| fitMultiDimensionalCone.matrix {aroma.core} | R Documentation |
Fits an affine transformation to multi-dimensional data using robust estimators.
## S3 method for class 'matrix':
fitMultiDimensionalCone(y, alpha=c(0.1, 0.075, 0.05, 0.03, 0.01), q=2, Q=98, ..., flavor=c("sfit", "expectile"))
y |
A numeric NxK matrix with one column for each dimension and
where N is the number of data points. |
alpha |
A numeric vector of decreasing values in (0,1).
This parameter "determines how far we are willing to press the
boundary of the [genotype cone]". Lowering alpha expand
the cone. When alpha goes to zero, all data points will
be on or inside the cone. |
q,Q |
Percentiles in [0,100] for which data points that are below (above) will be assigned zero weight in the fitting of the parameters. |
... |
Additional arguments passed to cfit. |
flavor |
A character string specifying what model/algorithm
should be used to fit the genotype cone. |
Returns the parameter estimates as a named list with elements:
M |
An estimate of the three vertices defining the genotype
triangle. These three vertices are describes as an 2x3 matrix
with column origin, AA, and BB. |
Minv |
The inverse of M. |
origin |
The estimate of the shift. |
W |
The estimate of shear/rotation matrix with columns
AA and BB. |
Winv |
The inverse of W. |
params |
The parameters used for the fit, i.e.
alpha, q, Q, and those passed in .... |
dimData |
The dimension of the input data. |
Henrik Bengtsson (http://www.braju.com/R/)
To backtransform data fitted using this method,
see *backtransformMultiDimensionalCone().
Internally cfit of the sfit package is used.
if (require("sfit")) {
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# Simulate data (taken from the cfit.matrix() example of 'sfit')
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
N <- 1000
# Simulate sequences
nucleotides <- c("A", "C", "G", "T")
g <- sample(nucleotides, size=N, replace=TRUE)
ndim <- length(nucleotides)
# Simulate concentrations of allele A and allele B
X <- matrix(rexp(N), nrow=N, ncol=ndim)
colnames(X) <- nucleotides
for (nucleotide in nucleotides) {
cc <- match(nucleotide, nucleotides);
X[g == nucleotide, -cc] <- 0
}
# The true offset
a0 <- rep(0.3, times=ndim)
# The crosstalk matrix
A <- matrix(c(
0.9, 0.3, 0.2, 0.1,
0.1, 0.8, 0.1, 0.1,
0.3, 0.4, 0.7, 0.1,
0.1, 0.1, 0.6, 0.9
), nrow=ndim, byrow=TRUE)
A <- apply(A, MARGIN=2, FUN=function(u) u / sqrt(sum(u^2)))
# Simulate random errors on the input
xi <- matrix(rnorm(ndim*N, mean=0, sd=0.05), ncol=ndim)
# Generate the noisy crosstalk affected input data
Z <- t(a0 + A %*% t(X + xi))
# Generate the noisy observations of the latter
eps <- matrix(rnorm(ndim*N, mean=0, sd=0.05), ncol=ndim)
Y <- Z + eps
# Fit crosstalk model and calibrate data accordingly
fit <- fitMultiDimensionalCone(Y, flavor="sfit");
Yc <- backtransformMultiDimensionalCone(Y, fit=fit);
lim <- c(-0.5,6)
layout(matrix(c(1,2,3,0,4,5,0,0,6), nrow=3, ncol=3, byrow=TRUE));
par(mar=c(5,4,1,1)+0.1);
for (ii in 1:(ndim-1)) {
for (jj in (ii+1):ndim) {
cc <- c(jj,ii);
labs <- nucleotides[cc];
plot(Y[,cc], cex=0.8, xlim=lim, ylim=lim, xlab=labs[1], ylab=labs[2]);
points(Yc[,cc], cex=0.8, col="blue");
Mcc <- fit$M[c(1,1+cc),cc];
class(Mcc) <- class(fit$M);
lines(Mcc, lwd=2, col="red");
}
}
}