Description Usage Arguments Details Value References Examples
Estimates the limits of quantification based on the asymptotes coefficients.
1 2 |
x |
a |
subset.list |
list of analytes to estimate.
Default |
low.asymp |
a number or character specifying the low
asymptote coefficient in the model.
Default |
high.asymp |
a number or character specifying the high
asymptote coefficient.
Default |
lowci |
specify the lowest limit if exists for asymptote,
only applies if |
highci |
specify the highest limit if exists for asymptote,
only applies if |
inter.method |
interval method for estimating interval LOQ ('prediction' or 'confidence') |
level |
0 to 1 value specifying level of confidence. Default 0.95. |
If low.asymp
(high.asymp
) is specified
lowci
(highci
) must be -Inf
(Inf
).
When lowci
(highci
) is specified asymptote argument
must be NULL
. When low.asymp
and lowci
arguments are the default values,
the funtion assumes that LOQs are maximum and minimum values of data.
If low.asymp
(high.asymp
) or lowci
(highci
)
arguments are specified but it is not possible to estimate the LOQ
(e.g., coefficient position is not well specified or estimated values
are beyond observed data) the function
estimates the LOQs based on maximum and minimum values.
When the background method is the constraint one, the LLOQ is the
concentration
value of the log10(Background MFI mean) and low.asymp and lowci doesn't
apply.
Object of class loq
.
Quinn CP, Semenova VA, Elie CM et al. (2002). Specific, Sensitive, and Quantitative Enzyme-Linked Immunosorbent Assay for Human Immunoglobulin G Antibodies to Anthrax Toxin Protective Antigen. Emerg Infect Dis 8 (10),1103-10
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 | # Load data and estimate models
data(ecdata)
data(mfidata)
dat <- mfidata[mfidata$plate=="plate_1" & mfidata$analyte=="FGF",]
sdf <- data_selection(dat, ecdata)$plate_1
igmodels <- scluminex("plate_1",sdf$standard, sdf$background,
lfct="SSl4",
bkg="ignore",
fmfi="mfi",
verbose=FALSE)
# All default arguments
loq_interval(igmodels)
# Low asymptote coefficient of the model is 2
loq_interval(igmodels, low.asymp = 2)
# Low asymptote coefficient of the model is 2 and high is 3
loq_interval(igmodels, low.asymp = 2, high.asymp = 3)
# Fix to 2 and 3 the lower and upper asymptote
loq_interval(igmodels, lowci=2, highci=3)
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