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flm

Fast (Weighted) Linear Model Fitting

Description

flm is a fast linear model command that takes matrices as input and (by default) only returns a coefficient matrix. 6 different efficient fitting methods are implemented: 4 using base R linear algebra, and 2 utilizing the RcppArmadillo and RcppEigen packages. The function itself only has an overhead of 5-10 microseconds, and is thus well suited as a bootstrap workhorse.

Usage

flm(y, X, w = NULL, add.icpt = FALSE, return.raw = FALSE, 
    method = c("lm", "solve", "qr", "arma", "chol", "eigen"),
    eigen.method = 3L, ...)

Arguments

`y`	a response vector or matrix. Multiple dependent variables are only supported by methods "lm", "solve", "qr" and "chol".
`X`	a matrix of regressors.
`w`	a weight vector.
`add.icpt`	logical. `TRUE` adds an intercept column named '(Intercept)' to `X`.

return.raw

logical. TRUE returns the original output from the different methods. For 'lm', 'arma' and 'eigen', this includes additional statistics such as residuals, fitted values or standard errors. The other methods just return coefficients but in different formats.

method

an integer or character string specifying the method of computation:

Int.	String	Description
1	"lm"	uses `.lm.fit`.
2	"solve"	`solve(crossprod(X), crossprod(X, y))`.
3	"qr"	`qr.coef(qr(X), y)`.
4	"arma"	uses `RcppArmadillo::fastLmPure`.
5	"chol"	`chol2inv(chol(crossprod(X))) %*% crossprod(X, y)` (quite fast but requires `crossprod(X)` to be positive definite).
6	"eigen"	uses `RcppEigen::fastLmPure` (very fast but potentially unstable, depending on the method).

eigen.method

integer. Select the method of computation used by RcppEigen::fastLmPure:

Int.		Description
0		column-pivoted QR decomposition.
1		unpivoted QR decomposition.
2		LLT Cholesky.
3		LDLT Cholesky.
4		Jacobi singular value decomposition (SVD).
5		method based on the eigenvalue-eigenvector decomposition of X'X.

See vignette("RcppEigen-Introduction", package = "RcppEigen") for details on these methods and benchmark results. Run source(system.file("examples", "lmBenchmark.R", package = "RcppEigen")) to re-run the benchmark on your machine.

...

further arguments passed to other methods. Sensible choices are tol = value - a numerical tolerance for the solution - applicable with methods "lm", "solve" and "qr" (default is 1e-7), or LAPACK = TRUE with method "qr" to use LAPACK routines to for the qr decomposition (typically faster than LINPACK (the default)).

Value

If return.raw = FALSE, a matrix of coefficients with the rows corresponding to the columns of X, otherwise the raw results from the various methods are returned.

Note

Method "qr" supports sparse matrices, so for an X matrix with many dummy variables consider method "qr" passing as(X, "dgCMatrix") instead of just X.

Examples

coef <- flm(mtcars$mpg, qM(mtcars[c("hp","carb")]),
            mtcars$wt, add.icpt = TRUE)
coef

lmcoef <- coef(lm(mpg ~ hp + carb, weights = wt, mtcars))
lmcoef

all.equal(drop(coef), lmcoef)

 
all_obj_equal(lapply(1:6, function(i)
  flm(mtcars$mpg, qM(mtcars[c("hp","carb")]),
      mtcars$wt, add.icpt = TRUE, method = i)))

collapse

Advanced and Fast Data Transformation

v1.5.3

GPL (>= 2) | file LICENSE

Authors

Sebastian Krantz [aut, cre], Matt Dowle [ctb], Arun Srinivasan [ctb], Laurent Berge [ctb], Dirk Eddelbuettel [ctb], Josh Pasek [ctb], Kevin Tappe [ctb], R Core Team and contributors worldwide [ctb], Martyn Plummer [cph], 1999-2016 The R Core Team [cph]

Initial release

2021-03-05

flm