tests/kin_target_script.R

In TIMP: Fitting Separable Nonlinear Models in Spectroscopy and Microscopy

########################################
## Data and model of Delmar Larsen
########################################

##############################
## load TIMP
##############################

require(TIMP)

## read data
delDat <- readData("PCP1.-50000fs.PP.txt")

## scale wavelength axis
delDatP <- preProcess(data = delDat, scalx2 = c(1.19, 404), sample_lambda = 4)

## set up the K matrix.

## initialize 2 5x5 arrays to 0
## replace 5 with the number of columns/rows in the
## desired K matrix

delK <- array(0, dim = c(5, 5, 2))

## the matrix is indexed:
## delK[ ROW K MATRIX, COL K MATRIX, matrix number]

## in the first matrix, put the label of compartments
## that are non-zero

delK[2, 1, 1] <- 1
delK[5, 1, 1] <- 1
delK[3, 2, 1] <- 2
delK[5, 2, 1] <- 2
delK[4, 3, 1] <- 3
delK[5, 3, 1] <- 3
delK[5, 4, 1] <- 4
delK[5, 5, 1] <- 5


## in the second matrix, put the indices of any branching
## parameters you want; these are the parameter values given
## in the vector kinscal and then fixed

delK[2, 1, 2] <- 1
delK[5, 1, 2] <- 3
delK[2, 2, 2] <- 2
delK[3, 2, 2] <- 4
delK[5, 2, 2] <- 5
delK[3, 3, 2] <- 2
delK[4, 3, 2] <- 5
delK[5, 3, 2] <- 7
delK[4, 4, 2] <- 2

## print out the resulting array to make sure it's right

delK

## , , 1
##
##     [,1] [,2] [,3] [,4] [,5]
## [1,]    0    0    0    0    0
## [2,]    1    0    0    0    0
## [3,]    0    2    0    0    0
## [4,]    0    0    3    0    0
## [5,]    1    2    3    4    5
##
## , , 2
##
##     [,1] [,2] [,3] [,4] [,5]
## [1,]    0    0    0    0    0
## [2,]    1    2    0    0    0
## [3,]    0    4    2    0    0
## [4,]    0    0    5    2    0
## [5,]    3    5    7    0    0

## in setting up the model, put the branching parameters
## in kinscal and then fix their values.

delMod <- initModel(
  mod_type = "kin",
  kinpar = c(25, 5, .9731, .2489, .013),
  kinscal = c(.73, .05, .3, .75, .24, .47, .4),
  irfpar = c(-.096, .022), kmat = delK, jvec = c(1, 0, 0, 0, 0),
  seqmod = FALSE,
  lambdac = 500,
  positivepar = c("kinpar"),
  weightpar = list(c(NA, NA, 460, 525, .3)),
  clp0 = list(list(comp = 5, low = 250, high = 550), list(
    comp = 5,
    low = 600, high = 800
  )),
  fixed = list(kinpar = c(1, 2), kinscal = c(1:7), jvector = 1:5)
)

serRes <- fitModel(list(delDatP), list(delMod),
  opt = kinopt(
    iter = 1, linrange = 20,
    makeps = "ser", plotkinspec = TRUE,
    selectedtraces = seq(1, delDatP@nl, by = 20),
    xlab = "time (ps)", ylab = "wavelength"
  )
)

##############################
## 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)
  }
}