R/linear.fit.R

In isglobal-brge/CNVassoc: Association analysis of CNVs and imputed SNPs incorporating uncertainty

linear.fit <- function(x, y, weights, tol = 1e-08, 
        max.iter = 25, verbose = FALSE) {
        
        if (!is.matrix(x)) 
            x <- as.matrix(x)
        
        nvars <- NCOL(x)
        nobs <- NROW(x)
        if (missing(weights)) 
            weights = rep(1, nobs)
        
        fit <- .Fortran("dqrls", qr = x * weights, 
            n = nobs, p = nvars, y = weights * 
                y, ny = as.integer(1), tol = min(1e-07, 
                glm.control()$epsilon/1000), 
            coefficients = double(nvars), residuals = double(nobs), 
            effects = double(nobs), rank = integer(1), 
            pivot = seq_len(nvars), qraux = double(nvars), 
            work = double(2 * nvars), PACKAGE = "CNVassoc")
        
        coeffic <- fit$coefficients
        mu <- drop(x %*% coeffic)
        sigma <- sqrt(sum((y - mu)^2 * weights)/sum(weights))
        
        coeffic <- as.vector(coeffic)
        names(coeffic) <- colnames(x)
        
        return(list(coeffic = coeffic, sigma = sigma))
        
}