Description Usage Arguments Details Value Examples
Estimates the inverse of the funtion. Given a response value, estimates the corresponding concentration value and the standard error.
1 2 |
x |
a |
analyte |
the specific analyte to estimate the invert values.
Default |
yvalue |
value of the response model to estimate the inverse in log10 scale. |
ci.method |
character defining the method to be applied for estimating standard error ('delta' or 'bootstrap'). Default 'delta'. |
level |
confidence level. Default 0.95. |
seed.boot |
numeric for the seed of the bootstrap method. Only applies for bootstrap method. Default 123. |
nboot |
number of bootstrap replicates. Only applies for bootstrap method. Default 100. |
Delta method function used is deltamethod
from the msm
package.
Bootstrap method generates nboot
response vectors
(assuming normality) and fit the same model with
original concentration data. The confidence interval is calculated
by the percentile method specified in the level
argument
(1-level
/2, 1-(1-level)
/2).
A data.frame
with the following components:
MFI variable, the yvalue
response vector
Fit of the concentration, concentration estimation of
the yvalue
vector
Fit of the concentration.lci, lower confidence bounds for the concentration estimation
Fit of the concentration.uci, upper confidence bounds for the concentration estimation
Fit of the concentration.se, estimation of the Standard
Error of the concentration. If ci.method
'bootstrap' is NA
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 | # Load data
data(ecdata)
data(mfidata)
dat <- mfidata[mfidata$plate=="plate_1" & mfidata$analyte=="FGF",]
# Estimate models
sdf <- data_selection(dat, ecdata)[[1]]
igmodels <- scluminex("plate_1",sdf$standard, sdf$background,
lfct="SSl4", bkg="ignore", fmfi="mfi", verbose=FALSE)
# Delta
invest(igmodels, "FGF", c(2, 2.5, 3), "delta")
# Bootstrap
invest(igmodels, "FGF" ,c(2, 2.5, 3), "bootstrap", nboot=10)
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