Class "kin" for kinetic model storage.
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. 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.
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 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.
See dat-class for an example of the initialization of a kin object via the initModel function.
## 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) } }
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.
Katharine M. Mullen, David Nicolaides, Ivo H. M. van Stokkum
dat-class, spec-class