Ion Selective Electrodes Analysis Methods
Description
Bayesian calibration for single or multiple ISEs using R and OpenBUGS (or JAGS). Estimation of analyte activities using single ISEs or ISE arrays.
Details
The primary funtions are loadISEdata (which loads calibration and experimental data from tab-delimited text files), describeISE (uses Bayesian calibration to estimate ISE parameters from calibration data), and analyseISE (combines calibration data with experimental data in basic or standard addition format to estimate analyte concentrations).
Author(s)
Peter Dillingham [aut, cre], Christina McGraw [ctb], Aleksandar Radu [ctb], Basim Alsaedi [ctb]
Maintainer: Peter Dillingham <peter.dillingham@otago.ac.nz>
References
Dillingham, P.W., Radu, T., Diamond, D., Radu, A. and McGraw, C.M. (2012). Bayesian Methods for Ion Selective Electrodes. Electroanalysis, 24, 316-324. <doi:10.1002/elan.201100510>
Dillingham, P.W., Alsaedi, B.S.O. and McGraw, C.M. (2017). Characterising uncertainty in instrumental limits of detection when sensor response is non-linear. 2017 IEEE SENSORS, Glasgow, United Kingdom, pp. 1-3. <doi:10.1109/ICSENS.2017.8233898>
Dillingham, P.W., Alsaedi, B.S.O., Radu, A., and McGraw, C.M. (2019). Semi-automated data analysis for ion-selective electrodes and arrays using the R package ISEtools. Sensors 19(20), 4544. <doi:10.3390/s19204544>
Dillingham, P.W., Alsaedi, B.S.O., Granados-Focil, S., Radu, A., and McGraw, C.M. (2020). Establishing meaningful Limits of Detection for ion-selective electrodes and other nonlinear sensors ACS Sensors, 5, 250-257. <doi:10.1021/acssensors.9b02133>
Examples
data(LeadStdAdd)
print(LeadStdAdd)
summary(LeadStdAdd)
plot(LeadStdAdd)
example1 = describeISE(LeadStdAdd, Z =2, temperature=21)
print(example1)
summary(example1)
plot(example1)
example2 = analyseISE(LeadStdAdd, Z =2, temperature=21)
print(example2)
summary(example2)
plot(example2, ylim = c(-7, -3), xlab = "ID (Sample)",
ylab = expression(paste(log[10], " ", Pb^{paste("2","+",sep="")} )))
ISE measurements of lead in soil
Description
A data set containing emf responses for 3 ISEs measuring lead in soil at Silvermines, Ireland. Calibration data and experimental data for 17 samples (in standard addition format) are included.
Usage
data(LeadStdAdd)Format
Load example lead data as an object of type ISEdata (see function loadISEdata)
References
Dillingham, P.W., Radu, T., Diamond, D., Radu, A. and McGraw, C.M. (2012). Bayesian Methods for Ion Selective Electrodes. Electroanalysis, 24, 316-324. <doi:10.1002/elan.201100510>
Dillingham, P.W., Alsaedi, B.S.O., Radu, A., and McGraw, C.M. (2019). Semi-automated data analysis for ion-selective electrodes and arrays using the R package ISEtools. Sensors 19(20), 4544. <doi: 10.3390/s19204544>
Dillingham, P.W., Alsaedi, B.S.O., Granados-Focil, S., Radu, A., and McGraw, C.M. (2020). Establishing meaningful Limits of Detection for ion-selective electrodes and other nonlinear sensors. ACS Sensors, 5, 250-257. <doi:10.1021/acssensors.9b02133>
Examples
data(LeadStdAdd)
print(LeadStdAdd)
summary(LeadStdAdd)
plot(LeadStdAdd)
## Not run:
# Additional usage of this dataset with describeISE and analyseISE:
example1 = describeISE(LeadStdAdd, Z = 2, temperature = 21)
print(example1)
summary(example1)
plot(example1)
example2 = analyseISE(LeadStdAdd, Z = 2, temperature = 21)
print(example2)
summary(example2)
plot(example2, ylim = c(-7, -3), xlab = "ID (Sample)",
ylab = expression(paste(log[10], " ", Pb^{paste("2","+",sep="")} )))
## End(Not run)
Ion selective electrode characterisation and estimation of sample concentrations
Description
Use Bayesian calibration to estimate parameters for y = a + b log(x + c) + error, where error follows a normal distribution with mean 0 and standard deviation sigma. The limit of detection (false positive/negative method or S/N=3 method) is also estimated. These values are then used to the estimate sample concentrations.
Usage
analyseISE(data, model.path=NA, model.name=NA, Z=NA, temperature = 21,
burnin=25000, iters = 50000, chains=4, thin = 1,
a.init= NA, b.init=NA, cstar.init=NA, logc.limits = c(-8.9, -1.9),
sigma.upper = 5, diagnostic.print=FALSE, offset = 1,
alpha = 0.05, beta = 0.05, SN = NA, program="OpenBUGS")
Arguments
data
Calibration and experimental data (of class 'ISEdata'; see loadISEdata)
model.path
The directory where the BUGS model is located (defaults to 'models' sub-directory under the location of ISEtools package, e.g. '.../ISEtools/models')
model.name
The name of the BUGS model (e.g. 'Single_ISE_model.txt') (defaults are located in ISEtools package)
Z
Ionic valence (e.g. for lead, Z = 2)
temperature
temperature in degrees C
burnin
Initial number of Monte Carlo simulations to discard.
iters
Total number of iterations.
chains
Number of parallel MCMC chains
thin
Thinning rate, equal to 1/Proportion of simulations saved (e.g. thin = 10 records every tenth iteration).
a.init
Initial value for parameter a
b.init
Initial value for parameter b
cstar.init
Initial value for parameter cstar (c = cstar^10)
logc.limits
Upper and lower limits for log c initial values
sigma.upper
Upper limit for initial value of sigma
diagnostic.print
logical flag indicating whether a diagnostic printout is desired (default is F)
offset
The initial value for the slope is based on the last data point as sorted by concentration (i.e. the Nth point) and the (N - offset) data point. The default is offset = 1, corresponding to the last and second to last data points.
alpha
False positive rate used for detection threshold (not output) to calculate LOD(alpha, beta) only returned if SN = NA
beta
False negative rate used to calculate LOD(alpha, beta) only returned if SN = NA
SN
Desired signal-to-noise ratio for LOD(S/N) calculations (default is to calculate the S/N equivalent based on alpha, beta)
program
Choice of "OpenBUGS" (default and recommended for Windows or Linux) or "jags" (for macOS, see manual for warnings).
Value
analyseISE returns a list of class 'analyseISE'. Individual components include:
SampleID
Sample identification number
log10x.exp
Estimated concentration (log scale, mol/l)
ahat
Estimated value for a (from the median of the posterior distribution)
bhat
Estimated value for b (from the median of the posterior distribution)
chat
Estimated value for c (from the median of the posterior distribution)
cstarhat
Estimated value for cstar (from the median of the posterior distribution)
sigmahat
Estimated value for cstar (from the median of the posterior distribution)
LOD.info
List describing LOD method (alpha, beta or S/N) and corresponding values (alpha, beta, SN)
LOD.hat
Estimated value for the limit of detection (from the median of the posterior distribution)
<parametername>.lcl
Lower limit for the above parameters (e.g. ahat.lcl, bhat.lcl, ...) (from the 2.5th percentile of the posterior distribution)
<parametername>.ucl
Upper limit for the above parameters (from the 97.5th percentile of the posterior distribution)
LOD.Q1
25th percentile estimated value of the limit of detection
LOD.Q3
75th percentile estimated value of the limit of detection
Author(s)
Peter Dillingham, peter.dillingham@otago.ac.nz
References
Dillingham, P.W., Radu, T., Diamond, D., Radu, A. and McGraw, C.M. (2012). Bayesian Methods for Ion Selective Electrodes. Electroanalysis, 24, 316-324. <doi:10.1002/elan.201100510>
Dillingham, P.W., Alsaedi, B.S.O. and McGraw, C.M. (2017). Characterising uncertainty in instrumental limits of detection when sensor response is non-linear. 2017 IEEE SENSORS, Glasgow, United Kingdom, pp. 1-3. <doi:10.1109/ICSENS.2017.8233898>
Dillingham, P.W., Alsaedi, B.S.O., Radu, A., and McGraw, C.M. (2019). Semi-automated data analysis for ion-selective electrodes and arrays using the R package ISEtools. Sensors 19(20), 4544. <doi: 10.3390/s19204544>
Dillingham, P.W., Alsaedi, B.S.O., Granados-Focil, S., Radu, A., and McGraw, C.M. (2020). Establishing meaningful Limits of Detection for ion-selective electrodes and other nonlinear sensors ACS Sensors, 5, 250-257. <doi:10.1021/acssensors.9b02133>
Examples
# Fast-running example with only 100 MCMC iterations for testing:
data(LeadStdAdd)
example2test = analyseISE(LeadStdAdd, Z = 2, temperature = 21,
burnin=100, iters=200, chains=1, a.init=c(176, 146, -112),
b.init=c(29, 30, 31), cstar.init=c(0.26, 0.27, 0.22), program="jags")
print(example2test)
summary(example2test)
plot(example2test, ylim = c(-7, -3), xlab = "ID (Sample)",
ylab = expression(paste(log[10], " ", Pb^{paste("2","+",sep="")} )))
# Full example with 100,000 iterations (25,000 by 4 chains):
data(LeadStdAdd)
example2 = analyseISE(LeadStdAdd, Z = 2, temperature = 21)
print(example2)
summary(example2)
plot(example2, ylim = c(-7, -3), xlab = "ID (Sample)",
ylab = expression(paste(log[10], " ", Pb^{paste("2","+",sep="")} )))
ISE measurements of carbonate in seawater
Description
A data set containing emf responses for 8 ISEs measuring carbonate in seawater
Usage
data(carbonate)Format
Load example carbonate data as an object of type ISEdata (see function loadISEdata)
References
Dillingham, P.W., Alsaedi, B.S.O. and McGraw, C.M. (2017). Characterising uncertainty in instrumental limits of detection when sensor response is non-linear. 2017 IEEE SENSORS, Glasgow, United Kingdom, pp. 1-3. <doi:10.1109/ICSENS.2017.8233898>
Examples
data(carbonate)
print(carbonate)
plot(carbonate)
Ion selective electrode characterisation
Description
Use Bayesian calibration to estimate parameters for y = a + b log(x + c) + error, where error follows a nomral distribution with mean 0 and standard deviation sigma. The limit of detection is also estimated.
Usage
describeISE(data, model.path=NA, model.name = NA, Z=NA, temperature = 21,
burnin=25000, iters = 50000, chains=4, thin = 1,
a.init= NA, b.init=NA, cstar.init=NA,
logc.limits = c(-8.9, -1.9), sigma.upper = 5, diagnostic.print=FALSE, offset = 1,
alpha = 0.05, beta = 0.05, SN = NA,
keep.coda=TRUE, coda.n=1000, program="OpenBUGS")
Arguments
data
Calibration data (of class 'ISEdata'; see loadISEdata)
model.path
The directory where the BUGS model is located (defaults to 'models' sub-directory under the location of ISEtools package, e.g. '.../ISEtools/models')
model.name
The name of the BUGS model (e.g. 'Single_ISE_model.txt') (defaults are located in ISEtools package)
Z
Ionic valence (e.g. for lead, Z = 2)
temperature
temperature in degrees C
burnin
Initial number of Monte Carlo simulations to discard.
iters
Total number of iterations.
chains
Number of parallel MCMC chains
thin
Thinning rate, equal to 1/Proportion of simulations saved (e.g. thin = 10 records every tenth iteration).
a.init
Initial value for parameter a
b.init
Initial value for parameter b
cstar.init
Initial value for parameter cstar (c = cstar^10)
logc.limits
Upper and lower limits for log c initial values
sigma.upper
Upper limit for initial value of sigma
diagnostic.print
logical flag indicating whether a diagnostic printout is desired (default is FALSE)
offset
The initial value for the slope is based on the last data point as sorted by concentration (i.e. the Nth point) and the (N - offset) data point. The default is offset = 1, corresponding to the last and second to last data points.
alpha
False positive rate used for detection threshold (not output) to calculate LOD(alpha, beta) only returned if SN = NA
beta
False negative rate used to calculate LOD(alpha, beta) only returned if SN = NA
SN
Desired signal-to-noise ratio for LOD(S/N) calculations (default is to calculate the S/N equivalent based on alpha, beta)
keep.coda
Logical flag indicating whether the MCMC simulations should be returned (keep.coda = TRUE) or not (keep.coda = FALSE)
coda.n
Indicates how many simulations to return (sampled with replacement). If coda.n >= the total, all are returned.
program
Choice of "OpenBUGS" (default and recommended for Windows or Linux) or "jags" (for macOS, see manual for warnings).
Value
describeISE returns a list of class 'ISEdescription'. Individual components are:
ahat
Estimated value for a (from the median of the posterior distribution)
bhat
Estimated value for b (from the median of the posterior distribution)
chat
Estimated value for c (from the median of the posterior distribution)
cstarhat
Estimated value for cstar (c to the 0.1 power) (from the median of the posterior distribution)
sigmahat
Estimated value for cstar (from the median of the posterior distribution)
LOD.info
List describing LOD method (alpha, beta or S/N) and corresponding values (alpha, beta, SN)
LOD.hat
Estimated value for the limit of detection (from the median of the posterior distribution)
<parametername>.lcl
Lower limit for the above parameters (e.g. ahat.lcl, bhat.lcl, ...) (from the 2.5th percentile of the posterior distribution)
<parametername>.ucl
Upper limit for the above parameters (from the 95.5th percentile of the posterior distribution)
LOD.Q1
25th percentile estimated value of the limit of detection
LOD.Q3
75th percentile estimated value of the limit of detection
If keep.coda = TRUE, then these additional items are returned:
ahat.coda
Random sample (without replacement) of length coda.n from the Markov Chain Monte Carlo simulations for a
bhat.coda
Random sample (without replacement) of length coda.n from the Markov Chain Monte Carlo simulations for b
chat.coda
Random sample (without replacement) of length coda.n from the Markov Chain Monte Carlo simulations for c
sigmahat.coda
Random sample (without replacement) of length coda.n from the Markov Chain Monte Carlo simulations for sigma
cstarhat.coda
Random sample (without replacement) of length coda.n from the Markov Chain Monte Carlo simulations for cstar
LOD.coda
Random sample (without replacement) of length coda.n from the Markov Chain Monte Carlo simulations for LOD
Author(s)
Peter Dillingham, peter.dillingham@otago.ac.nz
References
Dillingham, P.W., Radu, T., Diamond, D., Radu, A. and McGraw, C.M. (2012). Bayesian Methods for Ion Selective Electrodes. Electroanalysis, 24, 316-324.
Dillingham, P.W., Alsaedi, B.S.O. and McGraw, C.M. (2017). Characterising uncertainty in instrumental limits of detection when sensor response is non-linear. 2017 IEEE SENSORS, Glasgow, United Kingdom, pp. 1-3. <doi:10.1109/ICSENS.2017.8233898>
Dillingham, P.W., Alsaedi, B.S.O., Radu, A., and McGraw, C.M. (2019). Semi-automated data analysis for ion-selective electrodes and arrays using the R package ISEtools. Sensors 19(20), 4544. <doi: 10.3390/s19204544>
Dillingham, P.W., Alsaedi, B.S.O., Granados-Focil, S., Radu, A., and McGraw, C.M. (2020). Establishing meaningful Limits of Detection for ion-selective electrodes and other nonlinear sensors ACS Sensors, 5, 250-257. <doi:10.1021/acssensors.9b02133>
Examples
# Fast-running example with only 100 MCMC iterations for testing:
data(carbonate)
example3test = describeISE(carbonate, Z = -2, SN = 3.6,
burnin=100, iters=200, chains=1,
a.init= c(-50,180,140,65,100,170,100,130),
b.init=rep(-20,8), cstar.init=rep(0.2, 8), program="jags")
print(example3test)
summary(example3test)
plot(example3test)
# Full example with 100,000 iterations (25,000 by 4 chains):
data(carbonate)
example3 = describeISE(carbonate, Z = -2, SN = 3.6)
print(example3)
summary(example3)
plot(example3)
Load ISE calibration and experimental data.
Description
Loads tab-delimited calibration and (if it exists) experimental sample data.
Usage
loadISEdata(filename.calibration, filename.experimental = NA)
Arguments
filename.calibration
The name and location of the tab-delimited calibration file
It should have the following structure:
First line (header row): ISEID log10x emf
Remaining lines (data): ISEID is an identifier for the ISE. The ISEID variables should be integers, with the lowest value equal to 1, and no gaps. That is, if there are four ISEs, they must be labeled 1, 2, 3, and 4. log10x is the log10 concentration (mol/l) of the calibration samples. The emf readings (in mV) follow.
filename.experimental
The experimental file (if there is one, otherwise keep the default filename.experimental=NA) should have one of the following structures:
basic model: The header row will include ISEID, SampleID, and emf. ISEID is defined the same way as in the calibration file. SampleID is an integer indicating which sample is being measured, and must follow the same numbering rules as ISEID. Finally, emf is the mV reading of the experimental samples for each ISE.
or
standard addition: When using the standard addition model, the experimental file will contain ISEID and SampleID as before. Two emf values are recorded: emf1 is the mV reading of the sample, and emf2 is the mV reading of the sample plus the addition. Additionally, V.s is the volume of the sample, V.add is the volume of the addition, and conc.add is the concentration (mol/l) of the addition. The units of V.s and V.add do not matter as long as they are the same.
Details
Internally calls 'ISEdata.calibration' if there is no experimental data.
Value
loadISEdata returns the following values in a list of class ISEdata:
Calibration variables:
N
Total number of calibration measurements (e.g. for 5 calibration points measured with 3 ISEs, N = 15)
R
Number of ISEs
ISEID
Identifier for the ISE
log10x
log concentration (mol/l) of calibration data
emf
emf (mV) for calibration data
Experimental variables:
M
Number of experimental samples
M.obs
Total number of experimental measurements. E.g. for 4 samples each measured by 3 ISEs, M.obs = 12. Only returned if R > 1
ISEID.exp
Identifier for the ISE for the experimental data (returned if R >1)
x.exp
Identifier for the experimental (returned if R > 1)
Basic format only:
emf.exp
emf (mV) for experimental data
Standard addition format only:
delta.emf
difference between emf1 and emf2 (mV) for experimental data
V.s
Sample volume (any units allowed but must be consistent)
V.add
Volume added to the sample
conc.add
Concentration added.
Summary variables of calibration and experimental data:
calibration.only
Indicates whether there was only calibration data (TRUE) or calibration and experimental data (FALSE)
stdadd
Indicates whether standard addition was used. Returns NA (calibration data only), FALSE (basic experimental data), or TRUE (standard addition experimental data)
data.calib
The loaded calibration data frame
data.exp
The loaded experimental data frame
Author(s)
Peter Dillingham peter.dillingham@otago.ac.nz
Examples
###
# Loading the example tab-delimited text files for the lead data
###
# 1. Find pathnames for the lead example txt files:
path.calib = paste(path.package('ISEtools'), "/extdata",
"/Lead_calibration.txt", sep="")
path.basic = paste(path.package('ISEtools'), "/extdata",
"/Lead_experimentalBasic.txt", sep="")
path.sa = paste(path.package('ISEtools'), "/extdata",
"/Lead_experimentalSA.txt", sep="")
# Load the calibration data
lead.example1 = loadISEdata(filename.calibration = path.calib)
print(lead.example1)
# ... and with experimental data, Basic format
lead.example2 = loadISEdata(filename.calibration = path.calib,
filename.experimental = path.basic)
print(lead.example2)
# ... and with experimental data, Standard Addition format
lead.example3 = loadISEdata(filename.calibration = path.calib,
filename.experimental = path.sa)
print(lead.example3)
Basic plot of ion selective electrode calibration data
Description
Plots raw ISE calibration data; data should follow a hockey stick pattern coinciding with the equation y = a + b log(x + c) + error, where error follows a normal distribution with mean 0 and standard deviation sigma.
Usage
## S3 method for class 'ISEdata'
plot(x, xlab = expression(paste(log[10], " { ", italic(x),
" }")), ylab = "emf", pch = 20, ...)
Arguments
x
ISE calibration data
xlab
Label for the x-axis
ylab
Label for the y-axis
pch
Plotting symbol for data
...
Other arguments to be passed through to plotting functions.
Value
No return value, creates plot.
Author(s)
Peter Dillingham, peter.dillingham@otago.ac.nz
See Also
Examples
data(LeadStdAdd)
plot(LeadStdAdd)
Plot ISE parameter values
Description
Plots histograms of ISE parameter values a, b, c, sigma, and LOD (alpha, beta or S/N) for the equation y = a + b log(x + c) + error, where error follows a normal distribution with mean 0 and standard deviation sigma.
Usage
## S3 method for class 'ISEdescription'
plot(x, breaks = 20, ...)
Arguments
x
ISE description (e.g. object of class ISEdescription)
breaks
Approximate number of bins for histograms, defaults to 20
...
Other arguments to be passed through to plotting (histogram) functions
Value
No return value, creates plot.
Author(s)
Peter Dillingham, peter.dillingham@otago.ac.nz
See Also
Plot function for ion selective electrode characterisation and estimation of sample concentrations
Description
Plots sample concentration estimates derived from Bayesian calibration. E.g. analyseISE uses Bayesian calibration to estimate parameters for y = a + b log(x + c) + error, where error follows a normal distribution with mean 0 and standard deviation sigma. These valus are combined with experimental data to estimate sample concentrations.
Usage
## S3 method for class 'analyseISE'
plot(x, xlab = "Sample ID",
ylab = expression(paste(log[10], " { ", italic(x), " }")), xlim = NA,
ylim = c(-15, 0), x.ticks = NA, y.ticks = NA, x.ticks.label = TRUE,
y.ticks.label = TRUE, y.las = 2, col = 1, x.shift = 0, xaxs = "r",
yaxs = "r", add.box = TRUE, ...)
Arguments
x
Calibration and experimental sample results (of class 'analyseISE'; see analyseISE)
xlab
Label for the x-axis
ylab
Label for the y-axis
xlim
Limits for the x-axis. Automatically calculated if xlim = NA.
ylim
Limits for the y-axis.
x.ticks
Location of tickmarks for the x-axis. Automatically calculated if x.ticks = NA.
y.ticks
Location of tickmarks for the y-axis. Automatically calculated if y.ticks = NA.
x.ticks.label
Labels associated with x-axis tickmarks for the x-axis. Automatically calculated labels (TRUE), no labels (FALSE), or a column of text specifying custom labels (e.g. x.ticks.label = c("A", "B", "C") or similar, of the same length as x.ticks).
y.ticks.label
Labels associated with y-axis tickmarks for the y-axis. See x.ticks.label for details.
y.las
Indicates whether y-axis labels be perpendicular to the y-axis (2) or parallel to it (0).
col
Colour for the field of the plot.
x.shift
Shifts the plots to the left (- values) or right (+ values); useful for overlaying figures.
xaxs
The style of x-axis interval. See par for further details, but "r" adds 4 percent padding, "i" has no padding.
yaxs
The style of y-axis interval. See xaxs above.
add.box
Indicates whether a box should be drawn around the plot (TRUE) or not (FALSE).
...
Other arguments to be passed through to plotting functions.
Value
No return value, creates plot.
Author(s)
Peter Dillingham, peter.dillingham@otago.ac.nz
See Also
Prints ISE data
Description
Prints tables of calibration data and experimental data (if present).
Usage
## S3 method for class 'ISEdata'
print(x, ...)
Arguments
x
ISE data (e.g. object of class ISEdata)
...
Other objects passed through.
Value
No return value, prints ISE data.
Author(s)
Peter Dillingham, peter.dillingham@otago.ac.nz
See Also
Examples
data(LeadStdAdd)
print(LeadStdAdd)
Prints tables of ISE parameters.
Description
Prints tables of ISE parameters for one or multiple ISEs.
Usage
## S3 method for class 'ISEdescription'
print(x, ...)
Arguments
x
ISE analysis results (e.g. object of class analyseISE)
...
Other objects passed through.
Value
No return value, prints results from describeISE.
Author(s)
Peter Dillingham, peter.dillingham@otago.ac.nz
See Also
Prints tables of ISE parameters and estimated sample concentrations.
Description
Prints tables of ISE parameters and estimated sample concentrations.
Usage
## S3 method for class 'analyseISE'
print(x, ...)
Arguments
x
ISE analysis results (e.g. object of class analyseISE)
...
Other objects passed through.
Value
No return value, prints results from analyseISE.
Author(s)
Peter Dillingham, peter.dillingham@otago.ac.nz
See Also
Summarises ISE data
Description
summary.ISE takes an object of class ISEdata (e.g. see loadISEdata) and produces metadata for it.
Usage
## S3 method for class 'ISEdata'
summary(object, ...)
Arguments
object
Data set of class ISEdata
...
Other objects passed through.
Value
metadata: Metadata for the ISEs, a list with N, R, calibration.only, M, and stdadd
N
Total number of calibration observations
R
Number of ISEs
calibration.only
Indicates calibration only data (T), or calibration and experimental data (F)
M
Number of experimental samples (NA if no experimental data were loaded)
stdadd
Indicates whether standard addition used for experimental samples (T) or the basic model was used (F), or no experimental data (NA)
Author(s)
Peter Dillingham, peter.dillingham@otago.ac.nz
See Also
Examples
data(LeadStdAdd)
summary(LeadStdAdd)
Summarise ISE parameters
Description
summary.ISEdescription takes an object of class ISEddescription and prints a table of parameter values for y = a + b log(x + c) + error, with the erros following a Normal distribution with mean 0 and standard deviation sigma. Also calculates LOD using the conditional analytic method (alpha, beta, or S/N).
Usage
## S3 method for class 'ISEdescription'
summary(object, ...)
Arguments
object
object of class ISEdescription
...
Other objects passed through.
Value
table1: A matrix with parameter values for each ISE
Author(s)
Peter Dillingham, peter.dillingham@otago.ac.nz
See Also
Summary of estimates for ISE parameter values and experimental sample concentrations.
Description
summary.analyseISE takes an object of class analyseISE and produces summary tables.
Usage
## S3 method for class 'analyseISE'
summary(object, ...)
Arguments
object
Data set of class ISEdata
...
Other objects passed through.
Value
tables: Two tables (table1 and table2) are returned as a list.
table1
A table of ISE parameter values (see summary.describeISE for details)
table2
A table of estimated analyte concentrations for experimental samples
Author(s)
Peter Dillingham, peter.dillingham@otago.ac.nz