`FaSTLMM.flmm`

— Functionflmm: fit linear mixed model using grid of values

y: 2-d array of (rotated) phenotypes X: 2-d array of (rotated) covariates lambda: 1-d array of eigenvalues ngrid: number of grid values to consider

`FaSTLMM.flmm`

— Functionflmm: fit linear mixed model

y: 2-d array of (rotated) phenotypes X: 2-d array of (rotated) covariates lambda: 1-d array of eigenvalues reml: boolean indicating ML or REML estimation

`FaSTLMM.rotateData`

— MethodrotateData: Rotates data with respect to the kinship matrix

y = phenotype matrix X = predictor matrix K = kinship matrix, expected to be symmetric and positive definite

`FaSTLMM.wls`

— Functionwls: Weighted least squares estimation

y = outcome, matrix X = predictors, matrix w = weights (positive, inversely proportional to variance), one-dim vector

The variance estimate is maximum likelihood

`FaSTLMM.rss`

— Methodrss: residual sum of squares

y = outcome, matrix X = predictors, matrix

Calculates the residual sum of squares using a QR decomposition. The outcome matrix can be multivariate in which case the function returns the residual sum of squares of each column. The return values is a vector of length equal to the number of columns of y.