DClsei {quantiseqr} | R Documentation

## Solve Least Squares with Equality and Inequality Constraints (LSEI) problem

### Description

Solve Least Squares with Equality and Inequality Constraints (LSEI) problem

### Usage

    DClsei(b, A, G, H, scaling = NULL)

### Arguments

`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.

### Details

The `limSolve::lsei()` function is used as underlying framework. Please refer to that function for more details.

### Value

A vector containing the solution of the LSEI problem.

### Examples

    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)

[Package quantiseqr version 1.2.0 Index]