# Functions to print and return parts of the object returned by the fitting routines. 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. ```r 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. ## Returns `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 ## Examples ```r ## 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) } } ``` ## See Also `fitModel`

getResults function