getResults: Functions to print and return parts of the object returned by...
In TIMP: Fitting Separable Nonlinear Models in Spectroscopy and Microscopy
getResults R Documentation
Functions to print and return parts of the object returned
by the fitting routines.
Description
Functions to print and return parts of the object returned
by fitModel. onls returns the output of the
nls
function. sumonls returns the result of calling summary
on onls function. parEst returns a summary of model
parameter estimates. The remaining functions return lists
representing various aspects of the results returned by the function
fitModel.
Usage
onls(result)
sumnls(result)
parEst(result, param = "", dataset = NA, verbose = TRUE, file="",
stderr=TRUE)
getXList(result, group = vector(), file="")
getCLPList(result, getclperr = FALSE, file="")
getX(result, group = vector(), dataset=1, file="", lreturnA = FALSE, lreturnC = FALSE)
getC(result, dataset=1, file="")
getCLP(result, getclperr = FALSE, dataset=1, file="")
getDAS(result, getclperr = FALSE, dataset=1, file="")
getData(result, dataset = 1, weighted = FALSE)
getResiduals(result, dataset = 1)
getSVDResiduals(result, numsing = 2, dataset = 1)
getTraces(result, dataset = 1, file="")
getdim1(result, dataset = 1)
getdim2(result, dataset = 1)
Arguments
result
return value of fitModel
param
character vector of the particular parameters to return;
if param=="" then all parameters are given.
dataset
index of the dataset from which to return results; by
default dataset=NA in which case results from all datasets
are returned
verbose
logical that defaults to TRUE that determines
whether parEst just returns a list invisibly or prints as well.
getclperr
logical that defaults to FALSE that determines
whether a list containing the standard error estimates associated with
the conditionally linear parameters, as opposed to the conditionally
linear parameters themselves
numsing
integer that defaults to 2; determines the number of
singular vectors to return
weighted
logical indicating whether to return weighted or
unweighted data
lreturnA
logical indicating whether to return an A matrix instead
lreturnC
logical indicating whether to return a C matrix instead
file
character vector; if not "" writes the results to a
file with name file.
group
The value at which to determine the X matrix (maybe a
wavelength index, for example)
stderr
Whether to return standard error estimates on parameters,
if they were calculated in fitting.
Value
sumnls returns an object of class "summary.nls".
onls returns an object of class "nls".
parEst returns an object of class "list" representing
the parameter estimates and the standard errors if stderr=TRUE
and they have been calculated.
getXList returns a "list" of length equal to the number of
datasets modeled, where each element represents the matrix determined
by the nonlinear parameters (under a kinetic model, the concentrations).
getCLPList returns a "list" of length equal to the number of
datasets modeled, where each element represents the matrix determined
as conditionally linear parameters (under a kinetic model, the spectra).
getX returns a numeric "matrix"
that represents the matrix determined
by the nonlinear parameters (under a kinetic model, the concentrations).
However, in case lreturnC = TRUE it returns the C matrix, and in case
lreturnA = TRUE it returns the A matrix that is used to compute
the C matrix in case the kinetic model differs from parallel decays.
getC returns (under a kinetic model) a numeric "matrix"
that represents the raw matrix of concentrations of the dataset determined
by the nonlinear parameters.
getDAS returns (under a kinetic model) a numeric "matrix"
that represents the Decay Associated Spectra (DAS).
getCLPList returns a numeric "matrix"
that represents the matrix determined
as conditionally linear parameters (under a kinetic model, the spectra).
getSVDData
returns a "list" of length 3 with named elements
values, left and right, where values
contains the singular values, left contains numsing
left singular vectors, and right contains numsing
right singular vectors, all of the unweighted data. The number of singular
vectors returned is determined by numsing.
getData returns the dataset specified by the
argument dataset (weighted data in the case
that weighted=TRUE) as a "matrix"
getResiduals returns a "matrix" of residuals for the
dataset with index given by the argument dataset; the matrix
returned has the dimension of the dataset itself.
getSVDResiduals
returns a "list" of length 3 with named elements
values, left and right, where values
contains the singular values, left contains numsing
left singular vectors, and right contains numsing
right singular vectors, all of the residuals. The number of singular
vectors returned is determined by numsing.
getTraces returns a "matrix" of model estimates for the
dataset with index given by the argument dataset; the matrix
returned has the dimension of the dataset itself.
getdim1 returns a "vector" of
x values in the dataset (times for kinetic models).
getdim2 returns a "vector" of
x2 values (wavelengths for kinetic models).
Author(s)
Katharine M. Mullen, Ivo H. M. van Stokkum
See Also
fitModel
Examples
## Example showing the addition of non-negativity constraints to
## conditionally linear parameters (here the spectra associated with
## a kinetic model)
## For the 1st simulated dataset, the constraints offer a modest improvement
## in the estimated spectra, whereas for the 2nd simulated dataset, they
## prevent a catastrophe in which the estimated components are hugely
## compensating.
##############################
## load TIMP
##############################
require(TIMP)
##############################
## set random seed for reproducability of noise
##############################
set.seed(80)
##############################
## SIMULATE DATA, noise realization 1
##############################
dt4 <- simndecay_gen(kinpar = c(.4, .8, 2), seqmod = FALSE, tmax
= 2, deltat = .04, specpar = list(c(25000, 3000, .01), c(22000,
3000, .01), c(18000, 3000, .01)), lmin=350, lmax=550, deltal = 2,
sigma=.01)
##############################
## SPECIFY INITIAL MODEL
##############################
mod1 <- initModel(mod_type = "kin", kinpar = c(.4, .8, 2),
seqmod=FALSE)
##############################
## FIT INITIAL MODEL
##############################
sT <- fitModel(list(dt4), list(mod1), opt=kinopt(iter=50, plot=FALSE))
##############################
## EXTRACT ESTIMATED SPECTRA
## these spectra have some negative values
##############################
sTcp <- getCLP(sT)
## plot the estimated spectra with the values used in
## simulation (before adding noise) for comparison
matplot(dt4@x2, sTcp, xlab = "wavelength (nm)", col = 2:4, type="l",
ylab="",lty=1, main =
paste("Estimated spectra, adding no constraints\n"))
matplot(dt4@x2,dt4@E2, add=TRUE, type="l", col=1, lty=2)
abline(0,0)
##############################
## FIT INITIAL MODEL
## adding constraints to non-negativity of the
## spectra via the opt option nnls=TRUE
##############################
sV <- fitModel(list(dt4), list(mod1), opt=kinopt(iter=50, nnls=TRUE,
plot=FALSE))
##############################
## EXTRACT ESTIMATED SPECTRA
## these spectra have no negative values
##############################
sVcp <- getCLP(sV)
## plot the estimated spectra with the values used in
## simulation (before adding noise) for comparison
matplot(dt4@x2, sVcp, xlab = "wavelength (nm)", col = 2:4, type="l",
ylab="",lty=1,
main = paste("Estimated spectra, with non-negativity constraints\n"))
matplot(dt4@x2,dt4@E2, add=TRUE, type="l", col=1, lty=2)
abline(0,0)
##############################
## SIMULATE DATA, noise realization 2
##############################
dt4_2 <- simndecay_gen(kinpar = c(.4, .8, 2), seqmod = FALSE, tmax
= 2, deltat = .04, specpar = list(c(25000, 3000, .01), c(22000,
3000, .01), c(18000, 3000, .01)), lmin=350, lmax=550, deltal = 2,
sigma=.01)
##############################
## SPECIFY INITIAL MODEL
##############################
mod1 <- initModel(mod_type = "kin", kinpar = c(.4, .8, 2),
seqmod=FALSE)
##############################
## FIT INITIAL MODEL
##############################
sT <- fitModel(list(dt4_2), list(mod1), opt=kinopt(iter=50,plot=FALSE))
##############################
## EXTRACT ESTIMATED SPECTRA
## these spectra have some negative values
##############################
sTcp <- getCLP(sT)
## plot the estimated spectra with the values used in
## simulation (before adding noise) for comparison
matplot(dt4@x2, sTcp, xlab = "wavelength (nm)", col = 2:4, type="l",
ylab="",lty=1, main =
paste("Estimated spectra, adding no constraints\n"))
matplot(dt4@x2,dt4@E2, add=TRUE, type="l", col=1, lty=2)
abline(0,0)
##############################
## FIT INITIAL MODEL
## adding constraints to non-negativity of the
## spectra via the opt option nnls=TRUE
##############################
sV <- fitModel(list(dt4_2), list(mod1), opt=kinopt(iter=50, nnls=TRUE,
plot=FALSE))
##############################
## EXTRACT ESTIMATED SPECTRA
## these spectra have no negative values
##############################
sVcp <- getCLP(sV)
## plot the estimated spectra with the values used in
## simulation (before adding noise) for comparison
matplot(dt4@x2, sVcp, xlab = "wavelength (nm)", col = 2:4, type="l",
ylab="",lty=1,
main = paste("Estimated spectra, with non-negativity constraints\n"))
matplot(dt4@x2,dt4@E2, add=TRUE, type="l", col=1, lty=2)
abline(0,0)
# end donttest
##############################
## 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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| getResults | R Documentation |
Functions to print and return parts of the object returned by the fitting routines.
Description
Functions to print and return parts of the object returned
by fitModel. onls returns the output of the
nls
function. sumonls returns the result of calling summary
on onls function. parEst returns a summary of model
parameter estimates. The remaining functions return lists
representing various aspects of the results returned by the function
fitModel.
Usage
onls(result) sumnls(result) parEst(result, param = "", dataset = NA, verbose = TRUE, file="", stderr=TRUE) getXList(result, group = vector(), file="") getCLPList(result, getclperr = FALSE, file="") getX(result, group = vector(), dataset=1, file="", lreturnA = FALSE, lreturnC = FALSE) getC(result, dataset=1, file="") getCLP(result, getclperr = FALSE, dataset=1, file="") getDAS(result, getclperr = FALSE, dataset=1, file="") getData(result, dataset = 1, weighted = FALSE) getResiduals(result, dataset = 1) getSVDResiduals(result, numsing = 2, dataset = 1) getTraces(result, dataset = 1, file="") getdim1(result, dataset = 1) getdim2(result, dataset = 1)
Arguments
result |
return value of |
param |
character vector of the particular parameters to return;
if |
dataset |
index of the dataset from which to return results; by
default |
verbose |
logical that defaults to |
getclperr |
logical that defaults to |
numsing |
integer that defaults to 2; determines the number of singular vectors to return |
weighted |
logical indicating whether to return weighted or unweighted data |
lreturnA |
logical indicating whether to return an A matrix instead |
lreturnC |
logical indicating whether to return a C matrix instead |
file |
character vector; if not |
group |
The value at which to determine the X matrix (maybe a wavelength index, for example) |
stderr |
Whether to return standard error estimates on parameters, if they were calculated in fitting. |
Value
sumnls returns an object of class "summary.nls".
onls returns an object of class "nls".
parEst returns an object of class "list" representing
the parameter estimates and the standard errors if stderr=TRUE
and they have been calculated.
getXList returns a "list" of length equal to the number of
datasets modeled, where each element represents the matrix determined
by the nonlinear parameters (under a kinetic model, the concentrations).
getCLPList returns a "list" of length equal to the number of
datasets modeled, where each element represents the matrix determined
as conditionally linear parameters (under a kinetic model, the spectra).
getX returns a numeric "matrix"
that represents the matrix determined
by the nonlinear parameters (under a kinetic model, the concentrations).
However, in case lreturnC = TRUE it returns the C matrix, and in case
lreturnA = TRUE it returns the A matrix that is used to compute
the C matrix in case the kinetic model differs from parallel decays.
getC returns (under a kinetic model) a numeric "matrix"
that represents the raw matrix of concentrations of the dataset determined
by the nonlinear parameters.
getDAS returns (under a kinetic model) a numeric "matrix"
that represents the Decay Associated Spectra (DAS).
getCLPList returns a numeric "matrix"
that represents the matrix determined
as conditionally linear parameters (under a kinetic model, the spectra).
getSVDData
returns a "list" of length 3 with named elements
values, left and right, where values
contains the singular values, left contains numsing
left singular vectors, and right contains numsing
right singular vectors, all of the unweighted data. The number of singular
vectors returned is determined by numsing.
getData returns the dataset specified by the
argument dataset (weighted data in the case
that weighted=TRUE) as a "matrix"
getResiduals returns a "matrix" of residuals for the
dataset with index given by the argument dataset; the matrix
returned has the dimension of the dataset itself.
getSVDResiduals
returns a "list" of length 3 with named elements
values, left and right, where values
contains the singular values, left contains numsing
left singular vectors, and right contains numsing
right singular vectors, all of the residuals. The number of singular
vectors returned is determined by numsing.
getTraces returns a "matrix" of model estimates for the
dataset with index given by the argument dataset; the matrix
returned has the dimension of the dataset itself.
getdim1 returns a "vector" of
x values in the dataset (times for kinetic models).
getdim2 returns a "vector" of
x2 values (wavelengths for kinetic models).
Author(s)
Katharine M. Mullen, Ivo H. M. van Stokkum
See Also
fitModel
Examples
## Example showing the addition of non-negativity constraints to
## conditionally linear parameters (here the spectra associated with
## a kinetic model)
## For the 1st simulated dataset, the constraints offer a modest improvement
## in the estimated spectra, whereas for the 2nd simulated dataset, they
## prevent a catastrophe in which the estimated components are hugely
## compensating.
##############################
## load TIMP
##############################
require(TIMP)
##############################
## set random seed for reproducability of noise
##############################
set.seed(80)
##############################
## SIMULATE DATA, noise realization 1
##############################
dt4 <- simndecay_gen(kinpar = c(.4, .8, 2), seqmod = FALSE, tmax
= 2, deltat = .04, specpar = list(c(25000, 3000, .01), c(22000,
3000, .01), c(18000, 3000, .01)), lmin=350, lmax=550, deltal = 2,
sigma=.01)
##############################
## SPECIFY INITIAL MODEL
##############################
mod1 <- initModel(mod_type = "kin", kinpar = c(.4, .8, 2),
seqmod=FALSE)
##############################
## FIT INITIAL MODEL
##############################
sT <- fitModel(list(dt4), list(mod1), opt=kinopt(iter=50, plot=FALSE))
##############################
## EXTRACT ESTIMATED SPECTRA
## these spectra have some negative values
##############################
sTcp <- getCLP(sT)
## plot the estimated spectra with the values used in
## simulation (before adding noise) for comparison
matplot(dt4@x2, sTcp, xlab = "wavelength (nm)", col = 2:4, type="l",
ylab="",lty=1, main =
paste("Estimated spectra, adding no constraints\n"))
matplot(dt4@x2,dt4@E2, add=TRUE, type="l", col=1, lty=2)
abline(0,0)
##############################
## FIT INITIAL MODEL
## adding constraints to non-negativity of the
## spectra via the opt option nnls=TRUE
##############################
sV <- fitModel(list(dt4), list(mod1), opt=kinopt(iter=50, nnls=TRUE,
plot=FALSE))
##############################
## EXTRACT ESTIMATED SPECTRA
## these spectra have no negative values
##############################
sVcp <- getCLP(sV)
## plot the estimated spectra with the values used in
## simulation (before adding noise) for comparison
matplot(dt4@x2, sVcp, xlab = "wavelength (nm)", col = 2:4, type="l",
ylab="",lty=1,
main = paste("Estimated spectra, with non-negativity constraints\n"))
matplot(dt4@x2,dt4@E2, add=TRUE, type="l", col=1, lty=2)
abline(0,0)
##############################
## SIMULATE DATA, noise realization 2
##############################
dt4_2 <- simndecay_gen(kinpar = c(.4, .8, 2), seqmod = FALSE, tmax
= 2, deltat = .04, specpar = list(c(25000, 3000, .01), c(22000,
3000, .01), c(18000, 3000, .01)), lmin=350, lmax=550, deltal = 2,
sigma=.01)
##############################
## SPECIFY INITIAL MODEL
##############################
mod1 <- initModel(mod_type = "kin", kinpar = c(.4, .8, 2),
seqmod=FALSE)
##############################
## FIT INITIAL MODEL
##############################
sT <- fitModel(list(dt4_2), list(mod1), opt=kinopt(iter=50,plot=FALSE))
##############################
## EXTRACT ESTIMATED SPECTRA
## these spectra have some negative values
##############################
sTcp <- getCLP(sT)
## plot the estimated spectra with the values used in
## simulation (before adding noise) for comparison
matplot(dt4@x2, sTcp, xlab = "wavelength (nm)", col = 2:4, type="l",
ylab="",lty=1, main =
paste("Estimated spectra, adding no constraints\n"))
matplot(dt4@x2,dt4@E2, add=TRUE, type="l", col=1, lty=2)
abline(0,0)
##############################
## FIT INITIAL MODEL
## adding constraints to non-negativity of the
## spectra via the opt option nnls=TRUE
##############################
sV <- fitModel(list(dt4_2), list(mod1), opt=kinopt(iter=50, nnls=TRUE,
plot=FALSE))
##############################
## EXTRACT ESTIMATED SPECTRA
## these spectra have no negative values
##############################
sVcp <- getCLP(sV)
## plot the estimated spectra with the values used in
## simulation (before adding noise) for comparison
matplot(dt4@x2, sVcp, xlab = "wavelength (nm)", col = 2:4, type="l",
ylab="",lty=1,
main = paste("Estimated spectra, with non-negativity constraints\n"))
matplot(dt4@x2,dt4@E2, add=TRUE, type="l", col=1, lty=2)
abline(0,0)
# end donttest
##############################
## 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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