Description Usage Arguments Details Value Target dimension Subsetting dimensions See Also Examples
Reverse transformation of principalcurve fit.
1 2 3 4  ## S3 method for class 'matrix'
backtransformPrincipalCurve(X, fit, dimensions=NULL, targetDimension=NULL, ...)
## S3 method for class 'numeric'
backtransformPrincipalCurve(X, ...)

X 
An NxK 
fit 
An MxL principalcurve fit object of class

dimensions 
An (optional) subset of of D dimensions all in [1,L] to be returned (and backtransform). 
targetDimension 
An (optional) index specifying the dimension
in [1,L] to be used as the target dimension of the 
... 
Passed internally to 
Each column in X ("dimension") is backtransformed independently of the others.
The backtransformed NxK (or NxD) matrix
.
By default, the backtransform is such that afterward the signals are
approximately proportional to the (first) principal curve as fitted
by fitPrincipalCurve
(). This scale and origin of this
principal curve is not uniquely defined.
If targetDimension
is specified, then the backtransformed signals
are approximately proportional to the signals of the target dimension,
and the signals in the target dimension are unchanged.
Argument dimensions
can be used to backtransform a subset of
dimensions (K) based on a subset of the fitted dimensions (L).
If K = L, then both X
and fit
is subsetted.
If K <> L, then it is assumed that X
is already
subsetted/expanded and only fit
is subsetted.
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  # Consider the case where K=4 measurements have been done
# for the same underlying signals 'x'. The different measurements
# have different systematic variation
#
# y_k = f(x_k) + eps_k; k = 1,...,K.
#
# In this example, we assume nonlinear measurement functions
#
# f(x) = a + b*x + x^c + eps(b*x)
#
# where 'a' is an offset, 'b' a scale factor, and 'c' an exponential.
# We also assume heteroscedastic zeromean noise with standard
# deviation proportional to the rescaled underlying signal 'x'.
#
# Furthermore, we assume that measurements k=2 and k=3 undergo the
# same transformation, which may illustrate that the come from
# the same batch. However, when *fitting* the model below we
# will assume they are independent.
# Transforms
a < c(2, 15, 15, 3)
b < c(2, 3, 3, 4)
c < c(1, 2, 2, 1/2)
K < length(a)
# The true signal
N < 1000
x < rexp(N)
# The noise
bX < outer(b,x)
E < apply(bX, MARGIN=2, FUN=function(x) rnorm(K, mean=0, sd=0.1*x))
# The transformed signals with noise
Xc < t(sapply(c, FUN=function(c) x^c))
Y < a + bX + Xc + E
Y < t(Y)
#                                  
# Fit principal curve
#                                  
# Fit principal curve through Y = (y_1, y_2, ..., y_K)
fit < fitPrincipalCurve(Y)
# Flip direction of 'lambda'?
rho < cor(fit$lambda, Y[,1], use="complete.obs")
flip < (rho < 0)
if (flip) {
fit$lambda < max(fit$lambda, na.rm=TRUE)fit$lambda
}
L < ncol(fit$s)
#                                  
# Backtransform data according to model fit
#                                  
# Backtransform toward the principal curve (the "common scale")
YN1 < backtransformPrincipalCurve(Y, fit=fit)
stopifnot(ncol(YN1) == K)
# Backtransform toward the first dimension
YN2 < backtransformPrincipalCurve(Y, fit=fit, targetDimension=1)
stopifnot(ncol(YN2) == K)
# Backtransform toward the last (fitted) dimension
YN3 < backtransformPrincipalCurve(Y, fit=fit, targetDimension=L)
stopifnot(ncol(YN3) == K)
# Backtransform toward the third dimension (dimension by dimension)
# Note, this assumes that K == L.
YN4 < Y
for (cc in 1:L) {
YN4[,cc] < backtransformPrincipalCurve(Y, fit=fit,
targetDimension=1, dimensions=cc)
}
stopifnot(identical(YN4, YN2))
# Backtransform a subset toward the first dimension
# Note, this assumes that K == L.
YN5 < backtransformPrincipalCurve(Y, fit=fit,
targetDimension=1, dimensions=2:3)
stopifnot(identical(YN5, YN2[,2:3]))
stopifnot(ncol(YN5) == 2)
# Extract signals from measurement #2 and backtransform according
# its model fit. Signals are standardized to target dimension 1.
y6 < Y[,2,drop=FALSE]
yN6 < backtransformPrincipalCurve(y6, fit=fit, dimensions=2,
targetDimension=1)
stopifnot(identical(yN6, YN2[,2,drop=FALSE]))
stopifnot(ncol(yN6) == 1)
# Extract signals from measurement #2 and backtransform according
# the the model fit of measurement #3 (because we believe these
# two have undergone very similar transformations.
# Signals are standardized to target dimension 1.
y7 < Y[,2,drop=FALSE]
yN7 < backtransformPrincipalCurve(y7, fit=fit, dimensions=3,
targetDimension=1)
stopifnot(ncol(yN7) == 1)
stopifnot(cor(yN7, yN6) > 0.9999)

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