`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.

See `dat-class`

for an
example of the initialization of a
`kin`

object via the `initModel`

function.

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.

- anipar
- anispec
- autoclp0
- C2
- chinde
- clinde
- clp0
- clpCon
- clpdep
- clpequ
- clpequspecBD
- clpType
- cohcol
- cohirf
- datafile
- datCall
- drel
- dscalspec
- 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 specificy 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 additonal 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**X**r 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`

.

Class `dat-class`

, directly.

Katharine M. Mullen, David Nicolaides, Ivo H. M. van Stokkum

Puxty, G., Maeder, M., and Hungerbuhler, K. (2006) Tutorial on the fitting of
kinetics models to mulivariate spectroscopic measurements
with non-linear least-squares regression, *Chemometrics and Intelligent
Laboratory Systems* **81**, 149-164.

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 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 | ```
## 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
``` |

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