hillConcCorrect {braidrm} | R Documentation |
Hill-Based Concentration Correction
Description
Estimates actual underlying concentrations leading to a given set of response measurements based on the assumption that actual concentrations are log-normally distributed around target concentrations, response errors are normally distributed, and the actual underlying relationship between concentration and response is represented by the given Hill dose-response model.
Usage
hillConcCorrect(conc, act, parv, sigr = 1)
Arguments
conc |
a vector of expected or target concentrations, around which actual concentrations are assumed to be log-normally distributed |
act |
a vector of response values |
parv |
a four-parameter vector specifying a Hill model as described in |
sigr |
the estimated ratio of the noises in response- and log (base10) concentration-space |
Details
Suppose that is a given target concentration, and
is the actual concentration in given well, plate, or
condition. Suppose also that
is the actual response that would result from the concentration in the given Hill
dose-response model, and
is the measured response value. This function assumes that
for some , where
is a normal distribution, and
is the ratio specified by the parameter
sigr
.
Based on these assumptions, the function uses Bayes' rule to calculate the maximum likelihood estimate of for every given
value of
and
.
Value
A vector of concentrations representing the maximum likelihood estimates of the actual concentrations which produced the given responses.
Author(s)
Nathaniel R. Twarog
See Also
Examples
data(es8olatmz)
drv <- es8olatmz$compound2=="DMSO"
hfit <- findBestHill(act~conc1,es8olatmz[drv,],defaults=c(0,-2.7))
drvpos <- drv & es8olatmz$conc1>0
cconc <- hillConcCorrect(es8olatmz$conc1[drvpos],es8olatmz$act[drvpos],
coef(hfit$allfits[[hfit$bestModIdx]]),sigr=1)