lmMatrixFit {lol} | R Documentation |
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.
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