Description Usage Arguments Value Examples
Optimisation is done by solving a least square problem with inequality constraint thanks to lsei package
1 |
Zt |
The transpose of Z where Z is a matrix with K rows (number of subclones) and M x J columns (number of signal x number of segments) |
Y |
a matrix with n rows (number of samples) and M x J columns (number of signal x number of segments) |
type |
integer code determining algorithm to use 1=lsei, 2=solve.QP from R-package quadprog |
a matrix with n rows (number of samples) and K columns (number of subclones)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 | ## Example with M = 1 signal
J <- 11L ## Number of segments
K <- 4L ## Number of subclones
n <- 4L ## Number of samples
Z <- matrix(1, nrow = K, ncol = J)
Z[2, 2] <- 2
Z[3, 5:6] <- 2
Z[4, 9:10] <- 2
W <- matrix(0, nrow = n, ncol = K)
W[1,1] <- W[2,2] <- W[3,3] <- W[4,4] <- 1
E <- matrix(rnorm(n*J, sd = 0.1), nrow = n, ncol = J)
Y <- W %*% Z + E
What <- c3co:::get.W(t(Z), Y)
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