get_ns_spline | R Documentation |

Creates a function which can evaluate a natural cubic spline like
`ns`

.

The result may differ between different BLAS and LAPACK implementations as the QR decomposition is not unique. However, the column space of the returned matrix will always be the same regardless of the BLAS and LAPACK implementation.

get_ns_spline(knots, intercept = TRUE, do_log = TRUE)

`knots` |
sorted numeric vector with boundary and interior knots. |

`intercept` |
logical for whether to include an intercept. |

`do_log` |
logical for whether to evaluate the spline at |

# compare with splines library(splines) library(SimSurvNMarker) xs <- seq(1, 5, length.out = 10L) bks <- c(1, 5) iks <- 2:4 # we get the same if(require(Matrix)){ r1 <- unclass(ns(xs, knots = iks, Boundary.knots = bks, intercept = TRUE)) r2 <- get_ns_spline(knots = sort(c(iks, bks)), intercept = TRUE, do_log = FALSE)(xs) cat("Rank is correct: ", rankMatrix(cbind(r1, r2)) == NCOL(r1), "\n") r1 <- unclass(ns(log(xs), knots = log(iks), Boundary.knots = log(bks), intercept = TRUE)) r2 <- get_ns_spline(knots = log(sort(c(iks, bks))), intercept = TRUE, do_log = TRUE)(xs) cat("Rank is correct (log):", rankMatrix(cbind(r1, r2)) == NCOL(r1), "\n") } # the latter is faster system.time( replicate(100, ns(xs, knots = iks, Boundary.knots = bks, intercept = TRUE))) system.time( replicate(100, get_ns_spline(knots = sort(c(iks, bks)), intercept = TRUE, do_log = FALSE)(xs))) func <- get_ns_spline(knots = sort(c(iks, bks)), intercept = TRUE, do_log = FALSE) system.time(replicate(100, func(xs)))

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