Class "opt" stores options for fitting and plotting
Class "opt" stores options for fitting and plotting applicable to all model types class
Objects can be created by calls of the form new("opt", ...) or opt(...).
getStartTri:
imagepal:
maxfev:
minFactor:
nnlscrit:
noplotest:
notraces:
optimmethod:
parscale:
residtraces:
selectedtraces:
sumnls:
trilinear:
triStart:
writedata:
writefitivo:
xlim:
algorithm:: Object of class "character" that defaults to algorithm="nls", so that the function nls is used to optimize nonlinear parameters under least squares criteria. Other options are
- **`nls.lm`:**: optimize nonlinear parameters under least squares criteria using `nls.lm`
- **`optim`:**: optimize nonlinear parameters under poisson regression criteria with the Nelder-Mead algorithm in `optim`; if this option is used then it MUST be used in conjunction with `nnls=TRUE`. Currently, it must also be used with `stderrclp=FALSE`.
nnls:: Object of class "logical" that defaults to FALSE. If nnls=TRUE, constrain the conditionally linear parameters to nonnegativity via a nonnegative least squares algorithm as implemented via the function nnls from the package by the same name.
writecon:: Object of class "logical" that defaults to FALSE; if true then concentrations are written to a txt file; row labels are x
writespec:: Object of class "logical" that defaults to FALSE; if TRUE then spectra are written to a txt file; row labels are x2
writenormspec:: Object of class "logical" that defaults to FALSE; if TRUE then normalized spectra are written to a txt file; row labels are x2
writefit:: Object of class "logical" that defaults to FALSE; if TRUE then fit is written to a txt file; row and column labels are x and x2
writeclperr:: Object of class "logical"
that defaults to FALSE; if true then the error bars for clp are written to a txt file. This option is only sensible with `stderrclp=TRUE`.
output:: Object of class "character" that defaults to "ps", which means that plots written to file are postscript. Alternatively, specify output = "pdf", and plots are written as pdf files
addfilename:: Object of class "logical" that, for each data file, tries to add the filename to plots associated with output for that data.
residplot:: Object of class "logical" defaults to FALSE; if TRUE generate a plot of residuals in a separate window.
adddataimage:: Object of class "logical" defaults to FALSE; if TRUE adding imageplot of data in summary plot.
plot:: Object of class "logical" that defaults to TRUE; if FALSE then do not write output in the form of plots and other windows to the screen.
divdrel:: Object of class "logical" that defaults to FALSE; if TRUE, plot traces and concentration profiles divided by the dataset scaling parameters where they apply; this allows for the fit of datasets having different intensities on the same scale.
plotkinspec:: Object of class "logical" that defaults to FALSE; if TRUE, generates a separate plot of the spectra associated with the components that are not a part of a coherent artifact/scatter model.
superimpose:: Object of class "vector" containing dataset indices whose results should be superimposed in plots
xlab:: Object of class "character" containing label for x-axis, e.g., "nanoseconds"
or `"picoseconds"`
ylab:: Object of class "character" containing label for y-axis, e.g., "wavelength"
title:: Object of class "character" containing title to write at the top of plots.
makeps:: Object of class "character" containing prefix to plot files written to postscript; if present postscript will be written. Note that this string is also used as the preffix of txt output files
linrange:: Object of class "numeric" giving linear range of time axis for plotting; time will be plotted linearly from -linrange to linrange and plotted on a logarithmic (base 10) axis elsewhere
summaryplotrow:: Object of class "numeric" giving number of rows in summary plot; defaults to 4
summaryplotcol:: Object of class "numeric" giving number of columns in summary plot; defaults to 4
iter:: Object of class "numeric" giving number of iterations to optimize model parameters; if nls=FALSE so that the Levenberg-Marquardt algorithm is applied, then iter is interpreted as the maximum number of residual function evaluations (see the help page of the function nls.lm for details)
paropt:: Object of class "list"
of graphical parameters in format `par(...)`
to apply to plots.
stderrclp:: Object of class "logical" that defaults to FALSE; if TRUE, estimates of the standard error of conditionally linear parameters are made
addest:: Object of class "vector" containing character strings of which parameter estimates should be added to the summary plot, e.g., addest = c("kinpar", "irfpar")
kinspecerr: Object of class "logical" that defaults to FALSE; if TRUE, add standard error estimates to the clp a plot generated with kinspecest=TRUE or plotkinspec=TRUE. This option can only be used if the estimates were generated during fitting via the option stderrclp=TRUE
xlimspec: Object of class "vector" that defaults to vector(); if changed, it should specify the desired x-limits of the plot of clp
ylimspec: Object of class "vector" that defaults to vector(); if changed, it should specify the desired y-limits of the plot of clp. In the case of plotting the results of FLIM image analysis, ylimspec can be used to determine the range used in the image plot of lifetimes.
ylimspecplus: Object of class "vector" that defaults to vector(); if changed, the first value should specify a vector to add to the y-limits of the plot of clp
samespecline: Object of class "logical" that defaults to FALSE; if TRUE, then the line-type for clp is the same for all datasets
specinterpol: Object of class "logical" that defaults to FALSE; if TRUE, use spline instead of lines between the points representing estimated clp
specinterpolpoints: Object of class "logical" that defaults to TRUE; if TRUE, add points representing the actual estimates for clp to plots of the curves representing smoothed clp
specinterpolseg: Object of class "numeric" that defaults to 50; represents the number of segments used in a spline-based representation of clp
specinterpolbspline: Object of class "logical" that defaults to FALSE; determines whether a B-spline based representation of clp is used (when specinterpol=TRUE) or a piecewise polynomial representation
normspec: Object of class "logical" that determines whether clp are normalized in plots
writespecinterpol: Object of class "logical" that defaults to FALSE; if TRUE, a spline-based representation of clp is written to ASCII files
nlsalgorithm: Object of class "character" that defaults to "default" and determines the algorithm used by nls, if nls is used in optimization. See help(nls) for other possibilities, such as "port", which is more stable with respect to starting values but requires more time.
ltyfit: Object of class "numeric" if given, sets the line type of the fit in plots of the fit/data; see lty in help(par) for options.
ltydata: Object of class "numeric" if given, sets the line type of the data in plots of the fit/data; see lty in help(par) for options.
colfit: Object of class "vector" if given, sets the color of the fit corresponding to each dataset in plots of the fit/data; see col in help(par) for options. If given length(colfit) must be equal to the number of datasets in the analysis
coldata: Object of class "vector" if given, sets the color of the data for each dataset in plots of the fit/data; see col in help(par) for options. If given, length(coldata) must be equal to the number of datasets in the analysis
See kinopt-class, specopt-class and massopt-class for the specification of fitting/plotting options that are specific to the class type.
Katharine M. Mullen, Ivo H. M. van Stokkum
kinopt-class, specopt-class