |
(NULL or integer) optional subselection of lines of dat (will be used internal as refDat) |
silent |
(logical) suppress messages |
debug |
(logical) display additional messages for debugging |
callFrom |
(character) allow easier tracking of messages produced |
This function returns a matrix (of same dimensions as inlut matrix) with normalized values
normalizeThis
set.seed(2016); dat1 <- round(runif(50,0,100),1)
## extreme values will be further away :
adjBy2ptReg(dat1,lims=list(c(5,9), c(60,90)))
plot(dat1, adjBy2ptReg(dat1, lims=list(c(5,9),c(60,90))))
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