Description Construction Methods Author(s) Examples
The ResidualMatrix class supports delayed calculation of the residuals from a linear model fit. This serves as a lightweight 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 matrixlike object.
This can alternatively be a ResidualMatrixSeed, in which case design
and keep
are ignored.
design
, a numeric matrix containing the experimental design,
to be used for linear model fitting on each column of x
.
This defaults to an interceptonly 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.
If NULL
, all rows of x
are used.
When keep=NULL
, the ResidualMatrix contains values equivalent to lm.fit(x=design, y=x)$residuals
.
In the following code chunks, x
is a ResidualMatrix object:
x[i, j, .., drop=FALSE]
will return a ResidualMatrix object for the specified row and column subsets,
or a numeric vector if either i
or j
are of length 1.
t(x)
will return a ResidualMatrix object with transposed contents.
dimnames(x) < value
will return a ResidualMatrix object where the rows and columns are renamed by value
,
a list of two character vectors (or NULL
).
colSums(x)
, colMeans(x)
, rowSums(x)
and rowMeans(x)
will return the relevant statistics for a ResidualMatrix x
.
%*%
, crossprod
and tcrossprod
can also be applied
where one or both of the arguments are ResidualMatrix objects.
ResidualMatrix objects are derived from DelayedMatrix objects and support all of valid operations on the latter. All operations not listed here will use the underlying DelayedArray machinery. Unary or binary operations will generally create a new DelayedMatrix instance containing a ResidualMatrixSeed.
Aaron Lun
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20  design < model.matrix(~gl(5, 50))
library(Matrix)
y0 < rsparsematrix(nrow(design), 200, 0.1)
y < ResidualMatrix(y0, design)
y
# For comparison:
fit < lm.fit(x=design, y=as.matrix(y0))
DelayedArray(fit$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)

Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.