Type: Package
Title: Ion Selective Electrodes Analysis Methods
Version: 3.2.0
Date: 2022-10-14
Author: Peter Dillingham [aut, cre], Christina McGraw [ctb], Aleksandar Radu [ctb], Basim Alsaedi [ctb]
Maintainer: Peter Dillingham <peter.dillingham@otago.ac.nz>
Description: Characterisation and calibration of single or multiple Ion Selective Electrodes (ISEs); activity estimation of experimental samples. Implements methods described in: Dillingham, P.W., Radu, T., Diamond, D., Radu, A. and McGraw, C.M. (2012) <doi:10.1002/elan.201100510>, Dillingham, P.W., Alsaedi, B.S.O. and McGraw, C.M. (2017) <doi:10.1109/ICSENS.2017.8233898>, Dillingham, P.W., Alsaedi, B.S.O., Radu, A., and McGraw, C.M. (2019) <doi:10.3390/s19204544>, and Dillingham, P.W., Alsaedi, B.S.O., Granados-Focil, S., Radu, A., and McGraw, C.M. (2020) <doi:10.1021/acssensors.9b02133>.
Depends: coda
Imports: graphics, stats, utils
Suggests: R2OpenBUGS, rjags, boot, R.rsp
VignetteBuilder: R.rsp
SystemRequirements: OpenBUGS (>=3.0) or JAGS (>=4.3.1)
License: GPL-2
NeedsCompilation: no
Packaged: 2022-10-14 22:36:24 UTC; dillingh
Repository: CRAN
Date/Publication: 2022-10-19 09:17:57 UTC

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

Package: ISEtools
Type: Package
Version: 3.2.0
Depends: R (>4.1.0)
Date: 2022-10-14
License: GPL-2
SystemRequirements: OpenBUGS (>3.0) or JAGS (>=4.3.1)

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

loadISEdata

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

describeISE

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

analyseISE

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

loadISEdata

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

describeISE

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

analyseISE

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

loadISEdata

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

describeISE

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

See Also

analyseISE summary.ISEdescription