lmMatrixFit: Multiple lm fit for penalized regressions
In lol: Lots Of Lasso

Description Usage Arguments Value Author(s) See Also Examples

Refit the regressions given matrices of responses, predictors, and the coefficients/interactions matrix. This is typically used after the lasso, since the coefficients were shrinked.

1	lmMatrixFit(y, x = NULL, mat, th = NULL)

`y`	Input response matrix, typically expression data with genes/variables in columns and samples/measurements in rows. Or when input x is NULL, y should be an object of two lists: y: expression data and x: copy number data
`x`	Input predictor matrix, typically copy number data, genes/predictors in columns and samples/measurements in rows. Can be NULL
`mat`	Coefficient matrix, number of columns is the number of predictors (y) and number of rows is the number of responses (x)
`th`	The threshold to use in order to determine which coefficients are non-zero, so the corresponding predictors are used

`coefMat`	A coefficient matrix, rows are responses and columns are predictors
`resMat`	A residual matrix, each row is the residuals of a response.
`pvalMat`	Matrix of p-values for each coefficients

Yinyin Yuan

lm, matrixLasso

data(chin07)
data <- list(y=t(chin07$ge), x=t(chin07$cn))
res <- matrixLasso(data, method='cv', nFold=5)
res
res.lm <- lmMatrixFit(y=data, mat=abs(res$coefMat), th=0.01)
res.lm