kin-class: Class "kin" for kinetic model storage.
In TIMP: Fitting Separable Nonlinear Models in Spectroscopy and Microscopy
kin-class R Documentation
Class "kin" for kinetic model storage.
Description
kin is the class for kinetic models;
an object
of class "kin" is initialized if
mod_type = "kin" is an
argument of initModel.
All objects of class kin are sub-classes of
class dat; see documentation for dat
for a description of
these slots.
Details
See dat-class for an
example of the initialization of a
kin object via the initModel function.
Objects from the Class
Objects can be created by calls of the form new("kin", ...) or
kin(...). Slots whose
description are marked with *** may
be specified in the ...
argument of the initModel function.
Slots
- anipar
- anispec
- autoclp0
- C2
- chinde
- clinde
- clp0
- clpCon
- clpdep
- clpequ
- clpequspecBD
- clpType
- cohcol
- cohirf
- datafile
- datCall
- drel
- dscal
- dscalspec
dummy:Object of class "list" of dummy parameters which can be used in complex relations
- E2
- fixed
- fixedkmat
- free
- fvecind
- getX
- getXsuper
- highcon
- inten
- kin2scal
- kinpar2
- kinscalspecial
- kinscalspecialspec
- lclp0
- lclpequ
- title
- parnames
- prel
- prelspec
- psi.df
- psi.weight
- pvecind
- satMat
- scalx
- usecompnames0
- usecompnamesequ
- usekin2
- weight
- weightList
- weightM
- weightpar
- weightsmooth
- x
- x2
- clpequspec
- compnames
- constrained
- iter
- lightregimespec
- lowcon
- makeps
- mhist
- mod_type
- mvecind
- ncomp
- nl
- nt
- nvecind
- outMat
- positivepar
- sigma
- simdata
- speckin2
- kinpar
*** vector of rate constants to be used as starting
values for the exponential decay of components; the length of this
vector determines the number of components of the kinetic
model.
specpar:*** Object of class "list" parameters for
spectral constraints
seqmod:*** Object of class "logical" that is TRUE if a sequential model is to be applied
and FALSE otherwise
irf:Object of class "logical" that is TRUE is an IRF is modeled and FALSE otherwise
mirf:Object of class "logical" that is TRUE if a measured IRF is modeled and FALSE
otherwise
measured_irf:*** Object of class "vector" containing a measured IRF
convalg:*** Object of class "numeric" 1-3 determining the numerical convolution algorithm
used in the case of modeling a measured IRF; if 3 then supply a
reference lifetime in the slot reftau.
reftau:*** Object of class "numeric" containing a
reference lifetime to be used when convalg=3
irffun:*** Object of class "character" describing the
function to use to describe the IRF, by default "gaus"
irfpar:*** Object of class "vector" of IRF parameters; for the common Gaussian IRF this
vector is ordered c(location, width)
dispmu:Object of class "logical" that is TRUE if dispersion of the parameter for IRF
location is to be modeled and FALSE otherwise
dispmufun:***Object of class "character" describing the functional form of the
dispersion of the IRF location parameter; if equal to "discrete" then the
IRF location is shifted per element of x2 and parmu should have the same
length as x2. defaults to a polynomial description
parmu:*** Object of class "list" of starting values for the dispersion model for the
IRF location
disptau:Object of class "logical" that is TRUE if dispersion of the parameter for
IRF width is to be modeled and FALSE otherwise
disptaufun:*** Object of class "character" describing the functional form of the
dispersion of the IRF width parameter; if equal to "discrete" then the
IRF width is parameterized per element of x2 and partau should have the same
length as x2. defaults to a polynomial description
partau:*** Object of class "vector" of starting values for the dispersion model for the
IRF FWHM
fullk:Object of class "logical" that is TRUE if the data are to be modeled using a
compartmental model defined in a K matrix and FALSE otherwise
kmat:*** Object of class "array" containing the K matrix descriptive of a compartmental
model
jvec:*** Object of class "vector" containing the J vector descriptive of the inputs to a
compartmental model
ncolc:Object of class "vector" describing the number of columns of the C matrix for
each clp in x2
kinscal:*** Object of class "vector" of starting values for branching parameters in a
compartmental model
kmatfit:Object of class "array" of fitted values for a compartmental model
cohspec:*** Object of class "list" describing the model for
coherent artifact/scatter component(s) containing the element type
and optionally the element numdatasets. The element
type can be set as follows:
"irf":if type="irf", the coherent artifact/scatter has the
time profile of the IRF.
"freeirfdisp":if type="freeirfdisp", the coherent
artifact/scatter has a
Gaussian time profile whose location and width are parameterized in the
vector coh.
"irfmulti":if type="irfmulti" the time profile of
the IRF is used for
the coherent artifact/scatter model, but the IRF parameters are taken per
dataset (for the multidataset case), and the integer argument
numdatasets must be equal to the
number of datasets modeled.
"seq":if type="seq" a sequential exponential decay
model is applied, whose starting value are contained in an additional
list element start. This
often models oscillating behavior well, where the number of
oscillations is the number of parameter starting values given in
start. The starting values after optimization will be found in
the slot coh of the object of class theta corresponding
to each dataset modeled.
"mix":if type="mix" if type="mix" a sequential exponential decay
model is applied along with a model that follows the time profile of the IRF;
the coherent artifact/scatter is then a linear superposition of these two
models; see the above description of seq for how to supply the
starting values.
coh:*** Object of class "vector" of starting values for the parameterization of a
coherent artifact
oscspec:*** Object of class "list" describing the model for
additional oscillation component(s) containing the element type
and optionally the element start. The element start can be used to specify the starting values for the oscillation function. The element type can be set as follows:
"harmonic":if type="harmonic", the oscillation function is a damped harmonic oscillator.
oscpar:*** Object of class "vector" of starting values for the oscillation parameters
wavedep:Object of class "logical" describing whether the kinetic model is dependent on
x2 index (i.e., whether there is clp-dependence)
lambdac:*** Object of class "numeric" for the center wavelength to be used in a polynomial
description of x2-dependence
amplitudes:*** Object of class "vector"
that may be used to multiply the concentrations by a square diagonal
matrix with the number of columns that the concentration matrix has;
the diagonal is given in amplitudes and these values will be
treated as parameters to be optimized.
streak:*** Object of class "logical"
that defaults to FALSE; if streak=TRUE then
the period of the laser is expected via streakT.
streakT:*** Object of class "numeric"
the period of the laser; this will be used to add a
backsweep term to the concentration matrix and should be
set in conjunction streak=TRUE.
doublegaus:*** Object of class "logical"
that defaults to FALSE and determines whether a double
Gaussian should be used to model the IRF. If doublegaus=TRUE
then irfpar should contain four numeric values corresponding to
the location (mean) of the IRF, the FWHM of the first Gaussian, the
FWHM of the second Gaussian, and the relative amplitude of the
second Gaussian, respectively.
multiplegaus:*** Object of class "logical"
that defaults to FALSE and determines whether multiple
Gaussians should be used to model the IRF. If multiplegaus=TRUE
then irfpar should contain: two numeric values corresponding to
the location (mean) and the FWHM of the first Gaussian of the IRF, and
three numeric values for each additional gaussian modeled, corresponding to
the relative scaling to the first gaussian, the shift (in time) relative to the
first gaussian and the FWHM of the additional Gaussian, respectively.
numericalintegration:*** Object of class "logical"
that defaults to FALSE and determines whether a kinetic theory
model of a reaction mechanism should be numerically integrated (using
deSolve) to find the concentrations.
If numericalintegration=TRUE then
initialvals should specify the initial concentrations and
reactantstoichiometrymatrix and
stoichiometrymatrix should specify the reaction mechanism,
as per Puxty et. al. (2006).
initialvals:*** Object of class "vector"
giving the concentrations at the initial time step.
reactantstoichiometrymatrix:*** Object of class "vector"
giving the (integer) stoichiometric coefficients for the reactants; this
is the matrix Xr of Puxty et. al. (2006) with dim=NULL.
stoichiometrymatrix:*** Object of class "vector"
giving the (integer) stoichiometric coefficients for the reactions; this
is the matrix X of Puxty et. al. (2006) with dim=NULL.
Extends
Class dat-class, directly.
Author(s)
Katharine M. Mullen, David Nicolaides, Ivo H. M. van Stokkum
References
Puxty, G., Maeder, M., and Hungerbuhler, K. (2006) Tutorial on the fitting of
kinetics models to multivariate spectroscopic measurements
with non-linear least-squares regression, Chemometrics and Intelligent
Laboratory Systems 81, 149-164.
See Also
dat-class, spec-class
Examples
## Example in modeling second order kinetics, by
## David Nicolaides.
## On simulated data.
##############################
## load TIMP
##############################
library("TIMP")
##############################
## SIMULATE DATA
##############################
## set up the Example problem, a la in-situ UV-Vis spectroscopy of a simple
## reaction.
## A + 2B -> C + D, 2C -> E
cstart <- c(A = 1.0, B = 0.8, C = 0.0, D = 0.0, E = 0.0)
times <- c(seq(0,2, length=21), seq(3,10, length=8))
k <- c(kA = 0.5, k2C = 1)
## stoichiometry matrices
rsmatrix <- c(1,2,0,0,0,0,0,2,0,0)
smatrix <- c(-1,-2,1,1,0,0,0,-2,0,1)
concentrations <- calcD(k, times, cstart, rsmatrix, smatrix)
wavelengths <- seq(500, 700, by=2)
spectra <- matrix(nrow = length(wavelengths), ncol = length(cstart))
location <- c(550, 575, 625, 650, 675)
delta <- c(10, 10, 10, 10, 10)
spectra[, 1] <- exp( - log(2) *
(2 * (wavelengths - location[1])/delta[1])^2)
spectra[, 2] <- exp( - log(2) *
(2 * (wavelengths - location[2])/delta[2])^2)
spectra[, 3] <- exp( - log(2) *
(2 * (wavelengths - location[3])/delta[3])^2)
spectra[, 4] <- exp( - log(2) *
(2 * (wavelengths - location[4])/delta[4])^2)
spectra[, 5] <- exp( - log(2) *
(2 * (wavelengths - location[5])/delta[5])^2)
sigma <- .001
Psi_q <- concentrations %*% t(spectra) + sigma *
rnorm(dim(concentrations)[1] * dim(spectra)[1])
## store the simulated data in an object of class "dat"
kinetic_data <- dat(psi.df=Psi_q , x = times, nt = length(times),
x2 = wavelengths, nl = length(wavelengths))
##############################
## DEFINE MODEL
##############################
## starting values
kstart <- c(kA = 1, k2C = 0.5)
## model definition for 2nd order kinetics
kinetic_model <- initModel(mod_type = "kin", seqmod = FALSE,
kinpar = kstart,
numericalintegration = TRUE,
initialvals = cstart,
reactantstoichiometrymatrix = rsmatrix,
stoichiometrymatrix = smatrix )
##############################
## FIT INITIAL MODEL
## adding constraints to non-negativity of the
## spectra via the opt option nnls=TRUE
##############################
kinetic_fit <- fitModel(data=list(kinetic_data),
modspec = list(kinetic_model),
opt = kinopt(nnls = TRUE, iter=80,
selectedtraces = seq(1,kinetic_data@nl,by=2)))
## look at estimated parameters
parEst(kinetic_fit)
## various results
## concentrations
conRes <- getX(kinetic_fit)
matplot(times, conRes, type="b", col=1,pch=21, bg=1:5, xlab="time (sec)",
ylab="concentrations", main="Concentrations (2nd order kinetics)")
## spectra
specRes <- getCLP(kinetic_fit)
matplot(wavelengths, specRes, type="b", col=1,pch=21, bg=1:5,
xlab="wavelength (nm)",
ylab="amplitude", main="Spectra")
## see help(getResults) for how to get more results information from
## kinetic_fit
##############################
## CLEANUP GENERATED FILES
##############################
# This removes the files that were generated in this example
# (do not run this code if you wish to inspect the output)
file_list_cleanup = c(Sys.glob("*paramEst.txt"), Sys.glob("*.ps"), Sys.glob("Rplots*.pdf"))
# Iterate over the files and delete them if they exist
for (f in file_list_cleanup) {
if (file.exists(f)) {
unlink(f)
}
}
TIMP documentation built on Dec. 28, 2022, 3:06 a.m.
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| kin-class | R Documentation |
Class "kin" for kinetic model storage.
Description
kin is the class for kinetic models;
an object
of class "kin" is initialized if
mod_type = "kin" is an
argument of initModel.
All objects of class kin are sub-classes of
class dat; see documentation for dat
for a description of
these slots.
Details
See dat-class for an
example of the initialization of a
kin object via the initModel function.
Objects from the Class
Objects can be created by calls of the form new("kin", ...) or
kin(...). Slots whose
description are marked with *** may
be specified in the ...
argument of the initModel function.
Slots
- anipar
- anispec
- autoclp0
- C2
- chinde
- clinde
- clp0
- clpCon
- clpdep
- clpequ
- clpequspecBD
- clpType
- cohcol
- cohirf
- datafile
- datCall
- drel
- dscal
- dscalspec
dummy:Object of class
"list"of dummy parameters which can be used in complex relations- E2
- fixed
- fixedkmat
- free
- fvecind
- getX
- getXsuper
- highcon
- inten
- kin2scal
- kinpar2
- kinscalspecial
- kinscalspecialspec
- lclp0
- lclpequ
- title
- parnames
- prel
- prelspec
- psi.df
- psi.weight
- pvecind
- satMat
- scalx
- usecompnames0
- usecompnamesequ
- usekin2
- weight
- weightList
- weightM
- weightpar
- weightsmooth
- x
- x2
- clpequspec
- compnames
- constrained
- iter
- lightregimespec
- lowcon
- makeps
- mhist
- mod_type
- mvecind
- ncomp
- nl
- nt
- nvecind
- outMat
- positivepar
- sigma
- simdata
- speckin2
- kinpar
*** vector of rate constants to be used as starting values for the exponential decay of components; the length of this vector determines the number of components of the kinetic model.
specpar:*** Object of class
"list"parameters for spectral constraintsseqmod:*** Object of class
"logical"that isTRUEif a sequential model is to be applied andFALSEotherwiseirf:Object of class
"logical"that isTRUEis an IRF is modeled andFALSEotherwisemirf:Object of class
"logical"that isTRUEif a measured IRF is modeled andFALSEotherwisemeasured_irf:*** Object of class
"vector"containing a measured IRFconvalg:*** Object of class
"numeric"1-3 determining the numerical convolution algorithm used in the case of modeling a measured IRF; if3then supply a reference lifetime in the slotreftau.reftau:*** Object of class
"numeric"containing a reference lifetime to be used whenconvalg=3irffun:*** Object of class
"character"describing the function to use to describe the IRF, by default "gaus"irfpar:*** Object of class
"vector"of IRF parameters; for the common Gaussian IRF this vector is orderedc(location, width)dispmu:Object of class
"logical"that isTRUEif dispersion of the parameter for IRF location is to be modeled andFALSEotherwisedispmufun:***Object of class
"character"describing the functional form of the dispersion of the IRF location parameter; if equal to "discrete" then the IRF location is shifted per element ofx2andparmushould have the same length asx2. defaults to a polynomial descriptionparmu:*** Object of class
"list"of starting values for the dispersion model for the IRF locationdisptau:Object of class
"logical"that isTRUEif dispersion of the parameter for IRF width is to be modeled andFALSEotherwisedisptaufun:*** Object of class
"character"describing the functional form of the dispersion of the IRF width parameter; if equal to"discrete"then the IRF width is parameterized per element ofx2andpartaushould have the same length asx2. defaults to a polynomial descriptionpartau:*** Object of class
"vector"of starting values for the dispersion model for the IRF FWHMfullk:Object of class
"logical"that isTRUEif the data are to be modeled using a compartmental model defined in a K matrix andFALSEotherwisekmat:*** Object of class
"array"containing the K matrix descriptive of a compartmental modeljvec:*** Object of class
"vector"containing the J vector descriptive of the inputs to a compartmental modelncolc:Object of class
"vector"describing the number of columns of the C matrix for each clp inx2kinscal:*** Object of class
"vector"of starting values for branching parameters in a compartmental modelkmatfit:Object of class
"array"of fitted values for a compartmental modelcohspec:*** Object of class
"list"describing the model for coherent artifact/scatter component(s) containing the elementtypeand optionally the elementnumdatasets. The elementtypecan be set as follows:"irf":if
type="irf", the coherent artifact/scatter has the time profile of the IRF."freeirfdisp":if
type="freeirfdisp", the coherent artifact/scatter has a Gaussian time profile whose location and width are parameterized in the vectorcoh."irfmulti":if
type="irfmulti"the time profile of the IRF is used for the coherent artifact/scatter model, but the IRF parameters are taken per dataset (for the multidataset case), and the integer argumentnumdatasetsmust be equal to the number of datasets modeled."seq":if
type="seq"a sequential exponential decay model is applied, whose starting value are contained in an additional list elementstart. This often models oscillating behavior well, where the number of oscillations is the number of parameter starting values given instart. The starting values after optimization will be found in the slotcohof the object of classthetacorresponding to each dataset modeled."mix":if
type="mix"iftype="mix"a sequential exponential decay model is applied along with a model that follows the time profile of the IRF; the coherent artifact/scatter is then a linear superposition of these two models; see the above description ofseqfor how to supply the starting values.
coh:*** Object of class
"vector"of starting values for the parameterization of a coherent artifactoscspec:*** Object of class
"list"describing the model for additional oscillation component(s) containing the elementtypeand optionally the elementstart. The elementstartcan be used to specify the starting values for the oscillation function. The elementtypecan be set as follows:"harmonic":if
type="harmonic", the oscillation function is a damped harmonic oscillator.
oscpar:*** Object of class
"vector"of starting values for the oscillation parameterswavedep:Object of class
"logical"describing whether the kinetic model is dependent onx2index (i.e., whether there is clp-dependence)lambdac:*** Object of class
"numeric"for the center wavelength to be used in a polynomial description ofx2-dependenceamplitudes:*** Object of class
"vector"that may be used to multiply the concentrations by a square diagonal matrix with the number of columns that the concentration matrix has; the diagonal is given inamplitudesand these values will be treated as parameters to be optimized.streak:*** Object of class
"logical"that defaults toFALSE; ifstreak=TRUEthen the period of the laser is expected viastreakT.streakT:*** Object of class
"numeric"the period of the laser; this will be used to add a backsweep term to the concentration matrix and should be set in conjunctionstreak=TRUE.doublegaus:*** Object of class
"logical"that defaults toFALSEand determines whether a double Gaussian should be used to model the IRF. Ifdoublegaus=TRUEthenirfparshould contain four numeric values corresponding to the location (mean) of the IRF, the FWHM of the first Gaussian, the FWHM of the second Gaussian, and the relative amplitude of the second Gaussian, respectively.multiplegaus:*** Object of class
"logical"that defaults toFALSEand determines whether multiple Gaussians should be used to model the IRF. Ifmultiplegaus=TRUEthenirfparshould contain: two numeric values corresponding to the location (mean) and the FWHM of the first Gaussian of the IRF, and three numeric values for each additional gaussian modeled, corresponding to the relative scaling to the first gaussian, the shift (in time) relative to the first gaussian and the FWHM of the additional Gaussian, respectively.numericalintegration:*** Object of class
"logical"that defaults toFALSEand determines whether a kinetic theory model of a reaction mechanism should be numerically integrated (using deSolve) to find the concentrations. Ifnumericalintegration=TRUEtheninitialvalsshould specify the initial concentrations andreactantstoichiometrymatrixandstoichiometrymatrixshould specify the reaction mechanism, as per Puxty et. al. (2006).initialvals:*** Object of class
"vector"giving the concentrations at the initial time step.reactantstoichiometrymatrix:*** Object of class
"vector"giving the (integer) stoichiometric coefficients for the reactants; this is the matrix Xr of Puxty et. al. (2006) withdim=NULL.stoichiometrymatrix:*** Object of class
"vector"giving the (integer) stoichiometric coefficients for the reactions; this is the matrix X of Puxty et. al. (2006) withdim=NULL.
Extends
Class dat-class, directly.
Author(s)
Katharine M. Mullen, David Nicolaides, Ivo H. M. van Stokkum
References
Puxty, G., Maeder, M., and Hungerbuhler, K. (2006) Tutorial on the fitting of kinetics models to multivariate spectroscopic measurements with non-linear least-squares regression, Chemometrics and Intelligent Laboratory Systems 81, 149-164.
See Also
dat-class, spec-class
Examples
## Example in modeling second order kinetics, by
## David Nicolaides.
## On simulated data.
##############################
## load TIMP
##############################
library("TIMP")
##############################
## SIMULATE DATA
##############################
## set up the Example problem, a la in-situ UV-Vis spectroscopy of a simple
## reaction.
## A + 2B -> C + D, 2C -> E
cstart <- c(A = 1.0, B = 0.8, C = 0.0, D = 0.0, E = 0.0)
times <- c(seq(0,2, length=21), seq(3,10, length=8))
k <- c(kA = 0.5, k2C = 1)
## stoichiometry matrices
rsmatrix <- c(1,2,0,0,0,0,0,2,0,0)
smatrix <- c(-1,-2,1,1,0,0,0,-2,0,1)
concentrations <- calcD(k, times, cstart, rsmatrix, smatrix)
wavelengths <- seq(500, 700, by=2)
spectra <- matrix(nrow = length(wavelengths), ncol = length(cstart))
location <- c(550, 575, 625, 650, 675)
delta <- c(10, 10, 10, 10, 10)
spectra[, 1] <- exp( - log(2) *
(2 * (wavelengths - location[1])/delta[1])^2)
spectra[, 2] <- exp( - log(2) *
(2 * (wavelengths - location[2])/delta[2])^2)
spectra[, 3] <- exp( - log(2) *
(2 * (wavelengths - location[3])/delta[3])^2)
spectra[, 4] <- exp( - log(2) *
(2 * (wavelengths - location[4])/delta[4])^2)
spectra[, 5] <- exp( - log(2) *
(2 * (wavelengths - location[5])/delta[5])^2)
sigma <- .001
Psi_q <- concentrations %*% t(spectra) + sigma *
rnorm(dim(concentrations)[1] * dim(spectra)[1])
## store the simulated data in an object of class "dat"
kinetic_data <- dat(psi.df=Psi_q , x = times, nt = length(times),
x2 = wavelengths, nl = length(wavelengths))
##############################
## DEFINE MODEL
##############################
## starting values
kstart <- c(kA = 1, k2C = 0.5)
## model definition for 2nd order kinetics
kinetic_model <- initModel(mod_type = "kin", seqmod = FALSE,
kinpar = kstart,
numericalintegration = TRUE,
initialvals = cstart,
reactantstoichiometrymatrix = rsmatrix,
stoichiometrymatrix = smatrix )
##############################
## FIT INITIAL MODEL
## adding constraints to non-negativity of the
## spectra via the opt option nnls=TRUE
##############################
kinetic_fit <- fitModel(data=list(kinetic_data),
modspec = list(kinetic_model),
opt = kinopt(nnls = TRUE, iter=80,
selectedtraces = seq(1,kinetic_data@nl,by=2)))
## look at estimated parameters
parEst(kinetic_fit)
## various results
## concentrations
conRes <- getX(kinetic_fit)
matplot(times, conRes, type="b", col=1,pch=21, bg=1:5, xlab="time (sec)",
ylab="concentrations", main="Concentrations (2nd order kinetics)")
## spectra
specRes <- getCLP(kinetic_fit)
matplot(wavelengths, specRes, type="b", col=1,pch=21, bg=1:5,
xlab="wavelength (nm)",
ylab="amplitude", main="Spectra")
## see help(getResults) for how to get more results information from
## kinetic_fit
##############################
## CLEANUP GENERATED FILES
##############################
# This removes the files that were generated in this example
# (do not run this code if you wish to inspect the output)
file_list_cleanup = c(Sys.glob("*paramEst.txt"), Sys.glob("*.ps"), Sys.glob("Rplots*.pdf"))
# Iterate over the files and delete them if they exist
for (f in file_list_cleanup) {
if (file.exists(f)) {
unlink(f)
}
}
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