preProcess: Performs preprocessing on data stored as an objects of class...
In TIMP: Fitting Separable Nonlinear Models in Spectroscopy and Microscopy
Description
Usage
Arguments
Value
Author(s)
See Also
Examples
Description
Performs data sampling, selection, baseline correction,
scaling, and data correction on an object of class dat.
Usage
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preProcess(data, sample = 1, sample_time = 1, sample_lambda = 1,
sel_time = vector(), sel_lambda = vector(), baselinetime = vector(),
baselinelambda = vector(), scalx = NULL, scalx2 = NULL,
sel_lambda_ab = vector(), sel_time_ab = vector(), rm_x2=vector(),
rm_x = vector(), svdResid = list(), numV = 0, sel_special = list(),
doubleDiff = FALSE, doubleDiffFile = "doubleDiff.txt")
Arguments
data
Object of class dat
sample
integer describing sampling interval to take in both time and
x2; e.g., sample=2
will sample every 2nd time and every 2nd point in
x2.
sample_time
integer describing sampling interval in time; e.g.,
sample_time=2 will sample every 2nd element of the time vector.
sample_lambda
integer describing sampling interval in x2;
e.g., sample_lambda=2 will sample every 2nd element in the
x2 vector.
sel_time
vector of length 2 describing the first and last time
index of data to select; e.g., sel_time=c(5,120) will select
data at times indexed 5-120.
sel_lambda
vector of length 2 describing the first and last x2
index of data to select; e.g., sel_lambda=c(5,120)
will select data at x2
indexed 5-120.
baselinetime
a vector of form c(timeIndexmin, timeIndexmax,
lambdaIndexmin, lambdaIndexmax). The average of data between
x2 indexes
lambdaIndexmin and lambdaIndexmax
is subtracted from data with
time index between timeIndexmin and timeIndexmax.
baselinelambda
a vector of form c(timeIndexmin, timeIndexmax,
lambdaIndexmin, lambdaIndexmax). The average of data between time indexes
timeIndexmin and timeIndexmax
is subtracted from data with x2 index
between lambdaIndexmin and lambdaIndexmax.
scalx
numeric by which to linearly scale the x axis
(which often represents time), so that newx = oldx * scalx
scalx2
vector of length 2 by which to linearly scale the
x2 axis, so that newx2 = oldx2 * scalx2[1] + scalx2[2]
sel_lambda_ab
vector of length 2 describing the absolute values
(e.g., wavelengths, wavenumbers, etc.) between which data should be
selected. e.g., sel_lambda_ab = c(400, 600) will select data
associated with x2 values between 400 and 600.
sel_time_ab
vector of length 2 describing the absolute times
between which data should be
selected. e.g., sel_time_ab = c(50, 5000) will select data
associated with time values between 50 and 5000 picoseconds.
rm_x2
vector of x2 indices to remove from the data
rm_x
vector of x indices to remove from the data
svdResid
list returned from the getResid function, containing
residuals to be used in data correction.
numV
numeric specifying how many singular vectors to use in data
correction. Maximum is five.
sel_special
list of lists specifying x indices to remove
from individual wavelength ranges, e.g.,
sel_special = list(c(400,600,10,12),c(600,800,11,13))
indicates that between wavelength 400 and 600, time indices between 10
and 12 should be removed from the data, and between wavelengths 600 and
800, time indices between 11 and 13 should be removed from the data.
Note that the number of time indices to remove should be the same in
each wavelength interval specified. Also note that the time vector
associated with the data after the first set of indices is removed will
be associated with the resulting dataset.
doubleDiff
logical indicating whether the data should be
converted to represent differences between times.
doubleDiffFile
character string indicating the file name of
time difference data to create in the case that doubleDiff=TRUE.
Value
object of class dat.
Author(s)
Katharine M. Mullen, Ivo H. M. van Stokkum
See Also
Examples
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##############################
## READ DATA
##############################
data("target")
##############################
## PREPROCESS DATA
##############################
# select certain wavelengths for modeling
C1_1 <- preProcess(data = C1, baselinelambda = c(1,12,1,32) )
C1_1 <- preProcess(data = C1_1, sel_lambda = c(8, 27))
C1_1 <- preProcess(data = C1_1, rm_x = c(40, 41, 101, 116))
C1_1 <- preProcess(data = C1_1, sel_time_ab = c(-10, 100000))
C2_1 <- preProcess(data = C2, sel_lambda = c(2, 32))
C2_1 <- preProcess(data = C2_1, baselinelambda = c(1,12,1,32) )
C2_1 <- preProcess(data = C2_1, sel_time_ab = c(-10, 100000))
C3_1 <- preProcess(data = C3, sel_lambda = c(1, 25))
C3_1 <- preProcess(data = C3_1, baselinelambda = c(1,12,1,32) )
## Not run:
##############################
## SPECIFY K Matrix and J vector
##############################
## initialize 2 7x7 arrays to 0
delK <- array(0, dim=c(7,7,2))
## the matrix is indexed:
## delK[ ROW K MATRIX, COL K MATRIX, matrix number]
## in the first matrix, put the index of compartments
## that are non-zero
## the transfer rate of the compartment is governed by
## kinpar[index]
delK[1,1,1] <- 4
delK[5,1,1] <- 1
delK[2,2,1] <- 4
delK[5,2,1] <- 2
delK[3,3,1] <- 4
delK[5,3,1] <- 3
delK[4,4,1] <- 4
delK[6,5,1] <- 5
delK[7,6,1] <- 6
delK[7,7,1] <- 7
## print out the resulting array to make sure it's right
delK
jvector <- c(.48443195136500550341, .28740782363398824522,
.13749071230100625137, 0.9066953510E-01, 0, 0, 0)
datalist <- list(C1, C2, C3)
## for plotting selected traces, get a vector of all the wavenumbers
allx2 <- vector()
for(i in 1:length(datalist))
allx2 <- append(allx2,datalist[[i]]@x2)
allx2 <- sort(unique(allx2))
##############################
## SPECIFY INITIAL MODEL
## note that low is the larger wavenumber in the clpequ spec!
##############################
model1 <- initModel(mod_type = "kin",
kinpar=c( 0.13698630, 0.3448275849E-01, 0.1020408142E-01, 0.2941176528E-02,
0.17000, 0.015, 0.1074082902E-03),
fixed = list(prel = 1:6, clpequ=1:3, kinpar=1:7, irfpar=1, parmu=1),
irfpar=c(0.4211619198, 0.6299000233E-01),
prelspec = list(
list(what1="kinpar", ind1=1, what2 = "kinpar", ind2=4,
start=c(-1,0.1369863003)),
list(what1="kinpar", ind1=2, what2 = "kinpar", ind2=4,
start=c(-1,0.3448275849E-01)),
list(what1="kinpar", ind1=3, what2 = "kinpar", ind2=4,
start=c(-1,0.1020408142E-01))
),
parmu = list(c(-0.1411073953)),
lambdac = 1290,
kmat = delK,
jvec = jvector,
positivepar="kinpar",
weightpar=list( c(-20,1.4,1,2000,.2)),
clpequspec =list(
list(to=2, from=1, low=100, high=10000),
list(to=3, from=1, low=100, high=10000),
list(to=4, from=1, low=100, high=10000)),
clpequ = c(1,1,1),
cohspec = list( type = "irf"))
##############################
## GET RESID
## same format as call to fitModel, but does not plot
##############################
serResid <- getResid(list(C1_1, C2_1, C3_1), list(model1),
modeldiffs = list(thresh = 0.00005,
dscal = list(list(to=2,from=1,value=4),
list(to=3,from=1,value=0.8000000119)),
free = list(
list(what="irfpar", ind=1, start= c(0.1231127158), dataset=2),
list(what="parmu", ind=c(1,1), start= c(0.1219962388), dataset=2),
list(what="irfpar", ind=1, start= c(0.3724052608), dataset=3),
list(what="parmu", ind=c(1,1), start= c(0.8844097704E-01), dataset=3)),
change = list(
list(what="fixed", spec=list(clpequ=1:3, kinpar=1:7, irfpar=1:2,
parmu=1, drel = 1, prel=1:6), dataset=2:3))),
opt=kinopt(iter=0, title="Cosimo Spectra, Not Normalized, with Error",
stderrclp=TRUE, kinspecerr=TRUE, writespec = TRUE,
plotkinspec = TRUE,plotcohcolspec=FALSE,
selectedtraces = seq(1, length(allx2), by=2),
specinterpol = TRUE, specinterpolpoints=FALSE,
divdrel=TRUE, xlab="wavenumber",writeclperr = TRUE,
makeps = "err", linrange = 1, superimpose=1:3))
##############################
## MAKE CORRECTED DATASETS USING RESID INFO
##############################
C1_3 <- preProcess(data = C1_1, svdResid = serResid[[1]], numV = 2)
C2_3 <- preProcess(data = C2_1, svdResid = serResid[[2]], numV = 2)
C3_3 <- preProcess(data = C3_1, svdResid = serResid[[3]], numV = 2)
##############################
## FIT MODEL
##############################
serRes<-fitModel(list(C1_3, C2_3, C3_3), list(model1),
modeldiffs = list(thresh = 0.00005,
dscal = list(list(to=2,from=1,value=4),
list(to=3,from=1,value=0.8000000119)),
free = list(
list(what="irfpar", ind=1, start= c(0.1231127158), dataset=2),
list(what="parmu", ind=c(1,1), start= c(0.1219962388), dataset=2),
list(what="irfpar", ind=1, start= c(0.3724052608), dataset=3),
list(what="parmu", ind=c(1,1), start= c(0.8844097704E-01), dataset=3)),
change = list(
list(what="fixed", spec=list(clpequ=1:3, kinpar=1:7, irfpar=1:2,
parmu=1, drel = 1, prel=1:6), dataset=2:3))),
opt=kinopt(iter=0, title="Cosimo Spectra, Not Normalized, with Error",
stderrclp=TRUE, kinspecerr=TRUE, writespec = TRUE,
plotkinspec = TRUE,plotcohcolspec=FALSE, writerawcon = TRUE,
selectedtraces = seq(1, length(allx2), by=2),
specinterpol = TRUE, specinterpolpoints=FALSE,
divdrel=TRUE, xlab="wavenumber",writeclperr = TRUE,
makeps = "h20", linrange = 1, superimpose=1:3))
## End(Not run)
TIMP documentation built on May 2, 2019, 5:55 p.m.
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Description Usage Arguments Value Author(s) See Also Examples
Description
Performs data sampling, selection, baseline correction,
scaling, and data correction on an object of class dat.
Usage
1 2 3 4 5 6 | preProcess(data, sample = 1, sample_time = 1, sample_lambda = 1,
sel_time = vector(), sel_lambda = vector(), baselinetime = vector(),
baselinelambda = vector(), scalx = NULL, scalx2 = NULL,
sel_lambda_ab = vector(), sel_time_ab = vector(), rm_x2=vector(),
rm_x = vector(), svdResid = list(), numV = 0, sel_special = list(),
doubleDiff = FALSE, doubleDiffFile = "doubleDiff.txt")
|
Arguments
data |
Object of class |
sample |
integer describing sampling interval to take in both time and
|
sample_time |
integer describing sampling interval in time; e.g.,
|
sample_lambda |
integer describing sampling interval in |
sel_time |
vector of length 2 describing the first and last time
index of data to select; e.g., |
sel_lambda |
vector of length 2 describing the first and last |
baselinetime |
a vector of form |
baselinelambda |
a vector of form |
scalx |
numeric by which to linearly scale the |
scalx2 |
vector of length 2 by which to linearly scale the
|
sel_lambda_ab |
vector of length 2 describing the absolute values
(e.g., wavelengths, wavenumbers, etc.) between which data should be
selected. e.g., |
sel_time_ab |
vector of length 2 describing the absolute times
between which data should be
selected. e.g., |
rm_x2 |
vector of |
rm_x |
vector of |
svdResid |
list returned from the |
numV |
numeric specifying how many singular vectors to use in data correction. Maximum is five. |
sel_special |
list of lists specifying |
doubleDiff |
logical indicating whether the data should be converted to represent differences between times. |
doubleDiffFile |
character string indicating the file name of
time difference data to create in the case that |
Value
object of class dat.
Author(s)
Katharine M. Mullen, Ivo H. M. van Stokkum
See Also
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 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 | ##############################
## READ DATA
##############################
data("target")
##############################
## PREPROCESS DATA
##############################
# select certain wavelengths for modeling
C1_1 <- preProcess(data = C1, baselinelambda = c(1,12,1,32) )
C1_1 <- preProcess(data = C1_1, sel_lambda = c(8, 27))
C1_1 <- preProcess(data = C1_1, rm_x = c(40, 41, 101, 116))
C1_1 <- preProcess(data = C1_1, sel_time_ab = c(-10, 100000))
C2_1 <- preProcess(data = C2, sel_lambda = c(2, 32))
C2_1 <- preProcess(data = C2_1, baselinelambda = c(1,12,1,32) )
C2_1 <- preProcess(data = C2_1, sel_time_ab = c(-10, 100000))
C3_1 <- preProcess(data = C3, sel_lambda = c(1, 25))
C3_1 <- preProcess(data = C3_1, baselinelambda = c(1,12,1,32) )
## Not run:
##############################
## SPECIFY K Matrix and J vector
##############################
## initialize 2 7x7 arrays to 0
delK <- array(0, dim=c(7,7,2))
## the matrix is indexed:
## delK[ ROW K MATRIX, COL K MATRIX, matrix number]
## in the first matrix, put the index of compartments
## that are non-zero
## the transfer rate of the compartment is governed by
## kinpar[index]
delK[1,1,1] <- 4
delK[5,1,1] <- 1
delK[2,2,1] <- 4
delK[5,2,1] <- 2
delK[3,3,1] <- 4
delK[5,3,1] <- 3
delK[4,4,1] <- 4
delK[6,5,1] <- 5
delK[7,6,1] <- 6
delK[7,7,1] <- 7
## print out the resulting array to make sure it's right
delK
jvector <- c(.48443195136500550341, .28740782363398824522,
.13749071230100625137, 0.9066953510E-01, 0, 0, 0)
datalist <- list(C1, C2, C3)
## for plotting selected traces, get a vector of all the wavenumbers
allx2 <- vector()
for(i in 1:length(datalist))
allx2 <- append(allx2,datalist[[i]]@x2)
allx2 <- sort(unique(allx2))
##############################
## SPECIFY INITIAL MODEL
## note that low is the larger wavenumber in the clpequ spec!
##############################
model1 <- initModel(mod_type = "kin",
kinpar=c( 0.13698630, 0.3448275849E-01, 0.1020408142E-01, 0.2941176528E-02,
0.17000, 0.015, 0.1074082902E-03),
fixed = list(prel = 1:6, clpequ=1:3, kinpar=1:7, irfpar=1, parmu=1),
irfpar=c(0.4211619198, 0.6299000233E-01),
prelspec = list(
list(what1="kinpar", ind1=1, what2 = "kinpar", ind2=4,
start=c(-1,0.1369863003)),
list(what1="kinpar", ind1=2, what2 = "kinpar", ind2=4,
start=c(-1,0.3448275849E-01)),
list(what1="kinpar", ind1=3, what2 = "kinpar", ind2=4,
start=c(-1,0.1020408142E-01))
),
parmu = list(c(-0.1411073953)),
lambdac = 1290,
kmat = delK,
jvec = jvector,
positivepar="kinpar",
weightpar=list( c(-20,1.4,1,2000,.2)),
clpequspec =list(
list(to=2, from=1, low=100, high=10000),
list(to=3, from=1, low=100, high=10000),
list(to=4, from=1, low=100, high=10000)),
clpequ = c(1,1,1),
cohspec = list( type = "irf"))
##############################
## GET RESID
## same format as call to fitModel, but does not plot
##############################
serResid <- getResid(list(C1_1, C2_1, C3_1), list(model1),
modeldiffs = list(thresh = 0.00005,
dscal = list(list(to=2,from=1,value=4),
list(to=3,from=1,value=0.8000000119)),
free = list(
list(what="irfpar", ind=1, start= c(0.1231127158), dataset=2),
list(what="parmu", ind=c(1,1), start= c(0.1219962388), dataset=2),
list(what="irfpar", ind=1, start= c(0.3724052608), dataset=3),
list(what="parmu", ind=c(1,1), start= c(0.8844097704E-01), dataset=3)),
change = list(
list(what="fixed", spec=list(clpequ=1:3, kinpar=1:7, irfpar=1:2,
parmu=1, drel = 1, prel=1:6), dataset=2:3))),
opt=kinopt(iter=0, title="Cosimo Spectra, Not Normalized, with Error",
stderrclp=TRUE, kinspecerr=TRUE, writespec = TRUE,
plotkinspec = TRUE,plotcohcolspec=FALSE,
selectedtraces = seq(1, length(allx2), by=2),
specinterpol = TRUE, specinterpolpoints=FALSE,
divdrel=TRUE, xlab="wavenumber",writeclperr = TRUE,
makeps = "err", linrange = 1, superimpose=1:3))
##############################
## MAKE CORRECTED DATASETS USING RESID INFO
##############################
C1_3 <- preProcess(data = C1_1, svdResid = serResid[[1]], numV = 2)
C2_3 <- preProcess(data = C2_1, svdResid = serResid[[2]], numV = 2)
C3_3 <- preProcess(data = C3_1, svdResid = serResid[[3]], numV = 2)
##############################
## FIT MODEL
##############################
serRes<-fitModel(list(C1_3, C2_3, C3_3), list(model1),
modeldiffs = list(thresh = 0.00005,
dscal = list(list(to=2,from=1,value=4),
list(to=3,from=1,value=0.8000000119)),
free = list(
list(what="irfpar", ind=1, start= c(0.1231127158), dataset=2),
list(what="parmu", ind=c(1,1), start= c(0.1219962388), dataset=2),
list(what="irfpar", ind=1, start= c(0.3724052608), dataset=3),
list(what="parmu", ind=c(1,1), start= c(0.8844097704E-01), dataset=3)),
change = list(
list(what="fixed", spec=list(clpequ=1:3, kinpar=1:7, irfpar=1:2,
parmu=1, drel = 1, prel=1:6), dataset=2:3))),
opt=kinopt(iter=0, title="Cosimo Spectra, Not Normalized, with Error",
stderrclp=TRUE, kinspecerr=TRUE, writespec = TRUE,
plotkinspec = TRUE,plotcohcolspec=FALSE, writerawcon = TRUE,
selectedtraces = seq(1, length(allx2), by=2),
specinterpol = TRUE, specinterpolpoints=FALSE,
divdrel=TRUE, xlab="wavenumber",writeclperr = TRUE,
makeps = "h20", linrange = 1, superimpose=1:3))
## End(Not run)
|
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