# Class "dat" for model and data storage

### Description

`dat`

is the super-class of other classes representing models and data, so
that other model/data classes (e.g., `kin`

and `spec`

for kinetic and spectral
models respectively) also have the slots defined here. Slots whose
description are marked with *** may
be specified in the `...`

argument of the `initModel`

function.

### Objects from the Class

Objects from the class
can be created by calls of the form `new("dat", ...)`

or
`dat(...)`

, but
most are most often made by invoking another function such as
`readData`

or `initModel`

.

### Slots

- chinde
- clinde
- clpequspecBD
- cohcol
- compnames
- highcon
- lowcon
- mvecind
- nvecind
- outMat
- satMat
- usecompnames0
- usecompnamesequ
- weightList
- dscalspec
- getX
- getXsuper
`weightpar`

:*** Object of class

`"list"`

list of vectors`c(first_x, last_x, first_x2, last_x2, weight)`

, where each vector is of length 5 and specifies an interval in which to weight the data.first\_xfirst(absolute, not an index)

`x`

to weightlast\_xlast (absolute, not an index)

`x`

to weightfirst\_x2first (absolute, not an index)

`x2`

to weightlast\_x2last (absolute, not an index)

`x2`

to weightweightnumeric by which to weight data

Note that if vector elements 1-4 are

`NA`

(not a number), the firstmost point of the data is taken for elements 1 and 3, and the lastmost points are taken for 2 and 4. For example,`weightpar = list(c(40, 1500, 400, 600, .9), c(NA, NA, 700, 800, .1))`

will weight data between times 40 and 1500 picoseconds and 700 and 800 wavelengths by .9, and will weight data at all times between wavelength 700 and 800 by .1. Note also that for single photon counting data`weightpar = list(poisson = TRUE)`

will apply poisson weighting to all non-zero elements of the data.`mod_type`

:*** Object of class

`"character"`

character string defining the model type, e.g.,`"kin"`

or`"spec"`

`fixed`

:*** Object of class

`"list"`

list of lists or vectors giving the parameter values to fix (at their starting values) during optimization.`free`

:*** Object of class

`"list"`

list of lists or vectors giving the parameter values to free during optimization; if this list is present then all parameters not specified in it are fixed, e.g.,`free = list(irfpar = 2)`

will fix every parameter at its starting value except for the 2nd`irfpar`

. If`fix = list(none=TRUE)`

(or if the element`none`

has length greater than 0) then all parameters in the model are fixed. Note that this option only should be applied to multiexperiment models in which at least one parameter appling to some other dataset is optimized (`nls`

always must have at least one parameter to optimize).`constrained`

:*** Object of class

`"list"`

list whose elements are lists containing a character vector`what`

, a vector`ind`

, and either (but not both) a character vector`low`

and`high`

.`what`

should specify the parameter type to constrain.`ind`

should give the index of the parameter to be constrained, e.g.,`1`

if indexing into a vector, and`c(1,2)`

if indexing into a list.`low`

gives a number that the parameter should always remain lower than and`high`

gives a number that the parameter should always remain higher than (so that`low`

bounds the parameter value from above and`high`

bounds the parameter value from below). It is not now possible to specify both`low`

and`high`

for a single parameter value. An example of a complete`constrained`

specification is`constrained = list(list(what = "kinpar", ind = 2, low = .3), list(what = "parmu", ind = c(1,1), high = .002))`

`clp0`

:*** Object of class

`"list"`

list of lists with elements`low`

,`high`

and`comp`

, specifying the least value in`x2`

to constrain to zero, the greatest value in`x2`

to constrain to zero, and the component to which to apply the zero constraint, respectively. e.g.,`clp0 = list(list(low=400, high = 600, comp=2), list(low = 600, high = 650, comp=4))`

applies zero constraints to the spectra associated with components 2 and 4.`autoclp0`

:*** Object of class

`"list"`

that has two elements;`oldRes`

, the output of`fitModel`

and an index`ind`

representing the index of the dataset to use in`oldRes`

;`ind`

defaults to one. The clp that are negative in`oldRes`

are constrained to zero in the new model; this is primarily useful when fitting a model, finding some negative clp, and constraining them to zero by fitting again with this option. See also the help page for`opt`

for other ways to constrain the clp to non-negativity.`clpequspec`

:*** Object of class

`"list"`

list of lists each of which has elements`to, from, low, high`

, and optional element`dataset`

to specify the dataset from which to get the reference clp (that is, a spectrum for kinetic models).`to`

is the component to be fixed in relation to some other component; from is the reference component.`low`

and`high`

are the least and greatest absolute values of the`clp`

vector to constrain. e.g.,`clpequspec = list(list(low = 400, high = 600, to = 1, from = 2))`

will constrain the first component to equality to the second component between wavelengths 400 and 600. Note that equality constraints are actually constraints to a linear relationship. For each of the equality constraints specfied as a list in the`clpequspec`

list, specify a starting value parameterizing this linear relation in the vector`clpequ`

; if true equality is desired then fix the corresponding parameter in`clpequ`

to 1. Note that if multiple components are constrainted, the`from`

in the sublists should be increasing order, (i.e.,`(list(to=2, from=1, low=100, high=10000), list(to=3, from=1, low=10000, high=100))`

, not`list(to=3, from=1, low=10000, high=100), list(to=2, from=1, low=10000, high=100)`

)`clpequ`

:***Object of class

`"vector"`

describes the parameters governing the clp equality constraints specified in`clpequspec`

`prelspec`

:*** Object of class

`"list"`

list of lists to specify the functional relationship between parameters, each of which has elementswhat1character string describing the parameter type to relate, e.g.,

`"kinpar"`

what2the parameter type on which the relation is based; usually the same as

`what1`

ind1index into

`what1`

ind2index into

`what2`

relcharacter string, optional argument to specify functional relation type, by default linear

e.g.,

`prelspec = list(list(what1 = "kinpar", what2 = "kinpar", ind1 = 1, ind2 = 5))`

relates the 1st element of`kinpar`

to the 5th element of`kinpar`

. The starting values parameterizing the relationship are given in the`prel`

vector`positivepar`

:*** Object of class

`"vector"`

containing character strings of those parameter vectors to constrain to positivity, e.g.,`positivepar=c("kinpar")`

`weight`

:Object of class

`"logical"`

`TRUE`

when the specification in`weightpar`

is to be applied and`FALSE`

otherwise`psi.df`

:Object of class

`"matrix"`

dataset from 1 experiment`psi.weight`

:Object of class

`"matrix"`

weighted dataset from 1 experiment`x`

:Object of class

`"vector"`

time or other independent variable.`nt`

:Object of class

`"integer"`

length`x`

`x2`

:Object of class

`"vector"`

vector of points in 2nd independent dimension, such as wavelengths of wavenumbers`nl`

:Object of class

`"integer"`

length`x2`

`C2`

:Object of class

`"matrix"`

concentration matrix for simulated data`E2`

:Object of class

`"matrix"`

matrix of spectra for simulated data`sigma`

:Object of class

`"numeric"`

noise level in simulated data`parnames`

:Object of class

`"vector"`

vector of parameter names, used internally`simdata`

:Object of class

`"logical"`

logical that is`TRUE`

if the data is simulated,`FALSE`

otherwise; will determine whether values in`C2`

and`E2`

are plotted with results`weightM`

:Object of class

`"matrix"`

weights`weightsmooth`

:Object of class

`"list"`

type of smoothing to apply with weighting; not currently used`makeps`

:Object of class

`"character"`

specifyies the prefix of files written to postscript`lclp0`

:Object of class

`"logical"`

`TRUE`

if specification in`clp0`

is to be applied and`FALSE`

otherwise`lclpequ`

:Object of class

`"logical"`

`TRUE`

if specification in clpequspec is to be applied and`FALSE`

otherwise`title`

:Object of class

`"character"`

displayed on output plots`mhist`

:Object of class

`"list"`

list describing fitting history`datCall`

:Object of class

`"list"`

list of calls to functions`dscalspec`

:Object of class

`"list"`

`dummy`

:Object of class

`"list"`

containing dummy parameters`drel`

:Object of class

`"vector"`

vector of starting parameters for dataset scaling relations`scalx`

:Object of class

`"numeric"`

numeric by which to scale the`x`

axis in plotting- prel
vector of starting values for the relations described in prelspec

`fvecind`

:Object of class

`"vector"`

vector containing indices of fixed parameters`pvecind`

:Object of class

`"vector"`

used internally to store indices of related parameters.`iter`

:Object of class

`"numeric"`

describing the number of iterations that is run; this is sometimes stored after fitting, but has not effect as an argument to`initModel`

`clpCon`

:Object of class

`"list"`

used internally to enforce constraints on the clp`ncomp`

:Object of class

`"numeric"`

describing the number of components in a model`clpdep`

:Object of class

`"logical"`

describing whether a model is dependent on the index of`x2`

`inten`

:Object of class

`"matrix"`

for use with FLIM data; represents the number of photons per pixel measured over the course of all times $t$ represented by the dataset. See the help for the`readData`

function for more information.`datafile`

:Object of class

`"character"`

containing the name of a datafile associated with the`psi.df`

`clpType`

:Object of class

`"character"`

that is "nt" if the model has clp in the "x" dimension and "nl" otherwise (so that, e.g., if`mod\_type = "kin"`

, then`clpType = "nl"`

).

### Author(s)

Katharine M. Mullen, Ivo H. M. van Stokkum, Joris J. Snellenburg, Sergey P. Laptenok

### See Also

`kin-class`

, `spec-class`

### Examples

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 | ```
# simulate data
C <- matrix(nrow = 51, ncol = 2)
k <- c(.5, 1)
t <- seq(0, 2, by = 2/50)
C[, 1] <- exp( - k[1] * t)
C[, 2] <- exp( - k[2] * t)
E <- matrix(nrow = 51, ncol = 2)
wavenum <- seq(18000, 28000, by=200)
location <- c(25000, 20000)
delta <- c(5000, 7000)
amp <- c(1, 2)
E[, 1] <- amp[1] * exp( - log(2) * (2 * (wavenum - location[1])/delta[1])^2)
E[, 2] <- amp[2] * exp( - log(2) * (2 * (wavenum - location[2])/delta[2])^2)
sigma <- .001
Psi_q <- C %*% t(E) + sigma * rnorm(nrow(C) * nrow(E))
# initialize an object of class dat
Psi_q_data <- dat(psi.df = Psi_q, x = t, nt = length(t),
x2 = wavenum, nl = length(wavenum))
# initialize an object of class dat via initModel
# this dat object is also a kin object
kinetic_model <- initModel(mod_type = "kin", seqmod = FALSE,
kinpar = c(.1, 2))
``` |

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