The ResidualMatrix class supports delayed calculation of the residuals from a linear model fit. This serves as a light-weight representation of what would otherwise be a large dense matrix in memory. It also enables efficient matrix multiplication based on features of the the original matrix (e.g., sparsity).
ResidualMatrix(x, design=NULL, keep=NULL) returns a ResidualMatrix object, given:
x, a matrix-like object.
This can alternatively be a ResidualMatrixSeed, in which case
keep are ignored.
design, a numeric matrix containing the experimental design,
to be used for linear model fitting on each column of
This defaults to an intercept-only matrix.
keep, an integer vector specifying the columns of
design to not regress out.
By default, all columns of
design are regressed out.
restrict, an integer or logical vector specifying the rows of
x to use for model fitting.
NULL, all rows of
x are used.
keep=NULL, the ResidualMatrix contains values equivalent to
In the following code chunks,
x is a ResidualMatrix object:
x[i, j, .., drop=FALSE] will return a ResidualMatrix object for the specified row and column subsets,
or a numeric vector if either
j are of length 1.
t(x) will return a ResidualMatrix object with transposed contents.
dimnames(x) <- value will return a ResidualMatrix object where the rows and columns are renamed by
a list of two character vectors (or
will return the relevant statistics for a ResidualMatrix
tcrossprod can also be applied
where one or both of the arguments are ResidualMatrix objects.
ResidualMatrix objects are derived from DelayedMatrix objects and support all of valid operations on the latter. All operations not listed here will use the underlying DelayedArray machinery. Unary or binary operations will generally create a new DelayedMatrix instance containing a ResidualMatrixSeed.
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design <- model.matrix(~gl(5, 50)) library(Matrix) y0 <- rsparsematrix(nrow(design), 200, 0.1) y <- ResidualMatrix(y0, design) y # For comparison: fit <- lm.fit(x=design, y=as.matrix(y0)) DelayedArray(fit$residuals) # Keeping some of the factors: y2 <- ResidualMatrix(y0, design, keep=1:2) y2 DelayedArray(fit$residuals + design[,1:2] %*% fit$coefficients[1:2,]) # Matrix multiplication: crossprod(y) tcrossprod(y) y %*% rnorm(200)
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