DClsei {quantiseqr} | R Documentation |
Solve Least Squares with Equality and Inequality Constraints (LSEI) problem
DClsei(b, A, G, H, scaling = NULL)
b |
Numeric vector containing the right-hand side of the quadratic function to be minimized. |
A |
Numeric matrix containing the coefficients of the quadratic function to be minimized. |
G |
Numeric matrix containing the coefficients of the inequality constraints. |
H |
Numeric vector containing the right-hand side of the inequality constraints. |
scaling |
A vector of scaling factors to by applied to the estimates. Its length should equal the number of columns of A. |
The limSolve::lsei()
function is used as underlying framework. Please
refer to that function for more details.
A vector containing the solution of the LSEI problem.
data(dataset_racle) mixture <- dataset_racle$expr_mat signature.file <- system.file( "extdata", "TIL10_signature.txt", package = "quantiseqr", mustWork = TRUE) signature <- read.table(signature.file, header = TRUE, sep = "\t", row.names = 1) scaling.file <- system.file( "extdata", "TIL10_mRNA_scaling.txt", package = "quantiseqr", mustWork = TRUE) scaling <- as.vector( as.matrix(read.table(scaling.file, header = FALSE, sep = "\t", row.names = 1))) cgenes <- intersect(rownames(signature), rownames(mixture)) b <- as.vector(as.matrix(mixture[cgenes,1, drop=FALSE])) A <- as.matrix(signature[cgenes,]) G <- matrix(0, ncol = ncol(A), nrow = ncol(A)) diag(G) <- 1 G <- rbind(G, rep(-1, ncol(G))) H <- c(rep(0, ncol(A)), -1) # cellfrac <- quantiseqr:::DClsei(b = b, A = A, G= G, H = H, scaling = scaling)